DAR-MinSAAD Database in MySQL Server

Mindanao Sustainable Agrarian & Agriculture Development (MinSAAD)

MinSAAD Project is a poverty alleviation project in selected marginalized areas (12 settlements) in Mindanao. The Project addresses poverty issues through competitive and sustainable agriculture using inclusive approach and modalities in investments that are based on value chain analysis of key agricultural commodities. The Project is therefore focused on sustainable agricultural development and support services such as irrigation, rural roads and bridges, rural water supply, marketing and related aspects; effective governance and social protection; and others that would support the objective of alleviating poverty in marginalized target area.

Installing Packages

After installing the packages listed in the library below, proceed to create a database connection in the MySQL Server.

library(RODBC)
library(DBI)
library(odbc)

To enable data visualization, install the ggplot, dplyr, stringr and ggh4x package.

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(stringr)
library(ggh4x)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

To establish a connection with the MySQL Server, you need to create a database connection via Data Source Name (DSN).

con <- dbConnect(odbc::odbc(), dsn = "minsaad_mysql")

Retrieve a list of Component records along with the corresponding number of Sub-Components.

Utilize the following SQL statement to access the Components table within the database. To retrieve table(s) from the database and assign the data to a variable in R, you can use the following SQL statement:

vComponents <- dbGetQuery(con, 'SELECT
                                    components.ComponentName AS Components, 
                                    components.ComponentCode AS `Code`, 
                                    Count(subcomponents.SubComponentID_PK) AS SubComponents
                                FROM
                                    components
                                    INNER JOIN
                                    subcomponents
                                    ON 
                                        components.ComponentID_PK = subcomponents.ComponentID_FK
                                GROUP BY
                                    components.ComponentName, 
                                    components.ComponentCode
                                ORDER BY
                                    components.ComponentCode ASC;')

To view the Components table within the database, you can follow the command below:

# Make dataframe
results_df <- data.frame(vComponents, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Component", "Code", "No. of Sub-Components", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Components table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Components table

Component	Code	No. of Sub-Components
Agriculture, Agribusiness, and Agro-forestry Development	AAAD	4
Support Infrastructure	INFRA	5
Institutional Development	INSTI	5
Monitoring and Evaluation	M&E	2
Project Management	PM	2
Total:		18

To display a chart illustrating the records of Components along with the corresponding number of Sub-Components using the ggplot command in R.

ggplot(vComponents, aes(x=Code, as.integer(SubComponents), y=as.integer(SubComponents), fill=Code)) +
  geom_col(show.legend = TRUE) +
  geom_text(aes(label = as.integer(SubComponents), y = as.integer(SubComponents) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="Components")) +
  xlab("Components") + ylab("Number of Sub-Components") +
  labs(title= "Major Components with Number of Sub-Components",  
       caption="Data Collected by: Junald A. Lagod")

To retrieve a list of Sub-Component records along with the Components table.

To access the Sub-Components table within the database and retrieve its data while assigning it to a variable in R, you can follow the example below:

vSubComponents_POs <- dbGetQuery(con, 'SELECT
                                          components.ComponentCode AS Component, 
                                          subcomponents.SubComponentName AS `Sub-Component`, 
                                          subcomponents.SubComponentCode AS `Code`, 
                                          COUNT(pogroup.POID_PK) AS POs,
                                          SUM(pogroup.ARBMale ) AS ARBMale,
                                          SUM(pogroup.ARBFemale ) AS ARBFemale,
                                          SUM(pogroup.NonARBMale ) AS NonARBMale,
                                          SUM(pogroup.NonARBFemale ) AS NonARBFemale,
                                          SUM(pogroup.IPMale ) AS IPMale,
                                          SUM(pogroup.IPFemale ) AS IPFemale,
                                          SUM(pogroup.NonIPMale ) AS NonIPMale,
                                          SUM(pogroup.NonIPFemale ) AS NonIPFemale
      
                                        FROM
                                          components
                                          LEFT JOIN
                                          subcomponents
                                          ON 
                                              components.ComponentID_PK = subcomponents.ComponentID_FK
                                          LEFT JOIN
                                          pogroup
                                          ON 
                                              pogroup.POTypeID_FK = subcomponents.SubComponentID_PK
                                        GROUP BY
                                          components.ComponentCode, 
                                          subcomponents.SubComponentName, 
                                          subcomponents.SubComponentCode
                                        ORDER BY
                                          components.ComponentCode ASC, 
                                          subcomponents.SubComponentName ASC;')

vSubComponents_SPs <- dbGetQuery(con, 'SELECT
                                            components.ComponentCode AS Component, 
                                            subcomponents.SubComponentName AS `Sub-Component`, 
                                            subcomponents.SubComponentCode AS `Code`, 
                                            COUNT(subprojects.SPID_PK) AS SPs,
                                            SUM(subprojects.Ben) AS Beneficiaries,
                                            SUM(subprojects.Qty) AS Qty,
                                            (CASE 
                                                    WHEN subcomponents.SubComponentCode = "AGBiz" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "AGRI" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "AGRO" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "CI" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "BRDG" THEN "Lms"
                                                    WHEN subcomponents.SubComponentCode = "FMR" THEN "Kms"
                                                    WHEN subcomponents.SubComponentCode = "IRRIG" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "PHF" THEN "Units"
                                                    WHEN subcomponents.SubComponentCode = "RWS" THEN "HHs"
                                            END) AS UnitMeasure
                                        FROM
                                            components
                                            LEFT JOIN
                                            subcomponents
                                            ON 
                                                components.ComponentID_PK = subcomponents.ComponentID_FK
                                            LEFT JOIN
                                            subprojects
                                            ON 
                                                subcomponents.SubComponentID_PK = subprojects.SPTypeID_FK
                                        GROUP BY
                                            components.ComponentCode, 
                                            subcomponents.SubComponentName, 
                                            subcomponents.SubComponentCode
                                        ORDER BY
                                            components.ComponentCode ASC, 
                                            subcomponents.SubComponentCode ASC;')

To view the Sub-Components table within the database, you can follow the command below:

# Make dataframe
vSubComponents_POs_tmp <- vSubComponents_POs %>%
  select(-ARBMale, -ARBFemale, -NonARBMale, -NonARBFemale, -IPMale, -IPFemale, -NonIPMale, -NonIPFemale)

vSubComponents_SPs_tmp <- vSubComponents_SPs %>%
  select(-Beneficiaries, -Qty, -UnitMeasure)

vSubComponents <- merge(vSubComponents_POs_tmp, vSubComponents_SPs_tmp)
vSubComponents_tmp <- vSubComponents

results_df <- data.frame(vSubComponents_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Component", "Sub-Components", "Code", "No. of POs", "No. of SPs", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Sub-Components table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sub-Components table

Component	Sub-Components	Code	No. of POs	No. of SPs
AAAD	Agribusiness	AGBiz	0	65
AAAD	Agriculture	AGRI	0	0
AAAD	Agro-forestry	AGRO	0	35
AAAD	Crop Intensification	CI	0	22
INFRA	Bridge	BRDG	0	15
INFRA	Farm to Market Road	FMR	0	40
INFRA	Irrigation	IRRIG	0	19
INFRA	Post Harvest Facilities	PHF	0	65
INFRA	Rural Water Systems	RWS	0	26
INSTI	Cooperative	COOP	68	0
INSTI	Farmers Association	FA	206	0
INSTI	Irrigators Association	IA	21	0
INSTI	Water Users Association	WUA	31	0
INSTI	Womens Organization	WO	23	0
M&E	Management Information System	MIS	0	0
M&E	Monitoring and Evaluation	M&E	0	0
PM	Administration	ADMIN	0	0
PM	Finance	FINCE	0	0
Total:			349	287

To retrieve a list of Sub-Component records for AAAD & INFRA along with the Components table.

# Make dataframe
vSubComponents_SPs <- vSubComponents_SPs %>%
  filter(vSubComponents_SPs$Component == "AAAD" | vSubComponents_SPs$Component == "INFRA") %>%
  #drop_na() %>%
  select(-Component)

results_df <- data.frame(vSubComponents_SPs, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Sub-Components", "Code", "SPs", 
                       "Beneficiaries", "Qty", "Unit Measure", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Sub-Components table for AAAD & INFRA Components", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sub-Components table for AAAD & INFRA Components

Sub-Components	Code	SPs	Beneficiaries	Qty	Unit Measure
Agribusiness	AGBiz	65	12000	11619.973	Has
Agriculture	AGRI	0	NA	NA	Has
Agro-forestry	AGRO	35	1311	7177.776	Has
Crop Intensification	CI	22	200	5226.000	Has
Bridge	BRDG	15	0	777.000	Lms
Farm to Market Road	FMR	40	0	240.212	Kms
Irrigation	IRRIG	19	0	1930.330	Has
Post Harvest Facilities	PHF	65	0	88.000	Units
Rural Water Systems	RWS	26	0	7077.000	HHs
Total:		287	NA	NA

To display a chart illustrating the records of SP Types along with the corresponding number of Sub-Projects(SPs) using the ggplot command in R.

ggplot(vSubComponents_SPs, aes(x=Code, as.integer(SPs), y=as.integer(SPs), fill=Code)) +
  geom_col(show.legend = TRUE) +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 100, by = 10), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="SP Types")) +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  xlab("SP Types") + ylab("Number of Sub-Projects(SPs)") +
  labs(title= "SP Types with Number of Sub-Projects(SPs)",  
       caption="Data Collected by: Junald A. Lagod") 

To retrieve a list of Sub-Component records for INSTI along with the Components table.

vSubComponents_POs <- vSubComponents_POs %>%
  filter(vSubComponents_POs$Component == "INSTI") %>%
  select(-Component)

# Make dataframe
results_df <- data.frame(vSubComponents_POs, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Sub-Components", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Sub-Components table for INSTITUTIONAL(INSTI) DEVELOPMENT", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sub-Components table for INSTITUTIONAL(INSTI) DEVELOPMENT

Sub-Components	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	68	2756	2882	1081	1842	88	76	1669	2476
Farmers Association	FA	206	5315	3077	983	1242	1451	673	2224	1755
Irrigators Association	IA	21	544	168	72	20	103	35	144	61
Water Users Association	WUA	31	1504	1072	440	210	761	520	592	301
Womens Organization	WO	23	0	871	0	162	0	63	0	461
Total:		349	10119	8070	2576	3476	2403	1367	4629	5054

To display a chart illustrating the records of PO Types along with the corresponding number of Peoples Organizations(POs) using the ggplot command in R.

ggplot(vSubComponents_POs, aes(x=Code, as.integer(POs), y=as.integer(POs), fill=Code)) +
  geom_col(show.legend = TRUE) +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 250, by = 20), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="PO Types")) +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A",
                               "#BACCC1", "#3C8D53", "#FFF123")) +
  xlab("PO Types") + ylab("Number of Peoples Organizations(POs)") +
  labs(title= "PO Types with Number of Peoples Organizations(POs)",  
       caption="Data Collected by: Junald A. Lagod") 

To retrieve a list of PO Types with Status records along with the Components table.

To access the PO Types with Status table within the database and retrieve its data while assigning it to a variable in R, you can follow the example below:

vPOTypeStatus_OCR <- dbGetQuery(con, 'SELECT
                                        projstatus.Component, 
                                        projentities.EntityName AS "MajorStatus", 
                                        "Organizational Capacity" AS Rating, 
                                        "OCR" AS RatingCode, 
                                        subcomponents.SubComponentName AS "POType", 
                                        subcomponents.SubComponentCode AS "Code", 
                                        projstatus.ProjStatusID_PK AS StatusID, 
                                        projstatus.ProjStatusName AS "Status", 
                                        COUNT(pogroup.POID_PK) AS POs
                                      FROM
                                        components
                                        LEFT JOIN
                                        subcomponents
                                        ON 
                                            components.ComponentID_PK = subcomponents.ComponentID_FK
                                        LEFT JOIN
                                        pogroup
                                        ON 
                                            pogroup.POTypeID_FK = subcomponents.SubComponentID_PK
                                        LEFT JOIN
                                        projstatus
                                        ON 
                                            pogroup.OCRStatusID_FK = projstatus.ProjStatusID_PK
                                        INNER JOIN
                                        projentities
                                        ON 
                                            projstatus.MajorStatusID_FK = projentities.EntityID_PK
                                      WHERE
                                        components.ComponentCode = "INSTI"
                                      GROUP BY
                                        projstatus.Component, 
                                        projstatus.ProjStatusID_PK, 
                                        components.ComponentCode, 
                                        subcomponents.SubComponentName, 
                                        subcomponents.SubComponentCode, 
                                        projstatus.ProjStatusName
                                      ORDER BY
                                        components.ComponentCode ASC, 
                                        subcomponents.SubComponentName ASC;')

vPOTypeStatus_ERR <- dbGetQuery(con, 'SELECT
                                        projstatus.Component, 
                                        projentities.EntityName AS "MajorStatus", 
                                        "Enterprise Readiness" AS Rating, 
                                        "ERR" AS RatingCode, 
                                        subcomponents.SubComponentName AS "POType", 
                                        subcomponents.SubComponentCode AS "Code", 
                                        projstatus.ProjStatusID_PK AS StatusID, 
                                        projstatus.ProjStatusName AS "Status", 
                                        COUNT(pogroup.POID_PK) AS POs
                                      FROM
                                        components
                                        LEFT JOIN
                                        subcomponents
                                        ON 
                                            components.ComponentID_PK = subcomponents.ComponentID_FK
                                        LEFT JOIN
                                        pogroup
                                        ON 
                                            pogroup.POTypeID_FK = subcomponents.SubComponentID_PK
                                        LEFT JOIN
                                        projstatus
                                        ON 
                                            pogroup.ERRStatusID_FK = projstatus.ProjStatusID_PK
                                        INNER JOIN
                                        projentities
                                        ON 
                                            projstatus.MajorStatusID_FK = projentities.EntityID_PK
                                      WHERE
                                        components.ComponentCode = "INSTI"
                                      GROUP BY
                                        projstatus.Component, 
                                        projstatus.ProjStatusID_PK, 
                                        components.ComponentCode, 
                                        subcomponents.SubComponentName, 
                                        subcomponents.SubComponentCode, 
                                        projstatus.ProjStatusName
                                      ORDER BY
                                        components.ComponentCode ASC, 
                                        subcomponents.SubComponentName ASC;')



vPOTypeStatus_MLR <- dbGetQuery(con, 'SELECT
                                        projstatus.Component, 
                                        projentities.EntityName AS "MajorStatus", 
                                        "Maturity Level" AS Rating,  
                                        "MLR" AS RatingCode, 
                                        subcomponents.SubComponentName AS "POType", 
                                        subcomponents.SubComponentCode AS "Code", 
                                        projstatus.ProjStatusID_PK AS StatusID, 
                                        projstatus.ProjStatusName AS "Status", 
                                        COUNT(pogroup.POID_PK) AS POs
                                      FROM
                                        components
                                        LEFT JOIN
                                        subcomponents
                                        ON 
                                            components.ComponentID_PK = subcomponents.ComponentID_FK
                                        LEFT JOIN
                                        pogroup
                                        ON 
                                            pogroup.POTypeID_FK = subcomponents.SubComponentID_PK
                                        LEFT JOIN
                                        projstatus
                                        ON 
                                            pogroup.MaturityLevelID_FK = projstatus.ProjStatusID_PK
                                        INNER JOIN
                                        projentities
                                        ON 
                                            projstatus.MajorStatusID_FK = projentities.EntityID_PK
                                      WHERE
                                        components.ComponentCode = "INSTI"
                                      GROUP BY
                                        projstatus.Component, 
                                        projstatus.ProjStatusID_PK, 
                                        components.ComponentCode, 
                                        subcomponents.SubComponentName, 
                                        subcomponents.SubComponentCode, 
                                        projstatus.ProjStatusName
                                      ORDER BY
                                        components.ComponentCode ASC, 
                                        subcomponents.SubComponentName ASC;')

To display a chart illustrating the records of Cooperative PO Types along with the corresponding number of POs using the ggplot command in R.

vPOTypeStatus <- union(vPOTypeStatus_OCR, vPOTypeStatus_ERR)
vPOTypeStatus <- union(vPOTypeStatus, vPOTypeStatus_MLR)

vPOTypeStatus_COOP <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Code == "COOP")%>%
  group_by(POType, Code, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vPOTypeStatus_COOP) +
  geom_col(aes(x=as.integer(POs), y=Status, 
               fill = factor(Status)), width = 0.6)  + 
  geom_text(data = subset(vPOTypeStatus_COOP, as.integer(POs) >= 1),
            aes(0, y = Status, label = as.integer(POs)), hjust = 0, size = 3) +
  scale_x_continuous(
    breaks = seq(0, 150, by = 10), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
    position = "bottom"  # Labels are located on the top
  )  +
  facet_wrap(~ paste(Rating, "Ratings"), scales = "free_x", drop = TRUE) +
  theme(legend.position = "bottom") +
  guides(fill=guide_legend(title="Ratings")) +
  xlab("Number of PO Group") + ylab("Status Ratings") + 
  labs(title= "Cooperative PO Type Status with Number of POs", 
       caption="Data Collected by: Junald A. Lagod")  

To display a chart illustrating the records of Farmers Associations (FAs) PO Types along with the corresponding number of POs using the ggplot command in R.

vPOTypeStatus_FA <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Code == "FA")%>%
  group_by(POType, Code, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vPOTypeStatus_FA) +
  geom_col(aes(x=as.integer(POs), y=Status, 
               fill = factor(Status)), width = 0.6)  + 
  geom_text(data = subset(vPOTypeStatus_FA, as.integer(POs) >= 1),
            aes(0, y = Status, label = as.integer(POs)), hjust = 0, size = 3) +
  scale_x_continuous(
    breaks = seq(0, 150, by = 20), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
    position = "bottom"  # Labels are located on the top
  )  +
  facet_wrap(~ paste(Rating, "Ratings"), scales = "free_x", drop = TRUE) +
  theme(legend.position = "bottom") +
  guides(fill=guide_legend(title="")) +
  xlab("Number of PO Group") + ylab("Status Ratings") + 
  labs(title= "Farmers Associations (FAs) PO Type Status with Number of POs", 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Irrigators Associations (IAs) PO Types along with the corresponding number of POs using the ggplot command in R.

vPOTypeStatus_IA <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Code == "IA")%>%
  group_by(POType, Code, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vPOTypeStatus_IA) +
  geom_col(aes(x=as.integer(POs), y=Status, 
               fill = factor(Status)), width = 0.6)  + 
  geom_text(data = subset(vPOTypeStatus_IA, as.integer(POs) >= 1),
            aes(0, y = Status, label = as.integer(POs)), hjust = 0, size = 3) +
  scale_x_continuous(
    breaks = seq(0, 20, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
    position = "bottom"  # Labels are located on the top
  )  +
  facet_wrap(~ paste(Rating, "Ratings"), scales = "free_x", drop = TRUE) +
  theme(legend.position = "bottom") +
  guides(fill=guide_legend(title="")) +
  xlab("Number of PO Group") + ylab("Status Ratings") + 
  labs(title= "Irrigators Associations (IAs) PO Type Status with Number of POs", 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Water Users Associations (WUAs) PO Types along with the corresponding number of POs using the ggplot command in R.

vPOTypeStatus_WUA <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Code == "WUA")%>%
  group_by(POType, Code, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vPOTypeStatus_WUA) +
  geom_col(aes(x=as.integer(POs), y=Status, 
               fill = factor(Status)), width = 0.6)  + 
  geom_text(data = subset(vPOTypeStatus_WUA, as.integer(POs) >= 1),
            aes(0, y = Status, label = as.integer(POs)), hjust = 0, size = 3) +
  scale_x_continuous(
    breaks = seq(0, 20, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
    position = "bottom"  # Labels are located on the top
  )  +
  facet_wrap(~ paste(Rating, "Ratings"), scales = "free_x", drop = TRUE) +
  theme(legend.position = "bottom") +
  guides(fill=guide_legend(title="")) +
  xlab("Number of PO Group") + ylab("Status Ratings") + 
  labs(title= "Water Users Associations (WUAs) PO Type Status with Number of POs", 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Womens Organizations (WOs) PO Types along with the corresponding number of POs using the ggplot command in R.

vPOTypeStatus_WO <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Code == "WO")%>%
  group_by(POType, Code, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vPOTypeStatus_WO) +
  geom_col(aes(x=as.integer(POs), y=Status, 
               fill = factor(Status)), width = 0.6)  + 
  geom_text(data = subset(vPOTypeStatus_WO, as.integer(POs) >= 1),
            aes(0, y = Status, label = as.integer(POs)), hjust = 0, size = 3) +
  scale_x_continuous(
    breaks = seq(0, 20, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
    position = "bottom"  # Labels are located on the top
  )  +
  facet_wrap(~ paste(Rating, "Ratings"), scales = "free_x", drop = TRUE) +
  theme(legend.position = "bottom") +
  guides(fill=guide_legend(title="")) +
  xlab("Number of PO Group") + ylab("Status Ratings") + 
  labs(title= "Womens Organizations (WOs) PO Type Status with Number of POs", 
       caption="Data Collected by: Junald A. Lagod")

To retrieve a list of covered Regions records along with the corresponding number of Provinces.

This SQL query uses a JOIN operation to combine the Regions table with the Provinces table based on a shared RegionID_FK. It then calculates the count of ProvinceID_PK for each region using the COUNT function and aliases it as ‘Number Of Provinces’. The GROUP BY clause ensures that the count is calculated per region. By executing this query, you will retrieve a result set that includes the Regions records along with the corresponding number of Provinces for each region, you can use the following SQL statement:

vLocations_ARCs <- dbGetQuery(con, 'SELECT
                                          projarea_regions.RegionName AS Region, 
                                          projarea_regions.RegionCode AS RegCode, 
                                          projarea_provinces.ProvinceName AS Province, 
                                          projarea_provinces.ProvinceCode AS ProvCode, 
                                          projarea_settlements.SettlementAreaName AS Settlement, 
                                          projarea_settlements.SettlementAreaCode AS SACode, 
                                          projarea_municipalities.MunicipalityName AS Municipality, 
                                          projarea_municipalities.MunicipalityCode AS MunCode, 
                                          projarea_arcs.ARCName AS ARC, 
                                          Count(projarea_arcs.ARCID_PK) AS ARCs
                                      FROM
                                          (
                                              projarea_regions
                                              INNER JOIN
                                              (
                                                  (
                                                      projarea_provinces
                                                      INNER JOIN
                                                      projarea_settlements
                                                      ON 
                                                          projarea_provinces.ProvinceID_PK 
                                                          = projarea_settlements.ProvinceID_FK
                                                  )
                                                  LEFT JOIN
                                                  projarea_municipalities
                                                  ON 
                                                      projarea_settlements.SettlementID_PK 
                                                      = projarea_municipalities.SettlementID_FK
                                              )
                                              ON 
                                                  projarea_regions.RegionID_PK 
                                                  = projarea_provinces.RegionID_FK
                                          )
                                          LEFT JOIN
                                          projarea_arcs
                                          ON 
                                              projarea_municipalities.MunicipalityID_PK 
                                              = projarea_arcs.MunicipalityID_FK
                                      GROUP BY
                                          projarea_regions.RegionCode, 
                                          projarea_regions.RegionName, 
                                          projarea_provinces.ProvinceName, 
                                          projarea_provinces.ProvinceCode, 
                                          projarea_settlements.SettlementAreaCode, 
                                          projarea_settlements.SettlementAreaName, 
                                          projarea_municipalities.MunicipalityName, 
                                          projarea_municipalities.MunicipalityCode,
                                          projarea_arcs.ARCName
                                      ORDER BY
                                          projarea_regions.RegionCode ASC, 
                                          projarea_provinces.ProvinceCode ASC, 
                                          projarea_settlements.SettlementAreaCode ASC, 
                                          projarea_municipalities.MunicipalityName ASC;')

vLocations_BRGYS <- dbGetQuery(con, 'SELECT
                                          projarea_regions.RegionName AS Region, 
                                          projarea_regions.RegionCode AS RegCode, 
                                          projarea_provinces.ProvinceName AS Province, 
                                          projarea_provinces.ProvinceCode AS ProvCode, 
                                          projarea_settlements.SettlementAreaName AS Settlement, 
                                          projarea_settlements.SettlementAreaCode AS SACode, 
                                          projarea_municipalities.MunicipalityName AS Municipality, 
                                          projarea_municipalities.MunicipalityCode AS MunCode,
                                          projarea_barangays.BarangayName AS Barangay,
                                          Count(projarea_barangays.BarangayID_PK) AS Barangays
                                      FROM
                                          (
                                              projarea_regions
                                              INNER JOIN
                                              (
                                                  (
                                                      projarea_provinces
                                                      INNER JOIN
                                                      projarea_settlements
                                                      ON 
                                                          projarea_provinces.ProvinceID_PK 
                                                          = projarea_settlements.ProvinceID_FK
                                                  )
                                                  LEFT JOIN
                                                  projarea_municipalities
                                                  ON 
                                                      projarea_settlements.SettlementID_PK 
                                                      = projarea_municipalities.SettlementID_FK
                                              )
                                              ON 
                                                  projarea_regions.RegionID_PK 
                                                  = projarea_provinces.RegionID_FK
                                          )
                                          LEFT JOIN
                                          projarea_barangays
                                          ON 
                                              projarea_municipalities.MunicipalityID_PK 
                                              = projarea_barangays.MunicipalityID_FK
                                      GROUP BY
                                          projarea_regions.RegionCode, 
                                          projarea_regions.RegionName, 
                                          projarea_provinces.ProvinceName, 
                                          projarea_provinces.ProvinceCode, 
                                          projarea_settlements.SettlementAreaCode, 
                                          projarea_settlements.SettlementAreaName, 
                                          projarea_municipalities.MunicipalityName, 
                                          projarea_municipalities.MunicipalityCode,
                                          projarea_barangays.BarangayName
                                      ORDER BY
                                          projarea_regions.RegionCode ASC, 
                                          projarea_provinces.ProvinceCode ASC, 
                                          projarea_settlements.SettlementAreaCode ASC, 
                                          projarea_municipalities.MunicipalityName ASC;')

vLocations_ARCs_tmp <- vLocations_ARCs %>%
  select(-ARC) 
vLocations_BRGYS_tmp <- vLocations_BRGYS %>%
  select(-Barangay)

To view the Regions table within the database, you can follow the command below:

vLocREGIONS_tmp <- vLocations_BRGYS_tmp %>%
  group_by(Region, RegCode)  %>%
  count(Province) %>%
  summarise(Provinces = n(), .groups = 'drop')

# Make dataframe
results_df <- data.frame(vLocREGIONS_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Region", "Code", "No. of Provinces", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Covered Regions table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Covered Regions table

Region	Code	No. of Provinces
Region X	R10	2
Region XI	R11	2
Region XII	R12	3
Total:		7

To display a chart illustrating the records of Regions along with the corresponding number of Provinces using the ggplot command in R.

ggplot(vLocREGIONS_tmp, aes(x="", y= as.integer(Provinces), fill=Region)) +
  geom_col(show.legend = TRUE) +
  geom_text(aes(label = as.integer(Provinces)), 
    position = position_stack(vjust = 0.5), show.legend = FALSE) +
    coord_polar(theta = "y") +
    scale_fill_manual(values = c("#EC754A", "#FFF123", "#ABC123")) +
    theme(legend.position = "right") +
    guides(fill=guide_legend(title="Regions"))  +
    xlab("Number of Provinces") + ylab("Number of Provinces")  +
    labs(title= "Covered Regions with Number of Provinces",  
         caption="Data Collected by: Junald A. Lagod")

To retrieve a list of covered Regions and PO Types records along with the corresponding number of Provinces.

vLocations_POTypes <- dbGetQuery(con, 'SELECT
                                          projarea_regions.RegionName AS "Region", 
                                          projarea_provinces.ProvinceName AS "Province", 
                                          projarea_settlements.SettlementAreaName AS "SettlementArea", 
                                          projarea_municipalities.MunicipalityName AS "Municipality", 
                                          projarea_barangays.BarangayName AS "Barangay", 
                                          subcomponents.SubComponentName AS "POType",
                                          subcomponents.SubComponentCode AS "Code", 
                                          COUNT(pogroup.POID_PK) AS POs,
                                          SUM(pogroup.ARBMale ) AS ARBMale,
                                          SUM(pogroup.ARBFemale ) AS ARBFemale,
                                          SUM(pogroup.NonARBMale ) AS NonARBMale,
                                          SUM(pogroup.NonARBFemale ) AS NonARBFemale,
                                          SUM(pogroup.IPMale ) AS IPMale,
                                          SUM(pogroup.IPFemale ) AS IPFemale,
                                          SUM(pogroup.NonIPMale ) AS NonIPMale,
                                          SUM(pogroup.NonIPFemale ) AS NonIPFemale
                                      FROM
                                          projarea_regions
                                          JOIN
                                          projarea_provinces
                                          ON 
                                              projarea_regions.RegionID_PK 
                                              = projarea_provinces.RegionID_FK
                                          INNER JOIN
                                          projarea_settlements
                                          ON 
                                              projarea_provinces.ProvinceID_PK 
                                              = projarea_settlements.ProvinceID_FK
                                          INNER JOIN
                                          projarea_municipalities
                                          ON 
                                              projarea_settlements.SettlementID_PK 
                                              = projarea_municipalities.SettlementID_FK
                                          INNER JOIN
                                          projarea_barangays
                                          ON 
                                              projarea_municipalities.MunicipalityID_PK 
                                              = projarea_barangays.MunicipalityID_FK
                                          INNER JOIN
                                          pogroup
                                          ON 
                                              projarea_barangays.BarangayID_PK = pogroup.BarangayID_FK
                                          INNER JOIN
                                          subcomponents
                                          ON 
                                              pogroup.POTypeID_FK = subcomponents.SubComponentID_PK
                                      GROUP BY
                                          projarea_regions.RegionName, 
                                          projarea_provinces.ProvinceName, 
                                          projarea_settlements.SettlementAreaName, 
                                          projarea_municipalities.MunicipalityName, 
                                          projarea_barangays.BarangayName, 
                                          subcomponents.SubComponentName,
                                          subcomponents.SubComponentCode
                                      ORDER BY
                                          projarea_regions.RegionName ASC, 
                                          projarea_provinces.ProvinceName ASC, 
                                          projarea_settlements.SettlementAreaName ASC, 
                                          projarea_municipalities.MunicipalityName ASC, 
                                          projarea_barangays.BarangayName ASC, 
                                          subcomponents.SubComponentName ASC,
                                          subcomponents.SubComponentCode ASC;')

vLocations_SPTypes <- dbGetQuery(con, 'SELECT
                                            projarea_regions.RegionName AS Region, 
                                            projarea_provinces.ProvinceName AS Province, 
                                            projarea_settlements.SettlementAreaName AS SettlementArea, 
                                            projarea_municipalities.MunicipalityName AS Municipality, 
                                            projarea_barangays.BarangayName AS Barangay, 
                                            subcomponents.SubComponentName AS SPType, 
                                            subcomponents.SubComponentCode AS `Code`,
                                            COUNT(subprojects.SPID_PK) AS SPs, 
                                            SUM(subprojects.Ben) AS Beneficiaries, 
                                            SUM(subprojects.Qty) AS Qty,
                                            (CASE 
                                                    WHEN subcomponents.SubComponentCode = "AGBiz" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "AGRI" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "AGRO" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "CI" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "BRDG" THEN "Lms"
                                                    WHEN subcomponents.SubComponentCode = "FMR" THEN "Kms"
                                                    WHEN subcomponents.SubComponentCode = "IRRIG" THEN "Has"
                                                    WHEN subcomponents.SubComponentCode = "PHF" THEN "Units"
                                                    WHEN subcomponents.SubComponentCode = "RWS" THEN "HHs"
                                            END) AS UnitMeasure
                                        FROM
                                            projarea_regions
                                            JOIN
                                            projarea_provinces
                                            ON 
                                                projarea_regions.RegionID_PK = projarea_provinces.RegionID_FK
                                            INNER JOIN
                                            projarea_settlements
                                            ON 
                                                projarea_provinces.ProvinceID_PK = projarea_settlements.ProvinceID_FK
                                            INNER JOIN
                                            projarea_municipalities
                                            ON 
                                                projarea_settlements.SettlementID_PK = projarea_municipalities.SettlementID_FK
                                            INNER JOIN
                                            projarea_barangays
                                            ON 
                                                projarea_municipalities.MunicipalityID_PK = projarea_barangays.MunicipalityID_FK
                                            INNER JOIN
                                            subcomponents
                                            INNER JOIN
                                            subprojects
                                            ON 
                                                projarea_barangays.BarangayID_PK = subprojects.BarangayID_FK AND
                                                subcomponents.SubComponentID_PK = subprojects.SPTypeID_FK
                                        GROUP BY
                                            projarea_regions.RegionName, 
                                            projarea_provinces.ProvinceName, 
                                            projarea_settlements.SettlementAreaName, 
                                            projarea_municipalities.MunicipalityName, 
                                            projarea_barangays.BarangayName, 
                                            subcomponents.SubComponentName, 
                                            subcomponents.SubComponentCode
                                        ORDER BY
                                            projarea_regions.RegionName ASC, 
                                            projarea_provinces.ProvinceName ASC, 
                                            projarea_settlements.SettlementAreaName ASC, 
                                            projarea_municipalities.MunicipalityName ASC, 
                                            projarea_barangays.BarangayName ASC, 
                                            subcomponents.SubComponentName ASC, 
                                            subcomponents.SubComponentCode ASC;')

To view the Regions with SP Types table within the database, you can follow the command below:

#Region X
# Make dataframe
vRegion <- "Region X"

vRegionX_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Province, -SettlementArea, -Municipality, -Barangay)

vRegionX_SPTypes_tmp <- vRegionX_SPTypes_tmp %>%
  filter(vRegionX_SPTypes_tmp$Region == vRegion)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegionX_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Region X with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	24	3462	3352.36
Agro-forestry	AGRO	Has	16	745	4076.71
Bridge	BRDG	Lms	8	0	581.00
Crop Intensification	CI	Has	2	NA	300.00
Farm to Market Road	FMR	Kms	11	0	65.93
Irrigation	IRRIG	Has	7	0	575.00
Post Harvest Facilities	PHF	Units	22	NA	31.00
Rural Water Systems	RWS	HHs	8	0	1910.00
Total:			98	NA	10892.00

#Region XI
# Make dataframe
vRegion <- "Region XI"

vRegionXI_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Province, -SettlementArea, -Municipality, -Barangay) 

vRegionXI_SPTypes_tmp <- vRegionXI_SPTypes_tmp %>%
  filter(vRegionXI_SPTypes_tmp$Region == vRegion)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegionXI_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Region XI with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	12	1931	1867.05
Agro-forestry	AGRO	Has	2	74	408.90
Bridge	BRDG	Lms	2	0	55.00
Crop Intensification	CI	Has	4	NA	949.00
Farm to Market Road	FMR	Kms	6	0	34.66
Irrigation	IRRIG	Has	1	0	106.00
Post Harvest Facilities	PHF	Units	8	NA	12.00
Rural Water Systems	RWS	HHs	7	0	1886.00
Total:			42	NA	5318.61

#Region XII
# Make dataframe
vRegion <- "Region XII"

vRegionXII_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Province, -SettlementArea, -Municipality, -Barangay) 

vRegionXII_SPTypes_tmp <- vRegionXII_SPTypes_tmp %>%
  filter(vRegionXII_SPTypes_tmp$Region == vRegion)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegionXII_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	29	6607	6400.562
Agro-forestry	AGRO	Has	17	492	2692.166
Bridge	BRDG	Lms	5	0	141.000
Crop Intensification	CI	Has	16	NA	3977.000
Farm to Market Road	FMR	Kms	23	NA	139.622
Irrigation	IRRIG	Has	11	0	1249.330
Post Harvest Facilities	PHF	Units	35	NA	45.000
Rural Water Systems	RWS	HHs	11	NA	3281.000
Total:			147	NA	17925.681

To display a chart illustrating the records of Regions along with the corresponding number of SPs using the ggplot command in R.

vRegions_SPTypes_tmp <- vLocations_SPTypes %>%
  group_by(Region, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vRegions_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +
  scale_y_continuous(
    breaks = seq(0, 50, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ Region, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vRegions_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= "Covered Regions and SP Types with No. of Sub-Projects (SPs)",  
       caption="Data Collected by: Junald A. Lagod")

To view the Regions with PO Types table within the database, you can follow the command below:

#Region X
# Make dataframe
vRegionX_POTypes_tmp <- vLocations_POTypes %>%
  select(-Province, -SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegionX_POTypes_tmp <- vRegionX_POTypes_tmp %>%
  filter(vRegionX_POTypes_tmp$Region == "Region X")  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), 
            ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegionX_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", 
                       "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", 
                       "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Region X with PO Types table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Region X with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	38	839	773	106	364	15	22	5	3
Farmers Association	FA	19	438	181	87	46	0	0	0	0
Irrigators Association	IA	5	88	30	72	20	0	0	0	0
Water Users Association	WUA	10	505	243	5	0	0	0	0	0
Womens Organization	WO	3	0	41	0	30	0	0	0	0
Total:		75	1870	1268	270	460	15	22	5	3

#Region XI
# Make dataframe
vRegionXI_POTypes_tmp <- vLocations_POTypes %>%
  select(-Province, -SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegionXI_POTypes_tmp <- vRegionXI_POTypes_tmp %>%
  filter(vRegionXI_POTypes_tmp$Region == "Region XI")  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), 
            ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegionXI_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Region XI with PO Types table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Region XI with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	7	119	157	115	129	61	54	1	0
Farmers Association	FA	29	1655	595	507	318	1052	472	80	16
Irrigators Association	IA	2	103	35	0	0	103	35	0	0
Water Users Association	WUA	7	539	544	306	132	663	478	218	89
Total:		45	2416	1331	928	579	1879	1039	299	105

#Region XII
# Make dataframe
vRegionXII_POTypes_tmp <- vLocations_POTypes %>%
  select(-Province, -SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegionXII_POTypes_tmp <- vRegionXII_POTypes_tmp %>%
  filter(vRegionXII_POTypes_tmp$Region == "Region XII")  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), 
            ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegionXII_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Region XII with PO Types table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Region XII with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	23	1798	1952	860	1349	12	0	1663	2473
Farmers Association	FA	158	3222	2301	389	878	399	201	2144	1739
Irrigators Association	IA	14	353	103	0	0	0	0	144	61
Water Users Association	WUA	14	460	285	129	78	98	42	374	212
Womens Organization	WO	20	0	830	0	132	0	63	0	461
Total:		229	5833	5471	1378	2437	509	306	4325	4946

To display a chart illustrating the records of Regions along with the corresponding number of POs using the ggplot command in R.

vRegions_POTypes_tmp <- vLocations_POTypes %>%
  group_by(Region, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vRegions_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 200, by = 20), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ Region, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vRegions_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= "Covered Regions and PO Types with Number of PO Group",  
       caption="Data Collected by: Junald A. Lagod")

To view the Provinces in Region X with SP Types table within the database, you can follow the command below:

#Bukidnon, Region X
# Make dataframe
vRegion <- "Region X"
vProvince <- "Bukidnon"

vRegX_BUK_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-SettlementArea, -Municipality, -Barangay)

vRegX_BUK_SPTypes_tmp <- vRegX_BUK_SPTypes_tmp %>%
  filter(vRegX_BUK_SPTypes_tmp$Region == vRegion, 
         vRegX_BUK_SPTypes_tmp$Province == vProvince)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegX_BUK_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Bukidnon Region X with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	6	501	485.2685
Bridge	BRDG	Lms	1	0	18.0000
Crop Intensification	CI	Has	2	NA	300.0000
Farm to Market Road	FMR	Kms	3	0	12.6300
Irrigation	IRRIG	Has	3	0	265.0000
Post Harvest Facilities	PHF	Units	9	NA	11.0000
Rural Water Systems	RWS	HHs	1	0	212.0000
Total:			25	NA	1303.8985

#Lanao del Norte, Region X
# Make dataframe
vRegion <- "Region X"
vProvince <- "Lanao del Norte"

vRegX_LDN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-SettlementArea, -Municipality, -Barangay)

vRegX_LDN_SPTypes_tmp <- vRegX_LDN_SPTypes_tmp %>%
  filter(vRegX_LDN_SPTypes_tmp$Region == vRegion, 
         vRegX_LDN_SPTypes_tmp$Province == vProvince)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegX_LDN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Lanao del Norte Region X with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	18	2961	2867.091
Agro-forestry	AGRO	Has	16	745	4076.710
Bridge	BRDG	Lms	7	0	563.000
Farm to Market Road	FMR	Kms	8	0	53.300
Irrigation	IRRIG	Has	4	0	310.000
Post Harvest Facilities	PHF	Units	13	NA	20.000
Rural Water Systems	RWS	HHs	7	0	1698.000
Total:			73	NA	9588.101

To display a chart illustrating the records of Region X along with the corresponding number of SPs using the ggplot command in R.

vRegionX_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion)  %>%
  group_by(Province, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vRegionX_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ Province, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vRegionX_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Covered Provinces in", vRegion, "and SP Types w/ No. of Sub-Projects (SPs)"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Provinces in Region X with PO Types table within the database, you can follow the command below:

# Make dataframe
#Bukidnon
# Make dataframe
vProvince <- "Bukidnon"

vRegX_BUK_POTypes_tmp <- vLocations_POTypes %>%
  select(-SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegX_BUK_POTypes_tmp <- vRegX_BUK_POTypes_tmp %>%
  filter(vRegX_BUK_POTypes_tmp$Region == vRegion, vRegX_BUK_POTypes_tmp$Province == vProvince)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegX_BUK_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Bukidnon Region X with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	123	30	4	104	0	0	0	0
Farmers Association	FA	3	32	9	50	31	0	0	0	0
Irrigators Association	IA	1	19	7	55	13	0	0	0	0
Water Users Association	WUA	1	149	56	5	0	0	0	0	0
Womens Organization	WO	1	0	16	0	30	0	0	0	0
Total:		8	323	118	114	178	0	0	0	0

#Lanao del Norte
# Make dataframe
vProvince <- "Lanao del Norte"

vRegX_LDN_POTypes_tmp <- vLocations_POTypes %>%
  select(-SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegX_LDN_POTypes_tmp <- vRegX_LDN_POTypes_tmp %>%
  filter(vRegX_LDN_POTypes_tmp$Region == vRegion, vRegX_LDN_POTypes_tmp$Province == vProvince)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegX_LDN_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Lanao del Norte Region X with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	36	716	743	102	260	15	22	5	3
Farmers Association	FA	16	406	172	37	15	0	0	0	0
Irrigators Association	IA	4	69	23	17	7	0	0	0	0
Water Users Association	WUA	9	356	187	0	0	0	0	0	0
Womens Organization	WO	2	0	25	0	0	0	0	0	0
Total:		67	1547	1150	156	282	15	22	5	3

To display a chart illustrating the records of Provinces in Region along with the corresponding number of POs using the ggplot command in R.

vRegionX_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion)  %>%
  group_by(Province, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vRegionX_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 50, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Province, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Covered Provinces in", vRegion, "and PO Types with No. of PO Group"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Provinces in Region XI with SP Types table within the database, you can follow the command below:

#Davao de Oro, Region XI
# Make dataframe
vRegion <- "Region XI"
vProvince <- "Davao de Oro"

vRegXI_DDO_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-SettlementArea, -Municipality, -Barangay)

vRegXI_DDO_SPTypes_tmp <- vRegXI_DDO_SPTypes_tmp %>%
  filter(vRegXI_DDO_SPTypes_tmp$Region == vRegion, 
         vRegXI_DDO_SPTypes_tmp$Province == vProvince)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegXI_DDO_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Davao de Oro Region XI with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	5	869	840.76
Agro-forestry	AGRO	Has	2	74	408.90
Crop Intensification	CI	Has	2	NA	475.00
Farm to Market Road	FMR	Kms	2	0	16.07
Irrigation	IRRIG	Has	1	0	106.00
Post Harvest Facilities	PHF	Units	3	NA	6.00
Rural Water Systems	RWS	HHs	3	0	629.00
Total:			18	NA	2481.73

#Davao del Sur, Region XI
# Make dataframe
vProvince <- "Davao del Sur"

vRegXI_DDS_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-SettlementArea, -Municipality, -Barangay)

vRegXI_DDS_SPTypes_tmp <- vRegXI_DDS_SPTypes_tmp %>%
  filter(vRegXI_DDS_SPTypes_tmp$Region == vRegion, 
         vRegXI_DDS_SPTypes_tmp$Province == vProvince)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegXI_DDS_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Davao del Sur Region XI with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	7	1062	1026.29
Bridge	BRDG	Lms	2	0	55.00
Crop Intensification	CI	Has	2	NA	474.00
Farm to Market Road	FMR	Kms	4	0	18.59
Post Harvest Facilities	PHF	Units	5	NA	6.00
Rural Water Systems	RWS	HHs	4	0	1257.00
Total:			24	NA	2836.88

To display a chart illustrating the records of Region XI along with the corresponding number of SPs using the ggplot command in R.

vRegionXI_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion)  %>%
  group_by(Province, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vRegionXI_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ Province, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vRegionX_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Covered Provinces in", vRegion, "and SP Types w/ No. of Sub-Projects (SPs)"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Region XI Provinces table within the database, you can follow the command below:

#Davao de Oro
# Make dataframe
vProvince <- "Davao de Oro"

vRegXI_DDO_POTypes_tmp <- vLocations_POTypes %>%
  select(-SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegXI_DDO_POTypes_tmp <- vRegXI_DDO_POTypes_tmp %>%
  filter(vRegXI_DDO_POTypes_tmp$Region == vRegion, vRegXI_DDO_POTypes_tmp$Province == vProvince)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegXI_DDO_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Davao de Oro Region XI with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	4	60	54	2	0	61	54	1	0
Farmers Association	FA	2	650	324	0	0	650	324	0	0
Irrigators Association	IA	2	103	35	0	0	103	35	0	0
Water Users Association	WUA	3	515	394	0	0	515	394	0	0
Total:		11	1328	807	2	0	1329	807	1	0

#Davao del Sur
# Make dataframe
vProvince <- "Davao del Sur"

vRegXI_DDS_POTypes_tmp <- vLocations_POTypes %>%
  select(-SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegXI_DDS_POTypes_tmp <- vRegXI_DDS_POTypes_tmp %>%
  filter(vRegXI_DDS_POTypes_tmp$Region == vRegion, vRegXI_DDS_POTypes_tmp$Province == vProvince)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegXI_DDS_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Davao del Sur Region XI with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	59	103	113	129	0	0	0	0
Farmers Association	FA	27	1005	271	507	318	402	148	80	16
Water Users Association	WUA	4	24	150	306	132	148	84	218	89
Total:		34	1088	524	926	579	550	232	298	105

To display a chart illustrating the records of Provinces in Region XI along with the corresponding number of POs using the ggplot command in R.

vRegionXI_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion)  %>%
  group_by(Province, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vRegionXI_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Province, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Covered Provinces in",  vRegion, "and PO Types with No. of PO Group"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Provinces in Region XII with SP Types table within the database, you can follow the command below:

#North Cotabato, Region XII
# Make dataframe
vRegion <- "Region XII"
vProvince <- "North Cotabato"

vRegXII_NC_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-SettlementArea, -Municipality, -Barangay)

vRegXII_NC_SPTypes_tmp <- vRegXII_NC_SPTypes_tmp %>%
  filter(vRegXII_NC_SPTypes_tmp$Region == vRegion, 
         vRegXII_NC_SPTypes_tmp$Province == vProvince)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegXII_NC_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

North Cotabato Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	18	4682	4534.564
Agro-forestry	AGRO	Has	9	203	1102.385
Bridge	BRDG	Lms	2	0	54.000
Crop Intensification	CI	Has	6	NA	1407.000
Farm to Market Road	FMR	Kms	13	NA	80.322
Irrigation	IRRIG	Has	4	0	450.330
Post Harvest Facilities	PHF	Units	14	NA	18.000
Rural Water Systems	RWS	HHs	6	0	1771.000
Total:			72	NA	9417.600

#South Cotabato, Region XII
# Make dataframe
vProvince <- "South Cotabato"

vRegXII_SC_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-SettlementArea, -Municipality, -Barangay)

vRegXII_SC_SPTypes_tmp <- vRegXII_SC_SPTypes_tmp %>%
  filter(vRegXII_SC_SPTypes_tmp$Region == vRegion, 
         vRegXII_SC_SPTypes_tmp$Province == vProvince)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegXII_SC_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

South Cotabato Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	4	349	337.8100
Agro-forestry	AGRO	Has	2	78	425.2482
Bridge	BRDG	Lms	2	0	27.0000
Crop Intensification	CI	Has	1	NA	810.0000
Farm to Market Road	FMR	Kms	4	0	22.2900
Irrigation	IRRIG	Has	3	0	106.0000
Post Harvest Facilities	PHF	Units	6	NA	8.0000
Rural Water Systems	RWS	HHs	2	0	210.0000
Total:			24	NA	1946.3482

#Sultan Kudarat, Region XII
# Make dataframe
vRegion <- "Region XII"
vProvince <- "Sultan Kudarat"

vRegXII_SK_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-SettlementArea, -Municipality, -Barangay)

vRegXII_SK_SPTypes_tmp <- vRegXII_SK_SPTypes_tmp %>%
  filter(vRegXII_SK_SPTypes_tmp$Region == vRegion, 
         vRegXII_SK_SPTypes_tmp$Province == vProvince)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vRegXII_SK_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Sultan Kudarat Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	7	1576	1528.189
Agro-forestry	AGRO	Has	6	211	1164.533
Bridge	BRDG	Lms	1	0	60.000
Crop Intensification	CI	Has	9	NA	1760.000
Farm to Market Road	FMR	Kms	6	0	37.010
Irrigation	IRRIG	Has	4	0	693.000
Post Harvest Facilities	PHF	Units	15	NA	19.000
Rural Water Systems	RWS	HHs	3	NA	1300.000
Total:			51	NA	6561.732

To display a chart illustrating the records of Region XII along with the corresponding number of SPs using the ggplot command in R.

vRegionXII_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion)  %>%
  group_by(Province, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vRegionXII_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ Province, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vRegionX_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Covered Provinces in", vRegion, "and SP Types w/ No. of Sub-Projects (SPs)"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Region XII Provinces table within the database, you can follow the command below:

#North Cotabato
# Make dataframe
vProvince <- "North Cotabato"

vRegXII_NC_POTypes_tmp <- vLocations_POTypes %>%
  select(-SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegXII_NC_POTypes_tmp <- vRegXII_NC_POTypes_tmp %>%
  filter(vRegXII_NC_POTypes_tmp$Region == vRegion, vRegXII_NC_POTypes_tmp$Province == vProvince)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegXII_NC_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "North Cotabato, Region XII with PO Types table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

North Cotabato, Region XII with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	11	923	1206	740	1267	0	0	1663	2473
Farmers Association	FA	118	2134	1893	172	800	388	200	1845	1725
Irrigators Association	IA	6	144	61	0	0	0	0	144	61
Water Users Association	WUA	9	357	203	109	49	95	41	371	211
Womens Organization	WO	12	0	382	0	16	0	63	0	461
Total:		156	3558	3745	1021	2132	483	304	4023	4931

#South Cotabato
# Make dataframe
vProvince <- "South Cotabato"

vRegXII_SC_POTypes_tmp <- vLocations_POTypes %>%
  select(-SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegXII_SC_POTypes_tmp <- vRegXII_SC_POTypes_tmp %>%
  filter(vRegXII_SC_POTypes_tmp$Region == vRegion, vRegXII_SC_POTypes_tmp$Province == vProvince)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegXII_SC_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "South Cotabato, Region XII with PO Types table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

South Cotabato, Region XII with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	6	292	218	109	72	12	0	0	0
Irrigators Association	IA	3	0	0	0	0	0	0	0	0
Water Users Association	WUA	2	31	25	20	29	3	1	3	1
Total:		11	323	243	129	101	15	1	3	1

#Sultan Kudarat
# Make dataframe
vProvince <- "Sultan Kudarat"

vRegXII_SK_POTypes_tmp <- vLocations_POTypes %>%
  select(-SettlementArea, -Municipality, -Barangay) %>%
  drop_na()

vRegXII_SK_POTypes_tmp <- vRegXII_SK_POTypes_tmp %>%
  filter(vRegXII_SK_POTypes_tmp$Region == vRegion, vRegXII_SK_POTypes_tmp$Province == vProvince)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vRegXII_SK_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Sultan Kudarat, Region XII with PO Types table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sultan Kudarat, Region XII with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	6	583	528	11	10	0	0	0	0
Farmers Association	FA	40	1088	408	217	78	11	1	299	14
Irrigators Association	IA	5	209	42	0	0	0	0	0	0
Water Users Association	WUA	3	72	57	0	0	0	0	0	0
Womens Organization	WO	8	0	448	0	116	0	0	0	0
Total:		62	1952	1483	228	204	11	1	299	14

To display a chart illustrating the records of Provinces in Region XII along with the corresponding number of POs using the ggplot command in R.

vRegionXII_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == "Region XII")  %>%
  group_by(Province, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vRegionXII_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 200, by = 10), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Province, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= "Covered Provinces in Region XII and PO Types with No. of PO Group", 
       caption="Data Collected by: Junald A. Lagod")

To retrieve a list of covered Provines records along with the corresponding number of Settlements

To view the Province table within the database, you can follow the command below:

vLocPROVINCES_tmp <- vLocations_BRGYS_tmp %>%
  group_by(Region, Province, ProvCode)  %>%
  count(Settlement) %>%
  summarise(Settlements = n(), .groups = 'drop')

# Make dataframe
results_df <- data.frame(vLocPROVINCES_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Region", "Province", "Code", "No. of Settlements", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Covered Provinces table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Covered Provinces table

Region	Province	Code	No. of Settlements
Region X	Bukidnon	BUK	1
Region X	Lanao del Norte	LDN	3
Region XI	Davao de Oro	DDO	1
Region XI	Davao del Sur	DDS	1
Region XII	North Cotabato	NC	3
Region XII	South Cotabato	SC	1
Region XII	Sultan Kudarat	SK	2
Total:			12

To display a chart illustrating the records of Provinces along with the corresponding number of Settlements using the ggplot command in R.

ggplot(vLocPROVINCES_tmp, aes(x = ProvCode, y = as.integer(Settlements), fill=Province)) +
  geom_col() +
  geom_text(aes(label = as.integer(Settlements), y = as.integer(Settlements) / 2)) +
  scale_y_continuous(
    breaks = seq(0, 10, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="Provinces")) + 
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ Region, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocPROVINCES_tmp) + 
  xlab("Provinces") + ylab("Number of Settlements")  +
  labs(title= "Covered Provinces with Number of Settlements",  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Bukidnon, Region X with SP Types table within the database, you can follow the command below:

vRegion <- "Region X"
vProvince <- "Bukidnon"
vSettlementArea <- "Kadingilan Settlement Area"

vKAD_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vKAD_SA_SPTypes_tmp <- vKAD_SA_SPTypes_tmp %>%
  filter(vKAD_SA_SPTypes_tmp$Region == vRegion, 
         vKAD_SA_SPTypes_tmp$Province == vProvince,
         vKAD_SA_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vKAD_SA_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Bukidnon Region X with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	6	501	485.2685
Bridge	BRDG	Lms	1	0	18.0000
Crop Intensification	CI	Has	2	NA	300.0000
Farm to Market Road	FMR	Kms	3	0	12.6300
Irrigation	IRRIG	Has	3	0	265.0000
Post Harvest Facilities	PHF	Units	9	NA	11.0000
Rural Water Systems	RWS	HHs	1	0	212.0000
Total:			25	NA	1303.8985

To display a chart illustrating the records of Settlement Areas in Bukidnon, Region X along with the corresponding number of SPs using the ggplot command in R.

vKAD_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Province, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vKAD_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ Province, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vRegionX_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Bukidnon, Region X table within the database, you can follow the command below:

#Kadingilan Settlement Area
# Make dataframe
vKAD_SA_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vKAD_SA_POTypes_tmp <- vKAD_SA_POTypes_tmp %>%
  filter(vKAD_SA_POTypes_tmp$Region == vRegion, 
         vKAD_SA_POTypes_tmp$Province == vProvince,
         vKAD_SA_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vKAD_SA_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Kadingilan Settlement Area, Bukidnon with PO Types table", 
      booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Kadingilan Settlement Area, Bukidnon with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	123	30	4	104	0	0	0	0
Farmers Association	FA	3	32	9	50	31	0	0	0	0
Irrigators Association	IA	1	19	7	55	13	0	0	0	0
Water Users Association	WUA	1	149	56	5	0	0	0	0	0
Womens Organization	WO	1	0	16	0	30	0	0	0	0
Total:		8	323	118	114	178	0	0	0	0

To display a chart illustrating the records of Provinces in Region X along with the corresponding number of POs using the ggplot command in R.

vSA_KAD_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == "Region X", 
         vLocations_POTypes$Province == "Bukidnon")  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSA_KAD_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 10, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= "Settlement Areas in Bukidnon, Region X and PO Types with No. of PO Group", 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Lanao del Norte, Region X with SP Types table within the database, you can follow the command below:

vRegion <- "Region X"
vProvince <- "Lanao del Norte"
vSettlementArea <- "Lanao del Norte Settlement Area 1"

vLDN_SA1_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vLDN_SA1_SPTypes_tmp <- vLDN_SA1_SPTypes_tmp %>%
  filter(vLDN_SA1_SPTypes_tmp$Region == vRegion, 
         vLDN_SA1_SPTypes_tmp$Province == vProvince,
         vLDN_SA1_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vLDN_SA1_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Lanao del Norte Settlement Area 1 in Lanao del Norte Region X with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	12	1770	1714.251
Agro-forestry	AGRO	Has	11	480	2625.117
Bridge	BRDG	Lms	4	0	178.000
Farm to Market Road	FMR	Kms	7	0	51.320
Irrigation	IRRIG	Has	3	0	190.000
Post Harvest Facilities	PHF	Units	9	NA	14.000
Rural Water Systems	RWS	HHs	6	0	1508.000
Total:			52	NA	6280.688

vSettlementArea <- "Lanao del Norte Settlement Area 2"

vLDN_SA2_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vLDN_SA2_SPTypes_tmp <- vLDN_SA2_SPTypes_tmp %>%
  filter(vLDN_SA2_SPTypes_tmp$Region == vRegion, 
         vLDN_SA2_SPTypes_tmp$Province == vProvince,
         vLDN_SA2_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vLDN_SA2_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Lanao del Norte Settlement Area 2 in Lanao del Norte Region X with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	3	471	455.77
Agro-forestry	AGRO	Has	2	112	613.35
Bridge	BRDG	Lms	3	0	385.00
Farm to Market Road	FMR	Kms	1	0	1.98
Irrigation	IRRIG	Has	1	0	120.00
Post Harvest Facilities	PHF	Units	1	NA	2.00
Total:			11	NA	1578.10

vSettlementArea <- "Lanao del Norte Settlement Area 3"

vLDN_SA3_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vLDN_SA3_SPTypes_tmp <- vLDN_SA3_SPTypes_tmp %>%
  filter(vLDN_SA3_SPTypes_tmp$Region == vRegion, 
         vLDN_SA3_SPTypes_tmp$Province == vProvince,
         vLDN_SA3_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vLDN_SA3_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Lanao del Norte Settlement Area 3 in Lanao del Norte Region X with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	3	720	697.0703
Agro-forestry	AGRO	Has	3	153	838.2431
Post Harvest Facilities	PHF	Units	3	0	4.0000
Rural Water Systems	RWS	HHs	1	0	190.0000
Total:			10	873	1729.3134

To display a chart illustrating the records of Settlement Areas in Lanao del Norte, Region X along with the corresponding number of SPs using the ggplot command in R.

vLDN_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince)  %>%
  group_by(SettlementArea, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vLDN_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 2), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~ SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vRegionX_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Lanao del Norte, Region X table within the database, you can follow the command below:

#Lanao del Norte Settlement Area 1
# Make dataframe
vSettlementArea <- "Lanao del Norte Settlement Area 1"

vLDN_SA1_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vLDN_SA1_POTypes_tmp <- vLDN_SA1_POTypes_tmp %>%
  filter(vLDN_SA1_POTypes_tmp$Region == vRegion, 
         vLDN_SA1_POTypes_tmp$Province == vProvince,
         vLDN_SA1_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vLDN_SA1_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Lanao del Norte Settlement Area 1 in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	28	574	645	49	232	0	0	0	0
Farmers Association	FA	14	394	154	37	15	0	0	0	0
Irrigators Association	IA	3	69	23	17	7	0	0	0	0
Water Users Association	WUA	8	294	187	0	0	0	0	0	0
Womens Organization	WO	2	0	25	0	0	0	0	0	0
Total:		55	1331	1034	103	254	0	0	0	0

#Lanao del Norte Settlement Area 2
# Make dataframe
vSettlementArea <- "Lanao del Norte Settlement Area 2"

vLDN_SA2_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vLDN_SA2_POTypes_tmp <- vLDN_SA2_POTypes_tmp %>%
  filter(vLDN_SA2_POTypes_tmp$Region == vRegion, 
         vLDN_SA2_POTypes_tmp$Province == vProvince,
         vLDN_SA2_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vLDN_SA2_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Lanao del Norte Settlement Area 2 in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	5	47	33	22	8	15	22	5	3
Irrigators Association	IA	1	0	0	0	0	0	0	0	0
Total:		6	47	33	22	8	15	22	5	3

#Lanao del Norte Settlement Area 3
# Make dataframe
vSettlementArea <- "Lanao del Norte Settlement Area 3"

vLDN_SA3_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vLDN_SA3_POTypes_tmp <- vLDN_SA3_POTypes_tmp %>%
  filter(vLDN_SA3_POTypes_tmp$Region == vRegion, 
         vLDN_SA3_POTypes_tmp$Province == vProvince,
         vLDN_SA3_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vLDN_SA3_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Lanao del Norte Settlement Area 3 in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	95	65	31	20	0	0	0	0
Farmers Association	FA	2	12	18	0	0	0	0	0	0
Water Users Association	WUA	1	62	0	0	0	0	0	0	0
Total:		6	169	83	31	20	0	0	0	0

To display a chart illustrating the records of Settlement Areas in Lanao del Norte, Region X along with the corresponding number of POs using the ggplot command in R.

vSA_LDN_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == "Region X", 
         vLocations_POTypes$Province == "Lanao del Norte")  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSA_LDN_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and PO Types with No. of PO Group"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao de Oro, Region XI with SP Types table within the database, you can follow the command below:

vRegion <- "Region XI"
vProvince <- "Davao de Oro"
vSettlementArea <- "Karagan Valley Settlement Area"

vKAR_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vKAR_SA_SPTypes_tmp <- vKAR_SA_SPTypes_tmp %>%
  filter(vKAR_SA_SPTypes_tmp$Region == vRegion, 
         vKAR_SA_SPTypes_tmp$Province == vProvince,
         vKAR_SA_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vKAR_SA_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Karagan Valley Settlement Area in Davao de Oro with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	5	869	840.76
Agro-forestry	AGRO	Has	2	74	408.90
Crop Intensification	CI	Has	2	NA	475.00
Farm to Market Road	FMR	Kms	2	0	16.07
Irrigation	IRRIG	Has	1	0	106.00
Post Harvest Facilities	PHF	Units	3	NA	6.00
Rural Water Systems	RWS	HHs	3	0	629.00
Total:			18	NA	2481.73

To display a chart illustrating the records of Settlement Areas in Davao de Oro, Region XI along with the corresponding number of SPs using the ggplot command in R.

vKAR_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(SettlementArea, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vKAR_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vKAR_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao de Oro, Region XI table within the database, you can follow the command below:

#Karagan Valley Settlement Area
# Make dataframe
vKAR_SA_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vKAR_SA_POTypes_tmp <- vKAR_SA_POTypes_tmp %>%
  filter(vKAR_SA_POTypes_tmp$Region == vRegion, 
         vKAR_SA_POTypes_tmp$Province == vProvince,
         vKAR_SA_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vKAR_SA_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Karagan Valley Settlement Area in Davao de Oro with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	4	60	54	2	0	61	54	1	0
Farmers Association	FA	2	650	324	0	0	650	324	0	0
Irrigators Association	IA	2	103	35	0	0	103	35	0	0
Water Users Association	WUA	3	515	394	0	0	515	394	0	0
Total:		11	1328	807	2	0	1329	807	1	0

To display a chart illustrating the records of Settlement Areas in Region XI along with the corresponding number of POs using the ggplot command in R.

vSA_KAR_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSA_KAR_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 10, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vSA_KAR_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, vProvince, "and PO Types with No. of PO Group"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao del Sur, Region XI with SP Types table within the database, you can follow the command below:

vRegion <- "Region XI"
vProvince <- "Davao del Sur"
vSettlementArea <- "B'laan Settlement Area"

vBLA_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vBLA_SA_SPTypes_tmp <- vBLA_SA_SPTypes_tmp %>%
  filter(vBLA_SA_SPTypes_tmp$Region == vRegion, 
         vBLA_SA_SPTypes_tmp$Province == vProvince,
         vBLA_SA_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vBLA_SA_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

B’laan Settlement Area in Davao del Sur with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	7	1062	1026.29
Bridge	BRDG	Lms	2	0	55.00
Crop Intensification	CI	Has	2	NA	474.00
Farm to Market Road	FMR	Kms	4	0	18.59
Post Harvest Facilities	PHF	Units	5	NA	6.00
Rural Water Systems	RWS	HHs	4	0	1257.00
Total:			24	NA	2836.88

To display a chart illustrating the records of Settlement Areas in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vBLA_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(SettlementArea, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vBLA_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBLA_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao del Sur, Region XI table within the database, you can follow the command below:

#B'laan Settlement Area
# Make dataframe
vBLA_SA_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vBLA_SA_POTypes_tmp <- vBLA_SA_POTypes_tmp %>%
  filter(vBLA_SA_POTypes_tmp$Region == vRegion, 
         vBLA_SA_POTypes_tmp$Province == vProvince,
         vBLA_SA_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vBLA_SA_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

B’laan Settlement Area in Davao del Sur with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	59	103	113	129	0	0	0	0
Farmers Association	FA	27	1005	271	507	318	402	148	80	16
Water Users Association	WUA	4	24	150	306	132	148	84	218	89
Total:		34	1088	524	926	579	550	232	298	105

To display a chart illustrating the records of Settlement Areas in Region XI along with the corresponding number of POs using the ggplot command in R.

vSA_BLA_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSA_BLA_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 10), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vSA_BLA_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, vProvince, "and PO Types with No. of PO Group"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in North Cotabato, Region XII with SP Types table within the database, you can follow the command below:

vRegion <- "Region XII"
vProvince <- "North Cotabato"
vSettlementArea <- "North Cotabato Settlement Area 1"

vNC_SA1_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vNC_SA1_SPTypes_tmp <- vNC_SA1_SPTypes_tmp %>%
  filter(vNC_SA1_SPTypes_tmp$Region == vRegion, 
         vNC_SA1_SPTypes_tmp$Province == vProvince,
         vNC_SA1_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vNC_SA1_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

North Cotabato Settlement Area 1 in North Cotabato Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	7	899	870.7972
Agro-forestry	AGRO	Has	3	60	327.8200
Bridge	BRDG	Lms	1	0	24.0000
Crop Intensification	CI	Has	3	NA	1000.0000
Farm to Market Road	FMR	Kms	4	NA	35.1820
Irrigation	IRRIG	Has	2	0	284.3300
Post Harvest Facilities	PHF	Units	5	NA	5.0000
Rural Water Systems	RWS	HHs	1	0	196.0000
Total:			26	NA	2743.1292

vSettlementArea <- "North Cotabato Settlement Area 2"

vNC_SA2_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vNC_SA2_SPTypes_tmp <- vNC_SA2_SPTypes_tmp %>%
  filter(vNC_SA2_SPTypes_tmp$Region == vRegion, 
         vNC_SA2_SPTypes_tmp$Province == vProvince,
         vNC_SA2_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vNC_SA2_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

North Cotabato Settlement Area 2 in North Cotabato Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	7	2740	2653.4184
Agro-forestry	AGRO	Has	5	54	290.0248
Bridge	BRDG	Lms	1	0	30.0000
Crop Intensification	CI	Has	2	NA	150.0000
Farm to Market Road	FMR	Kms	6	0	24.1000
Irrigation	IRRIG	Has	2	0	166.0000
Post Harvest Facilities	PHF	Units	4	NA	6.0000
Rural Water Systems	RWS	HHs	2	0	300.0000
Total:			29	NA	3619.5432

vSettlementArea <- "Sultan Kudarat Settlement Area 1 Phase 2"

vNC_SA3_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vNC_SA3_SPTypes_tmp <- vNC_SA3_SPTypes_tmp %>%
  filter(vNC_SA3_SPTypes_tmp$Region == vRegion, 
         vNC_SA3_SPTypes_tmp$Province == vProvince,
         vNC_SA3_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vNC_SA3_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Sultan Kudarat Settlement Area 1 Phase 2 in North Cotabato Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	4	1043	1010.348
Agro-forestry	AGRO	Has	1	89	484.540
Crop Intensification	CI	Has	1	NA	257.000
Farm to Market Road	FMR	Kms	3	0	21.040
Post Harvest Facilities	PHF	Units	5	NA	7.000
Rural Water Systems	RWS	HHs	3	0	1275.000
Total:			17	NA	3054.928

To display a chart illustrating the records of Settlement Areas in North Cotabato, Region XII along with the corresponding number of SPs using the ggplot command in R.

vNC_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince)  %>%
  group_by(SettlementArea, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vNC_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNC_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in North Cotabato, Region XII table within the database, you can follow the command below:

#North Cotabato Settlement Area 1
# Make dataframe
vSettlementArea <- "North Cotabato Settlement Area 1"

vNC_SA1_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vNC_SA1_POTypes_tmp <- vNC_SA1_POTypes_tmp %>%
  filter(vNC_SA1_POTypes_tmp$Region == vRegion, 
         vNC_SA1_POTypes_tmp$Province == vProvince,
         vNC_SA1_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vNC_SA1_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

North Cotabato Settlement Area 1 in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	5	130	134	96	258	0	0	226	392
Farmers Association	FA	37	994	934	94	68	384	197	677	786
Irrigators Association	IA	3	47	9	0	0	0	0	47	9
Water Users Association	WUA	1	2	0	93	41	95	41	0	0
Womens Organization	WO	6	0	190	0	16	0	0	0	206
Total:		52	1173	1267	283	383	479	238	950	1393

#North Cotabato Settlement Area 2
# Make dataframe
vSettlementArea <- "North Cotabato Settlement Area 2"

vNC_SA2_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vNC_SA2_POTypes_tmp <- vNC_SA2_POTypes_tmp %>%
  filter(vNC_SA2_POTypes_tmp$Region == vRegion, 
         vNC_SA2_POTypes_tmp$Province == vProvince,
         vNC_SA2_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vNC_SA2_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

North Cotabato Settlement Area 2 in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	651	973	632	967	0	0	1283	1940
Farmers Association	FA	50	569	503	46	724	4	3	565	475
Irrigators Association	IA	2	76	42	0	0	0	0	76	42
Water Users Association	WUA	2	124	86	0	0	0	0	124	86
Total:		57	1420	1604	678	1691	4	3	2048	2543

#Sultan Kudarat Settlement Area 1 Phase 2
# Make dataframe
vSettlementArea <- "Sultan Kudarat Settlement Area 1 Phase 2"

vNC_SA3_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vNC_SA3_POTypes_tmp <- vNC_SA3_POTypes_tmp %>%
  filter(vNC_SA3_POTypes_tmp$Region == vRegion, 
         vNC_SA3_POTypes_tmp$Province == vProvince,
         vNC_SA3_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vNC_SA3_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sultan Kudarat Settlement Area 1 Phase 2 in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	142	99	12	42	0	0	154	141
Farmers Association	FA	31	571	456	32	8	0	0	603	464
Irrigators Association	IA	1	21	10	0	0	0	0	21	10
Water Users Association	WUA	6	231	117	16	8	0	0	247	125
Womens Organization	WO	6	0	192	0	0	0	63	0	255
Total:		47	965	874	60	58	0	63	1025	995

To display a chart illustrating the records of Settlement Areas in North Cotabato, Region XII along with the corresponding number of POs using the ggplot command in R.

vSA_NC_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince)  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSA_NC_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 50, by = 10), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in South Cotabato, Region XII with SP Types table within the database, you can follow the command below:

# Make dataframe
vRegion <- "Region XII"
vProvince <- "South Cotabato"
vSettlementArea <- "Ned Settlement Area"

vNED_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vNED_SA_SPTypes_tmp <- vNED_SA_SPTypes_tmp %>%
  filter(vNED_SA_SPTypes_tmp$Region == vRegion, 
         vNED_SA_SPTypes_tmp$Province == vProvince,
         vNED_SA_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vNED_SA_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Ned Settlement Area in South Cotabato Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	4	349	337.8100
Agro-forestry	AGRO	Has	2	78	425.2482
Bridge	BRDG	Lms	2	0	27.0000
Crop Intensification	CI	Has	1	NA	810.0000
Farm to Market Road	FMR	Kms	4	0	22.2900
Irrigation	IRRIG	Has	3	0	106.0000
Post Harvest Facilities	PHF	Units	6	NA	8.0000
Rural Water Systems	RWS	HHs	2	0	210.0000
Total:			24	NA	1946.3482

To display a chart illustrating the records of Settlement Areas in South Cotabato, Region XII along with the corresponding number of SPs using the ggplot command in R.

vNED_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince)  %>%
  group_by(SettlementArea, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vNED_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNED_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in South Cotabato, Region XII table within the database, you can follow the command below:

#Ned Settlement Area
vNED_SA_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vNED_SA_POTypes_tmp <- vNED_SA_POTypes_tmp %>%
  filter(vNED_SA_POTypes_tmp$Region == vRegion, 
         vNED_SA_POTypes_tmp$Province == vProvince,
         vNED_SA_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vNED_SA_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Ned Settlement Area in South Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	6	292	218	109	72	12	0	0	0
Irrigators Association	IA	3	0	0	0	0	0	0	0	0
Water Users Association	WUA	2	31	25	20	29	3	1	3	1
Total:		11	323	243	129	101	15	1	3	1

To display a chart illustrating the records of Settlement Areas in South Cotabato, Region XII along with the corresponding number of POs using the ggplot command in R.

vSA_NED_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince)  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSA_NED_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and PO Types w/ No. of POs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Sultan Kudarat, Region XII with SP Types table within the database, you can follow the command below:

# Make dataframe
vRegion <- "Region XII"
vProvince <- "Sultan Kudarat"
vSettlementArea <- "Sultan Kudarat Settlement Area 1"

vSK_SA1_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vSK_SA1_SPTypes_tmp <- vSK_SA1_SPTypes_tmp %>%
  filter(vSK_SA1_SPTypes_tmp$Region == vRegion, 
         vSK_SA1_SPTypes_tmp$Province == vProvince,
         vSK_SA1_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vSK_SA1_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Sultan Kudarat Settlement Area 1 in Sultan Kudarat Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	442	428.9653
Agro-forestry	AGRO	Has	2	74	408.8965
Crop Intensification	CI	Has	2	150	300.0000
Farm to Market Road	FMR	Kms	2	0	6.6800
Irrigation	IRRIG	Has	3	0	657.0000
Post Harvest Facilities	PHF	Units	1	NA	2.0000
Rural Water Systems	RWS	HHs	1	0	659.0000
Total:			13	NA	2462.5418

vSettlementArea <- "Sultan Kudarat Settlement Area 2"

vSK_SA2_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Municipality, -Barangay)

vSK_SA2_SPTypes_tmp <- vSK_SA2_SPTypes_tmp %>%
  filter(vSK_SA2_SPTypes_tmp$Region == vRegion, 
         vSK_SA2_SPTypes_tmp$Province == vProvince,
         vSK_SA2_SPTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vSK_SA2_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, vRegion, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Sultan Kudarat Settlement Area 2 in Sultan Kudarat Region XII with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	5	1134	1099.2236
Agro-forestry	AGRO	Has	4	137	755.6369
Bridge	BRDG	Lms	1	0	60.0000
Crop Intensification	CI	Has	7	NA	1460.0000
Farm to Market Road	FMR	Kms	4	0	30.3300
Irrigation	IRRIG	Has	1	0	36.0000
Post Harvest Facilities	PHF	Units	14	NA	17.0000
Rural Water Systems	RWS	HHs	2	NA	641.0000
Total:			38	NA	4099.1906

To display a chart illustrating the records of Settlement Areas in Sultan Kudarat, Region XII along with the corresponding number of SPs using the ggplot command in R.

vSK_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince)  %>%
  group_by(SettlementArea, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vSK_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vSK_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Sultan Kudarat, Region XII table within the database, you can follow the command below:

#Sultan Kudarat Settlement Area 1
# Make dataframe
vSettlementArea <- "Sultan Kudarat Settlement Area 1"
vSK_SA1_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vSK_SA1_POTypes_tmp <- vSK_SA1_POTypes_tmp %>%
  filter(vSK_SA1_POTypes_tmp$Region == vRegion, 
         vSK_SA1_POTypes_tmp$Province == vProvince,
         vSK_SA1_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vSK_SA1_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sultan Kudarat Settlement Area 1 in Sultan Kudarat with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	275	331	0	0	0	0	0	0
Farmers Association	FA	13	98	37	43	0	9	0	0	0
Irrigators Association	IA	4	187	36	0	0	0	0	0	0
Water Users Association	WUA	1	72	57	0	0	0	0	0	0
Total:		19	632	461	43	0	9	0	0	0

#Sultan Kudarat Settlement Area 2
# Make dataframe
vSettlementArea <- "Sultan Kudarat Settlement Area 2"
vSK_SA2_POTypes_tmp <- vLocations_POTypes %>%
  select(-Municipality, -Barangay) %>%
  drop_na()

vSK_SA2_POTypes_tmp <- vSK_SA2_POTypes_tmp %>%
  filter(vSK_SA2_POTypes_tmp$Region == vRegion, 
         vSK_SA2_POTypes_tmp$Province == vProvince,
         vSK_SA2_POTypes_tmp$SettlementArea == vSettlementArea)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vSK_SA2_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vSettlementArea, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sultan Kudarat Settlement Area 2 in Sultan Kudarat with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	5	308	197	11	10	0	0	0	0
Farmers Association	FA	27	990	371	174	78	2	1	299	14
Irrigators Association	IA	1	22	6	0	0	0	0	0	0
Water Users Association	WUA	2	0	0	0	0	0	0	0	0
Womens Organization	WO	8	0	448	0	116	0	0	0	0
Total:		43	1320	1022	185	204	2	1	299	14

To display a chart illustrating the records of Settlement Areas in South Cotabato, Region XII along with the corresponding number of POs using the ggplot command in R.

vSA_SK_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == "Region XII", 
         vLocations_POTypes$Province == "Sultan Kudarat")  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSA_SK_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLocations_POTypes) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Settlement Areas in", vProvince, vRegion, "and PO Types w/ No. of POs"),  
       caption="Data Collected by: Junald A. Lagod")

To retrieve a list of covered Settlement Areas records along with the corresponding number of Municipalities.

To view the Settlements table within the database, you can follow the command below:

vLocSETTLEMENTS_tmp <- vLocations_BRGYS_tmp %>%
  group_by(Region, Province, Settlement, SACode)  %>%
  count(Municipality) %>%
  summarise(Municipalities = n(), .groups = 'drop')

# Make dataframe
results_df <- data.frame(vLocSETTLEMENTS_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Region", "Province", "Settlement", "Code", "No. of Municipalities", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Covered Settlement Areas table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Covered Settlement Areas table

Region	Province	Settlement	Code	No. of Municipalities
Region X	Bukidnon	Kadingilan Settlement Area	KAD-SA	1
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	LDN1-SA	7
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	LDN2-SA	1
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	LDN3-SA	2
Region XI	Davao de Oro	Karagan Valley Settlement Area	KAR-SA	2
Region XI	Davao del Sur	B’laan Settlement Area	BLA-SA	2
Region XII	North Cotabato	North Cotabato Settlement Area 1	NC1-SA	2
Region XII	North Cotabato	North Cotabato Settlement Area 2	NC2-SA	3
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	SK1-P2-SA	2
Region XII	South Cotabato	Ned Settlement Area	NED-SA	1
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	SK1-SA	1
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	SK2-SA	3
Total:				27

To display a chart illustrating the records of Settlements along with the corresponding number of Municipalities using the ggplot command in R.

ggplot(vLocSETTLEMENTS_tmp) +
  geom_col(aes(as.integer(Municipalities), Settlement), fill = "#ABC123", width = 0.6)  + 
  geom_text(data = subset(vLocSETTLEMENTS_tmp, Municipalities >= 1),
    aes(0, y = Settlement, label = Settlement), hjust = 0, size = 3) +
    scale_x_continuous(
      breaks = seq(0, 10, by = 1), 
      expand = c(0, 0), # The horizontal axis does not extend to either side
      position = "top"  # Labels are located on the top
    )  +
    theme(axis.text.y = element_blank()) +
    xlab("Number of Municipalities") + ylab("Settlement Areas") + 
    labs(title= "Covered Settlements with Number of Municipalities", 
         caption="Data Collected by: Junald A. Lagod")  

To view the Settlement Areas in Bukidnon, Region X with SP Types table within the database, you can follow the command below:

vRegion <- "Region X"
vProvince <- "Bukidnon"
vSettlementArea <- "Kadingilan Settlement Area"
vMunicipality <- "Kadingilan"

vKAD_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vKAD_MUN_SPTypes_tmp <- vKAD_MUN_SPTypes_tmp %>%
  filter(vKAD_MUN_SPTypes_tmp$Region == vRegion, 
         vKAD_MUN_SPTypes_tmp$Province == vProvince,
         vKAD_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vKAD_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vKAD_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Kadingilan Bukidnon with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	6	501	485.2685
Bridge	BRDG	Lms	1	0	18.0000
Crop Intensification	CI	Has	2	NA	300.0000
Farm to Market Road	FMR	Kms	3	0	12.6300
Irrigation	IRRIG	Has	3	0	265.0000
Post Harvest Facilities	PHF	Units	9	NA	11.0000
Rural Water Systems	RWS	HHs	1	0	212.0000
Total:			25	NA	1303.8985

To display a chart illustrating the records of Municipalities in Bukidnon, Region X along with the corresponding number of SPs using the ggplot command in R.

vKAD_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vKAD_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vKAD_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Kadingilan, Bukidnon, Region X table within the database, you can follow the command below:

#Kadingilan
# Make dataframe
vKAD_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vKAD_POTypes_tmp <- vKAD_POTypes_tmp %>%
  filter(vKAD_POTypes_tmp$Region == vRegion, 
         vKAD_POTypes_tmp$Province == vProvince,
         vKAD_POTypes_tmp$SettlementArea == vSettlementArea,
         vKAD_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vKAD_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Kadingilan in Bukidnon with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	123	30	4	104	0	0	0	0
Farmers Association	FA	3	32	9	50	31	0	0	0	0
Irrigators Association	IA	1	19	7	55	13	0	0	0	0
Water Users Association	WUA	1	149	56	5	0	0	0	0	0
Womens Organization	WO	1	0	16	0	30	0	0	0	0
Total:		8	323	118	114	178	0	0	0	0

To display a chart illustrating the records of Kadingilan Settlement Area in Bukidnon, Region X along with the corresponding number of POs using the ggplot command in R.

vKAD_SA_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea,
         vLocations_POTypes$Municipality == vMunicipality)  %>%
  group_by(SettlementArea, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vKAD_SA_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~SettlementArea, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vKAD_SA_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vMunicipality, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Lanao del Norte, Region X with SP Types table within the database, you can follow the command below:

vRegion <- "Region X"
vProvince <- "Lanao del Norte"
vSettlementArea <- "Lanao del Norte Settlement Area 1"
vMunicipality <- "Kolambugan"

vKOL_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vKOL_MUN_SPTypes_tmp <- vKOL_MUN_SPTypes_tmp %>%
  filter(vKOL_MUN_SPTypes_tmp$Region == vRegion, 
         vKOL_MUN_SPTypes_tmp$Province == vProvince,
         vKOL_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vKOL_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vKOL_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Kolambugan Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	17	16.08620
Agro-forestry	AGRO	Has	1	4	20.44482
Farm to Market Road	FMR	Kms	1	0	6.02000
Post Harvest Facilities	PHF	Units	1	0	1.00000
Rural Water Systems	RWS	HHs	1	0	473.00000
Total:			6	21	516.55102

vMunicipality <- "Magsaysay"

vMAG1_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vMAG1_MUN_SPTypes_tmp <- vMAG1_MUN_SPTypes_tmp %>%
  filter(vMAG1_MUN_SPTypes_tmp$Region == vRegion, 
         vMAG1_MUN_SPTypes_tmp$Province == vProvince,
         vMAG1_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vMAG1_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vMAG1_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Magsaysay Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	61	58.98207
Agro-forestry	AGRO	Has	3	280	1531.31948
Farm to Market Road	FMR	Kms	1	0	7.46000
Irrigation	IRRIG	Has	1	0	20.00000
Post Harvest Facilities	PHF	Units	1	NA	2.00000
Rural Water Systems	RWS	HHs	1	0	220.00000
Total:			9	NA	1839.76154

vMunicipality <- "Maigo"

vMAI_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vMAI_MUN_SPTypes_tmp <- vMAI_MUN_SPTypes_tmp %>%
  filter(vMAI_MUN_SPTypes_tmp$Region == vRegion, 
         vMAI_MUN_SPTypes_tmp$Province == vProvince,
         vMAI_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vMAI_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vMAI_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Maigo Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	3	1056	1022.546
Bridge	BRDG	Lms	1	0	30.000
Farm to Market Road	FMR	Kms	1	0	11.150
Post Harvest Facilities	PHF	Units	1	0	2.000
Total:			6	1056	1065.696

vMunicipality <- "Munai"

vMUN_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vMUN_MUN_SPTypes_tmp <- vMUN_MUN_SPTypes_tmp %>%
  filter(vMUN_MUN_SPTypes_tmp$Region == vRegion, 
         vMUN_MUN_SPTypes_tmp$Province == vProvince,
         vMUN_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vMUN_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vMUN_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Munai Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	1	55	53.62066
Agro-forestry	AGRO	Has	3	106	582.67750
Bridge	BRDG	Lms	1	0	80.00000
Farm to Market Road	FMR	Kms	1	0	7.93000
Post Harvest Facilities	PHF	Units	2	NA	3.00000
Rural Water Systems	RWS	HHs	1	0	176.00000
Total:			9	NA	903.22817

vMunicipality <- "Pantao Ragat"

vPAN_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vPAN_MUN_SPTypes_tmp <- vPAN_MUN_SPTypes_tmp %>%
  filter(vPAN_MUN_SPTypes_tmp$Region == vRegion, 
         vPAN_MUN_SPTypes_tmp$Province == vProvince,
         vPAN_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vPAN_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vPAN_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Pantao Ragat Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	304	294.9127
Bridge	BRDG	Lms	1	0	50.0000
Farm to Market Road	FMR	Kms	1	0	3.9500
Post Harvest Facilities	PHF	Units	2	NA	3.0000
Rural Water Systems	RWS	HHs	2	0	389.0000
Total:			8	NA	740.8627

vMunicipality <- "Tangcal"

vTAN_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vTAN_MUN_SPTypes_tmp <- vTAN_MUN_SPTypes_tmp %>%
  filter(vTAN_MUN_SPTypes_tmp$Region == vRegion, 
         vTAN_MUN_SPTypes_tmp$Province == vProvince,
         vTAN_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vTAN_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vTAN_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Tangcal Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	1	111	107.2413
Agro-forestry	AGRO	Has	2	49	265.7800
Bridge	BRDG	Lms	1	0	18.0000
Farm to Market Road	FMR	Kms	1	0	5.9700
Irrigation	IRRIG	Has	1	0	40.0000
Post Harvest Facilities	PHF	Units	1	NA	2.0000
Rural Water Systems	RWS	HHs	1	0	250.0000
Total:			8	NA	688.9913

vMunicipality <- "Tubod"

vTUB_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vTUB_MUN_SPTypes_tmp <- vTUB_MUN_SPTypes_tmp %>%
  filter(vTUB_MUN_SPTypes_tmp$Region == vRegion, 
         vTUB_MUN_SPTypes_tmp$Province == vProvince,
         vTUB_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vTUB_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vTUB_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Tubod Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	1	166	160.8620
Agro-forestry	AGRO	Has	2	41	224.8948
Farm to Market Road	FMR	Kms	1	0	8.8400
Irrigation	IRRIG	Has	1	0	130.0000
Post Harvest Facilities	PHF	Units	1	NA	1.0000
Total:			6	NA	525.5968

To display a chart illustrating the records of Municipalities in Lanao del Norte, Region X along with the corresponding number of SPs using the ggplot command in R.

vLDN_SA1_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vLDN_SA1_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLDN_SA1_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Area 1 in Lanao del Norte, Region X table within the database, you can follow the command below:

#Kolambugan
# Make dataframe
vMunicipality <- "Kolambugan"

vKOL_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vKOL_POTypes_tmp <- vKOL_POTypes_tmp %>%
  filter(vKOL_POTypes_tmp$Region == vRegion, 
         vKOL_POTypes_tmp$Province == vProvince,
         vKOL_POTypes_tmp$SettlementArea == vSettlementArea,
         vKOL_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vKOL_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Kolambugan in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	6	90	213	6	17	0	0	0	0
Farmers Association	FA	3	0	0	0	0	0	0	0	0
Water Users Association	WUA	2	27	37	0	0	0	0	0	0
Total:		11	117	250	6	17	0	0	0	0

#Magsaysay
# Make dataframe
vMunicipality <- "Magsaysay"
vMAG1_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vMAG1_POTypes_tmp <- vMAG1_POTypes_tmp %>%
  filter(vMAG1_POTypes_tmp$Region == vRegion, 
         vMAG1_POTypes_tmp$Province == vProvince,
         vMAG1_POTypes_tmp$SettlementArea == vSettlementArea,
         vMAG1_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')


results_df <- data.frame(vMAG1_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Magsaysay in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	9	114	148	0	6	0	0	0	0
Irrigators Association	IA	1	0	0	0	0	0	0	0	0
Water Users Association	WUA	1	82	83	0	0	0	0	0	0
Total:		11	196	231	0	6	0	0	0	0

#Maigo
# Make dataframe
vMunicipality <- "Maigo"
vMAI_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vMAI_POTypes_tmp <- vMAI_POTypes_tmp %>%
  filter(vMAI_POTypes_tmp$Region == vRegion, 
         vMAI_POTypes_tmp$Province == vProvince,
         vMAI_POTypes_tmp$SettlementArea == vSettlementArea,
         vMAI_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')


results_df <- data.frame(vMAI_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Maigo in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	23	12	14	6	0	0	0	0
Farmers Association	FA	2	331	120	0	0	0	0	0	0
Total:		3	354	132	14	6	0	0	0	0

#Munai
# Make dataframe
vMunicipality <- "Munai"
vMUN_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vMUN_POTypes_tmp <- vMUN_POTypes_tmp %>%
  filter(vMUN_POTypes_tmp$Region == vRegion, 
         vMUN_POTypes_tmp$Province == vProvince,
         vMUN_POTypes_tmp$SettlementArea == vSettlementArea,
         vMUN_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vMUN_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Munai in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	23	33	23	61	0	0	0	0
Farmers Association	FA	5	49	23	37	15	0	0	0	0
Water Users Association	WUA	1	40	9	0	0	0	0	0	0
Total:		8	112	65	60	76	0	0	0	0

#Pantao Ragat
# Make dataframe
vMunicipality <- "Pantao Ragat"
vPAN_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vPAN_POTypes_tmp <- vPAN_POTypes_tmp %>%
  filter(vPAN_POTypes_tmp$Region == vRegion, 
         vPAN_POTypes_tmp$Province == vProvince,
         vPAN_POTypes_tmp$SettlementArea == vSettlementArea,
         vPAN_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vPAN_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Pantao Ragat in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	4	93	44	0	67	0	0	0	0
Farmers Association	FA	1	0	0	0	0	0	0	0	0
Water Users Association	WUA	2	95	48	0	0	0	0	0	0
Total:		7	188	92	0	67	0	0	0	0

#Tangcal
# Make dataframe
vMunicipality <- "Tangcal"
vTAN_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vTAN_POTypes_tmp <- vTAN_POTypes_tmp %>%
  filter(vTAN_POTypes_tmp$Region == vRegion, 
         vTAN_POTypes_tmp$Province == vProvince,
         vTAN_POTypes_tmp$SettlementArea == vSettlementArea,
         vTAN_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vTAN_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Tangcal in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	40	57	0	0	0	0	0	0
Farmers Association	FA	3	14	11	0	0	0	0	0	0
Irrigators Association	IA	1	27	9	17	7	0	0	0	0
Water Users Association	WUA	2	50	10	0	0	0	0	0	0
Womens Organization	WO	2	0	25	0	0	0	0	0	0
Total:		11	131	112	17	7	0	0	0	0

#Tubod
# Make dataframe
vMunicipality <- "Tubod"
vTUB_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vTUB_POTypes_tmp <- vTUB_POTypes_tmp %>%
  filter(vTUB_POTypes_tmp$Region == vRegion, 
         vTUB_POTypes_tmp$Province == vProvince,
         vTUB_POTypes_tmp$SettlementArea == vSettlementArea,
         vTUB_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vTUB_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Tubod in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	191	138	6	75	0	0	0	0
Irrigators Association	IA	1	42	14	0	0	0	0	0	0
Total:		4	233	152	6	75	0	0	0	0

To display a chart illustrating the records of Lanao del Norte Settlement Area 1 in Lanao del Norte, Region X along with the corresponding number of POs using the ggplot command in R.

vSettlementArea <- "Lanao del Norte Settlement Area 1"
vLDNSA1_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vLDNSA1_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 2), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLDNSA1_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Lanao del Norte, Region X with SP Types table within the database, you can follow the command below:

vRegion <- "Region X"
vProvince <- "Lanao del Norte"
vSettlementArea <- "Lanao del Norte Settlement Area 2"
vMunicipality <- "Sapad"

vSAP_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vSAP_MUN_SPTypes_tmp <- vSAP_MUN_SPTypes_tmp %>%
  filter(vSAP_MUN_SPTypes_tmp$Region == vRegion, 
         vSAP_MUN_SPTypes_tmp$Province == vProvince,
         vSAP_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vSAP_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vSAP_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Sapad Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	3	471	455.77
Agro-forestry	AGRO	Has	2	112	613.35
Bridge	BRDG	Lms	3	0	385.00
Farm to Market Road	FMR	Kms	1	0	1.98
Irrigation	IRRIG	Has	1	0	120.00
Post Harvest Facilities	PHF	Units	1	NA	2.00
Total:			11	NA	1578.10

To display a chart illustrating the records of Municipalities in Lanao del Norte, Region X along with the corresponding number of SPs using the ggplot command in R.

vLDN2_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vLDN2_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLDN2_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Area 2 in Lanao del Norte, Region X table within the database, you can follow the command below:

#Sapad
# Make dataframe
vSAP_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vSAP_POTypes_tmp <- vSAP_POTypes_tmp %>%
  filter(vSAP_POTypes_tmp$Region == vRegion, 
         vSAP_POTypes_tmp$Province == vProvince,
         vSAP_POTypes_tmp$SettlementArea == vSettlementArea,
         vSAP_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vSAP_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sapad in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	5	47	33	22	8	15	22	5	3
Irrigators Association	IA	1	0	0	0	0	0	0	0	0
Total:		6	47	33	22	8	15	22	5	3

To display a chart illustrating the records of Lanao del Norte Settlement Area 2 in Lanao del Norte, Region X along with the corresponding number of POs using the ggplot command in R.

vLDNSA2_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vLDNSA2_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLDNSA2_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Lanao del Norte, Region X with SP Types table within the database, you can follow the command below:

vRegion <- "Region X"
vProvince <- "Lanao del Norte"
vSettlementArea <- "Lanao del Norte Settlement Area 3"
vMunicipality <- "Nunungan"

vNUN_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vNUN_MUN_SPTypes_tmp <- vNUN_MUN_SPTypes_tmp %>%
  filter(vNUN_MUN_SPTypes_tmp$Region == vRegion, 
         vNUN_MUN_SPTypes_tmp$Province == vProvince,
         vNUN_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vNUN_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vNUN_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Nunungan Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	1	138	134.0500
Agro-forestry	AGRO	Has	2	78	429.3431
Post Harvest Facilities	PHF	Units	2	0	3.0000
Rural Water Systems	RWS	HHs	1	0	190.0000
Total:			6	216	756.3931

vMunicipality <- "Sultan Naga Dimaporo"

vSND_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vSND_MUN_SPTypes_tmp <- vSND_MUN_SPTypes_tmp %>%
  filter(vSND_MUN_SPTypes_tmp$Region == vRegion, 
         vSND_MUN_SPTypes_tmp$Province == vProvince,
         vSND_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vSND_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vSND_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Sultan Naga Dimaporo Lanao del Norte with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	582	563.0203
Agro-forestry	AGRO	Has	1	75	408.9000
Post Harvest Facilities	PHF	Units	1	0	1.0000
Total:			4	657	972.9203

To display a chart illustrating the records of Municipalities in Lanao del Norte, Region X along with the corresponding number of SPs using the ggplot command in R.

vLDN3_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vLDN3_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLDN3_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Area 2 in Lanao del Norte, Region X table within the database, you can follow the command below:

#Nunungan
# Make dataframe
vMunicipality <- "Nunungan"

vNUN_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vNUN_POTypes_tmp <- vNUN_POTypes_tmp %>%
  filter(vNUN_POTypes_tmp$Region == vRegion, 
         vNUN_POTypes_tmp$Province == vProvince,
         vNUN_POTypes_tmp$SettlementArea == vSettlementArea,
         vNUN_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vNUN_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Nunungan in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	52	28	31	20	0	0	0	0
Farmers Association	FA	1	0	0	0	0	0	0	0	0
Water Users Association	WUA	1	62	0	0	0	0	0	0	0
Total:		4	114	28	31	20	0	0	0	0

#Sultan Naga Dimaporo
# Make dataframe
vMunicipality <- "Sultan Naga Dimaporo"
vSND_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vSND_POTypes_tmp <- vSND_POTypes_tmp %>%
  filter(vSND_POTypes_tmp$Region == vRegion, 
         vSND_POTypes_tmp$Province == vProvince,
         vSND_POTypes_tmp$SettlementArea == vSettlementArea,
         vSND_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vSND_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Sultan Naga Dimaporo in Lanao del Norte with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	43	37	0	0	0	0	0	0
Farmers Association	FA	1	12	18	0	0	0	0	0	0
Total:		2	55	55	0	0	0	0	0	0

To display a chart illustrating the records of Lanao del Norte Settlement Area 3 in Lanao del Norte, Region X along with the corresponding number of POs using the ggplot command in R.

vLDNSA3_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vLDNSA3_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vLDNSA3_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao de Oro, Region XI with SP Types table within the database, you can follow the command below:

vRegion <- "Region XI"
vProvince <- "Davao de Oro"
vSettlementArea <- "Karagan Valley Settlement Area"
vMunicipality <- "Maragusan"

vMAR_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vMAR_MUN_SPTypes_tmp <- vMAR_MUN_SPTypes_tmp %>%
  filter(vMAR_MUN_SPTypes_tmp$Region == vRegion, 
         vMAR_MUN_SPTypes_tmp$Province == vProvince,
         vMAR_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vMAR_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vMAR_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Maragusan Davao de Oro with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	1	28	26.81
Crop Intensification	CI	Has	2	NA	475.00
Farm to Market Road	FMR	Kms	1	0	10.32
Irrigation	IRRIG	Has	1	0	106.00
Post Harvest Facilities	PHF	Units	2	NA	4.00
Rural Water Systems	RWS	HHs	2	0	339.00
Total:			9	NA	961.13

vMunicipality <- "New Bataan"

vNEW_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vNEW_MUN_SPTypes_tmp <- vNEW_MUN_SPTypes_tmp %>%
  filter(vNEW_MUN_SPTypes_tmp$Region == vRegion, 
         vNEW_MUN_SPTypes_tmp$Province == vProvince,
         vNEW_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vNEW_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vNEW_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

New Bataan Davao de Oro with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	4	841	813.95
Agro-forestry	AGRO	Has	2	74	408.90
Farm to Market Road	FMR	Kms	1	0	5.75
Post Harvest Facilities	PHF	Units	1	NA	2.00
Rural Water Systems	RWS	HHs	1	0	290.00
Total:			9	NA	1520.60

To display a chart illustrating the records of Municipalities in Davao de Oro, Region XI along with the corresponding number of SPs using the ggplot command in R.

vKAR_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vKAR_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vKAR_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao de Oro, Region XI table within the database, you can follow the command below:

#Maragusan
# Make dataframe
vMunicipality <- "Maragusan"

vMAR_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vMAR_POTypes_tmp <- vLocations_POTypes %>%
  filter(vMAR_POTypes_tmp$Region == vRegion, 
         vMAR_POTypes_tmp$Province == vProvince,
         vMAR_POTypes_tmp$SettlementArea == vSettlementArea,
         vMAR_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vMAR_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Maragusan in Davao de Oro with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	60	54	2	0	61	54	1	0
Farmers Association	FA	2	650	324	0	0	650	324	0	0
Irrigators Association	IA	1	40	9	0	0	40	9	0	0
Water Users Association	WUA	2	240	254	0	0	240	254	0	0
Total:		7	990	641	2	0	991	641	1	0

#New Bataan
# Make dataframe
vMunicipality <- "New Bataan"

vNEW_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vNEW_POTypes_tmp <- vLocations_POTypes %>%
  filter(vNEW_POTypes_tmp$Region == vRegion, 
         vNEW_POTypes_tmp$Province == vProvince,
         vNEW_POTypes_tmp$SettlementArea == vSettlementArea,
         vNEW_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vNEW_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

New Bataan in Davao de Oro with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	0	0	0	0	0	0	0	0
Irrigators Association	IA	1	63	26	0	0	63	26	0	0
Water Users Association	WUA	1	275	140	0	0	275	140	0	0
Total:		4	338	166	0	0	338	166	0	0

To display a chart illustrating the records of Karagan Valley Settlement Area in Davao de Oro, Region XI along with the corresponding number of POs using the ggplot command in R.

vKARSA_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vKARSA_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vKARSA_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao del Sur, Region XI with SP Types table within the database, you can follow the command below:

vRegion <- "Region XI"
vProvince <- "Davao del Sur"
vSettlementArea <- "B'laan Settlement Area"
vMunicipality <- "Magsaysay"

vMAG2_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vMAG2_MUN_SPTypes_tmp <- vMAG2_MUN_SPTypes_tmp %>%
  filter(vMAG2_MUN_SPTypes_tmp$Region == vRegion, 
         vMAG2_MUN_SPTypes_tmp$Province == vProvince,
         vMAG2_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vMAG2_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vMAG2_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Magsaysay Davao del Sur with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	3	335	323.33
Bridge	BRDG	Lms	1	0	25.00
Crop Intensification	CI	Has	2	NA	474.00
Farm to Market Road	FMR	Kms	2	0	11.46
Post Harvest Facilities	PHF	Units	2	NA	2.00
Rural Water Systems	RWS	HHs	2	0	600.00
Total:			12	NA	1435.79

vMunicipality <- "Matanao"

vMAT_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vMAT_MUN_SPTypes_tmp <- vMAT_MUN_SPTypes_tmp %>%
  filter(vMAT_MUN_SPTypes_tmp$Region == vRegion, 
         vMAT_MUN_SPTypes_tmp$Province == vProvince,
         vMAT_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vMAT_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vMAT_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Matanao Davao del Sur with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	4	727	702.9603
Bridge	BRDG	Lms	1	0	30.0000
Farm to Market Road	FMR	Kms	2	0	7.1300
Post Harvest Facilities	PHF	Units	3	NA	4.0000
Rural Water Systems	RWS	HHs	2	0	657.0000
Total:			12	NA	1401.0903

To display a chart illustrating the records of Municipalities in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vBLA_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vBLA_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBLA_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Davao del Sur, Region XI table within the database, you can follow the command below:

#Magsaysay
# Make dataframe
vMunicipality <- "Magsaysay"

vMAG2_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vMAG2_POTypes_tmp <- vMAG2_POTypes_tmp %>%
  filter(vMAG2_POTypes_tmp$Region == vRegion, 
         vMAG2_POTypes_tmp$Province == vProvince,
         vMAG2_POTypes_tmp$SettlementArea == vSettlementArea,
         vMAG2_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vMAG2_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Magsaysay in Davao del Sur with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	19	21	65	93	0	0	0	0
Farmers Association	FA	5	241	61	195	193	0	0	0	0
Water Users Association	WUA	2	12	26	286	108	134	52	218	89
Total:		9	272	108	546	394	134	52	218	89

#Matanao
# Make dataframe
vMunicipality <-  "Matanao"

vMAT_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vMAT_POTypes_tmp <- vMAT_POTypes_tmp %>%
  filter(vMAT_POTypes_tmp$Region == vRegion, 
         vMAT_POTypes_tmp$Province == vProvince,
         vMAT_POTypes_tmp$SettlementArea == vSettlementArea,
         vMAT_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vMAT_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Matanao in Davao del Sur with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	40	82	48	36	0	0	0	0
Farmers Association	FA	22	764	210	312	125	402	148	80	16
Water Users Association	WUA	2	12	124	20	24	14	32	0	0
Total:		25	816	416	380	185	416	180	80	16

To display a chart illustrating the records of B’laan Valley Settlement Area in Davao del Sur, Region XI along with the corresponding number of POs using the ggplot command in R.

vBLASA_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBLASA_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBLASA_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in North Cotabato, Region XII with SP Types table within the database, you can follow the command below:

vRegion <- "Region XII"
vProvince <- "North Cotabato"
vSettlementArea <- "North Cotabato Settlement Area 1"
vMunicipality <-  "Banisilan"

vBAN_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vBAN_MUN_SPTypes_tmp <- vBAN_MUN_SPTypes_tmp %>%
  filter(vBAN_MUN_SPTypes_tmp$Region == vRegion, 
         vBAN_MUN_SPTypes_tmp$Province == vProvince,
         vBAN_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vBAN_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vBAN_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Banisilan North Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	5	595	576.4172
Agro-forestry	AGRO	Has	1	10	57.2500
Bridge	BRDG	Lms	1	0	24.0000
Crop Intensification	CI	Has	3	NA	1000.0000
Farm to Market Road	FMR	Kms	2	0	7.4900
Irrigation	IRRIG	Has	2	0	284.3300
Post Harvest Facilities	PHF	Units	4	NA	4.0000
Total:			18	NA	1953.4872

vMunicipality <- "Carmen"

vCAR_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vCAR_MUN_SPTypes_tmp <- vCAR_MUN_SPTypes_tmp %>%
  filter(vCAR_MUN_SPTypes_tmp$Region == vRegion, 
         vCAR_MUN_SPTypes_tmp$Province == vProvince,
         vCAR_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vCAR_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vCAR_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Carmen North Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	304	294.380
Agro-forestry	AGRO	Has	2	50	270.570
Farm to Market Road	FMR	Kms	2	NA	27.692
Post Harvest Facilities	PHF	Units	1	NA	1.000
Rural Water Systems	RWS	HHs	1	0	196.000
Total:			8	NA	789.642

To display a chart illustrating the records of Municipalities in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vNC1_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vNC1_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNC1_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the North Cotabato Settlement Area 1 in North Cotabato, Region XII table within the database, you can follow the command below:

#Banisilan
# Make dataframe
vMunicipality <-  "Banisilan"

vBAN_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vBAN_POTypes_tmp <- vBAN_POTypes_tmp %>%
  filter(vBAN_POTypes_tmp$Region == vRegion, 
         vBAN_POTypes_tmp$Province == vProvince,
         vBAN_POTypes_tmp$SettlementArea == vSettlementArea,
         vBAN_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vBAN_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Banisilan in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	5	130	134	96	258	0	0	226	392
Farmers Association	FA	29	684	785	22	20	2	0	677	786
Irrigators Association	IA	3	47	9	0	0	0	0	47	9
Womens Organization	WO	6	0	190	0	16	0	0	0	206
Total:		43	861	1118	118	294	2	0	950	1393

#Carmen
# Make dataframe
vMunicipality <-  "Carmen"

vCAR_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vCAR_POTypes_tmp <- vCAR_POTypes_tmp %>%
  filter(vCAR_POTypes_tmp$Region == vRegion, 
         vCAR_POTypes_tmp$Province == vProvince,
         vCAR_POTypes_tmp$SettlementArea == vSettlementArea,
         vCAR_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vCAR_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Carmen in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Farmers Association	FA	8	310	149	72	48	382	197	0	0
Water Users Association	WUA	1	2	0	93	41	95	41	0	0
Total:		9	312	149	165	89	477	238	0	0

To display a chart illustrating the records of North Cotabato Settlement Area 1 in North Cotabato, Region XII along with the corresponding number of POs using the ggplot command in R.

vNCSA1_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vNCSA1_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 10), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNCSA1_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in North Cotabato, Region XII with SP Types table within the database, you can follow the command below:

vRegion <- "Region XII"
vProvince <- "North Cotabato"
vSettlementArea <- "North Cotabato Settlement Area 2"
vMunicipality <-  "Alamada"

vALA_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vALA_MUN_SPTypes_tmp <- vALA_MUN_SPTypes_tmp %>%
  filter(vALA_MUN_SPTypes_tmp$Region == vRegion, 
         vALA_MUN_SPTypes_tmp$Province == vProvince,
         vALA_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vALA_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vALA_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Alamada North Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	5	2654	2570.038
Agro-forestry	AGRO	Has	1	4	20.440
Bridge	BRDG	Lms	1	0	30.000
Crop Intensification	CI	Has	1	50	50.000
Farm to Market Road	FMR	Kms	4	0	17.890
Irrigation	IRRIG	Has	2	0	166.000
Post Harvest Facilities	PHF	Units	2	NA	3.000
Rural Water Systems	RWS	HHs	1	0	150.000
Total:			17	NA	3007.368

vMunicipality <- "Libungan"

vLIB_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vLIB_MUN_SPTypes_tmp <- vLIB_MUN_SPTypes_tmp %>%
  filter(vLIB_MUN_SPTypes_tmp$Region == vRegion, 
         vLIB_MUN_SPTypes_tmp$Province == vProvince,
         vLIB_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vLIB_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vLIB_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Libungan North Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	86	83.38
Agro-forestry	AGRO	Has	2	21	110.12
Farm to Market Road	FMR	Kms	1	0	2.43
Post Harvest Facilities	PHF	Units	1	NA	2.00
Total:			6	NA	197.93

vMunicipality <- "Pigcawayan"

vPIG_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vPIG_MUN_SPTypes_tmp <- vPIG_MUN_SPTypes_tmp %>%
  filter(vPIG_MUN_SPTypes_tmp$Region == vRegion, 
         vPIG_MUN_SPTypes_tmp$Province == vProvince,
         vPIG_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vPIG_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vPIG_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Pigcawayan North Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agro-forestry	AGRO	Has	2	29	159.4648
Crop Intensification	CI	Has	1	NA	100.0000
Farm to Market Road	FMR	Kms	1	0	3.7800
Post Harvest Facilities	PHF	Units	1	NA	1.0000
Rural Water Systems	RWS	HHs	1	0	150.0000
Total:			6	NA	414.2448

To display a chart illustrating the records of Municipalities in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vNC2_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vNC2_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNC2_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the North Cotabato Settlement Area 2 in North Cotabato, Region XII table within the database, you can follow the command below:

#Alamada
# Make dataframe
vMunicipality <-  "Alamada"

vALA_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vALA_POTypes_tmp <- vALA_POTypes_tmp %>%
  filter(vALA_POTypes_tmp$Region == vRegion, 
         vALA_POTypes_tmp$Province == vProvince,
         vALA_POTypes_tmp$SettlementArea == vSettlementArea,
         vALA_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vALA_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Alamada in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	651	973	632	967	0	0	1283	1940
Farmers Association	FA	36	314	200	5	698	0	0	318	216
Irrigators Association	IA	2	76	42	0	0	0	0	76	42
Water Users Association	WUA	1	29	31	0	0	0	0	29	31
Total:		42	1070	1246	637	1665	0	0	1706	2229

#Libungan
# Make dataframe
vMunicipality <-  "Libungan"

vLIB_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vLIB_POTypes_tmp <- vLIB_POTypes_tmp %>%
  filter(vLIB_POTypes_tmp$Region == vRegion, 
         vLIB_POTypes_tmp$Province == vProvince,
         vLIB_POTypes_tmp$SettlementArea == vSettlementArea,
         vLIB_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vLIB_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Libungan in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Farmers Association	FA	7	146	186	26	19	0	0	146	146
Total:		7	146	186	26	19	0	0	146	146

#Pigcawayan
# Make dataframe
vMunicipality <-  "Pigcawayan"

vPIG_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vPIG_POTypes_tmp <- vPIG_POTypes_tmp %>%
  filter(vPIG_POTypes_tmp$Region == vRegion, 
         vPIG_POTypes_tmp$Province == vProvince,
         vPIG_POTypes_tmp$SettlementArea == vSettlementArea,
         vPIG_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vPIG_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Pigcawayan in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Farmers Association	FA	7	109	117	15	7	4	3	101	113
Water Users Association	WUA	1	95	55	0	0	0	0	95	55
Total:		8	204	172	15	7	4	3	196	168

To display a chart illustrating the records of North Cotabato Settlement Area 2 in North Cotabato, Region XII along with the corresponding number of POs using the ggplot command in R.

vNCSA2_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vNCSA2_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 50, by = 10), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNCSA2_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in North Cotabato, Region XII with SP Types table within the database, you can follow the command below:

vRegion <- "Region XII"
vProvince <- "North Cotabato"
vSettlementArea <- "Sultan Kudarat Settlement Area 1 Phase 2"
vMunicipality <-  "Makilala"

vMAK_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vMAK_MUN_SPTypes_tmp <- vMAK_MUN_SPTypes_tmp %>%
  filter(vMAK_MUN_SPTypes_tmp$Region == vRegion, 
         vMAK_MUN_SPTypes_tmp$Province == vProvince,
         vMAK_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vMAK_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vMAK_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Makilala North Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	310	300.4082
Farm to Market Road	FMR	Kms	1	0	12.4200
Post Harvest Facilities	PHF	Units	2	NA	3.0000
Rural Water Systems	RWS	HHs	2	0	655.0000
Total:			7	NA	970.8282

vMunicipality <- "Tulunan"

vTUL_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vTUL_MUN_SPTypes_tmp <- vTUL_MUN_SPTypes_tmp %>%
  filter(vTUL_MUN_SPTypes_tmp$Region == vRegion, 
         vTUL_MUN_SPTypes_tmp$Province == vProvince,
         vTUL_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vTUL_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vTUL_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Tulunan North Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	733	709.9397
Agro-forestry	AGRO	Has	1	89	484.5400
Crop Intensification	CI	Has	1	NA	257.0000
Farm to Market Road	FMR	Kms	2	0	8.6200
Post Harvest Facilities	PHF	Units	3	NA	4.0000
Rural Water Systems	RWS	HHs	1	0	620.0000
Total:			10	NA	2084.0997

To display a chart illustrating the records of Municipalities in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vNC3_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vNC3_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNC3_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Sultan Kudarat Settlement Area 1 Phase 2 in North Cotabato, Region XII table within the database, you can follow the command below:

#Makilala
# Make dataframe
vMunicipality <-  "Makilala"

vMAK_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vMAK_POTypes_tmp <- vMAK_POTypes_tmp %>%
  filter(vMAK_POTypes_tmp$Region == vRegion, 
         vMAK_POTypes_tmp$Province == vProvince,
         vMAK_POTypes_tmp$SettlementArea == vSettlementArea,
         vMAK_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vMAK_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Makilala in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	2	67	54	12	42	0	0	79	96
Farmers Association	FA	13	196	197	1	1	0	0	197	198
Water Users Association	WUA	5	226	115	0	0	0	0	226	115
Womens Organization	WO	4	0	132	0	0	0	63	0	195
Total:		24	489	498	13	43	0	63	502	604

#Tulunan
# Make dataframe
vMunicipality <-  "Tulunan"

vTUL_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vTUL_POTypes_tmp <- vTUL_POTypes_tmp %>%
  filter(vTUL_POTypes_tmp$Region == vRegion, 
         vTUL_POTypes_tmp$Province == vProvince,
         vTUL_POTypes_tmp$SettlementArea == vSettlementArea,
         vTUL_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vTUL_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Tulunan in North Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	75	45	0	0	0	0	75	45
Farmers Association	FA	18	375	259	31	7	0	0	406	266
Irrigators Association	IA	1	21	10	0	0	0	0	21	10
Water Users Association	WUA	1	5	2	16	8	0	0	21	10
Womens Organization	WO	2	0	60	0	0	0	0	0	60
Total:		23	476	376	47	15	0	0	523	391

To display a chart illustrating the records of Sultan Kudarat Settlement Area 1 Phase 2 in North Cotabato, Region XII along with the corresponding number of POs using the ggplot command in R.

vNCSA1_2_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vNCSA1_2_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 5), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNCSA1_2_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in South Cotabato, Region XII with SP Types table within the database, you can follow the command below:

vRegion <- "Region XII"
vProvince <- "South Cotabato"
vSettlementArea <- "Ned Settlement Area"
vMunicipality <-  "Lake Sebu"

vLAKE_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vLAKE_MUN_SPTypes_tmp <- vLAKE_MUN_SPTypes_tmp %>%
  filter(vLAKE_MUN_SPTypes_tmp$Region == vRegion, 
         vLAKE_MUN_SPTypes_tmp$Province == vProvince,
         vLAKE_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vLAKE_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vLAKE_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Lake Sebu South Cotabato with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	4	349	337.8100
Agro-forestry	AGRO	Has	2	78	425.2482
Bridge	BRDG	Lms	2	0	27.0000
Crop Intensification	CI	Has	1	NA	810.0000
Farm to Market Road	FMR	Kms	4	0	22.2900
Irrigation	IRRIG	Has	3	0	106.0000
Post Harvest Facilities	PHF	Units	6	NA	8.0000
Rural Water Systems	RWS	HHs	2	0	210.0000
Total:			24	NA	1946.3482

To display a chart illustrating the records of Municipalities in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vNED_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vNED_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNED_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Ned Settlement Area in South Cotabato, Region XII table within the database, you can follow the command below:

#Lake Sebu
# Make dataframe
vMunicipality <-  "Lake Sebu"

vLAKE_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vLAKE_POTypes_tmp <- vLAKE_POTypes_tmp %>%
  filter(vLAKE_POTypes_tmp$Region == vRegion, 
         vLAKE_POTypes_tmp$Province == vProvince,
         vLAKE_POTypes_tmp$SettlementArea == vSettlementArea,
         vLAKE_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vLAKE_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Lake Sebu in South Cotabato with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	6	292	218	109	72	12	0	0	0
Irrigators Association	IA	3	0	0	0	0	0	0	0	0
Water Users Association	WUA	2	31	25	20	29	3	1	3	1
Total:		11	323	243	129	101	15	1	3	1

To display a chart illustrating the records of Ned Settlement Area in South Cotabato, Region XII along with the corresponding number of POs using the ggplot command in R.

vNEDSA_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vNEDSA_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vNEDSA_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Sultan Kudarat, Region XII with SP Types table within the database, you can follow the command below:

vRegion <- "Region XII"
vProvince <- "Sultan Kudarat"
vSettlementArea <- "Sultan Kudarat Settlement Area 1"
vMunicipality <-  "Columbio"

vCOL_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vCOL_MUN_SPTypes_tmp <- vCOL_MUN_SPTypes_tmp %>%
  filter(vCOL_MUN_SPTypes_tmp$Region == vRegion, 
         vCOL_MUN_SPTypes_tmp$Province == vProvince,
         vCOL_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vCOL_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vCOL_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Columbio Sultan Kudarat with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	2	442	428.9653
Agro-forestry	AGRO	Has	2	74	408.8965
Crop Intensification	CI	Has	2	150	300.0000
Farm to Market Road	FMR	Kms	2	0	6.6800
Irrigation	IRRIG	Has	3	0	657.0000
Post Harvest Facilities	PHF	Units	1	NA	2.0000
Rural Water Systems	RWS	HHs	1	0	659.0000
Total:			13	NA	2462.5418

To display a chart illustrating the records of Municipalities in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vSK1_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vSK1_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vSK1_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Sultan Kudarat Settlement Area 1 in Sultan Kudarat, Region XII table within the database, you can follow the command below:

#Columbio
# Make dataframe
vMunicipality <-  "Columbio"

vCOL_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vCOL_POTypes_tmp <- vCOL_POTypes_tmp %>%
  filter(vCOL_POTypes_tmp$Region == vRegion, 
         vCOL_POTypes_tmp$Province == vProvince,
         vCOL_POTypes_tmp$SettlementArea == vSettlementArea,
         vCOL_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vCOL_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Columbio in Sultan Kudarat with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	275	331	0	0	0	0	0	0
Farmers Association	FA	13	98	37	43	0	9	0	0	0
Irrigators Association	IA	4	187	36	0	0	0	0	0	0
Water Users Association	WUA	1	72	57	0	0	0	0	0	0
Total:		19	632	461	43	0	9	0	0	0

To display a chart illustrating the records of Sultan Kudarat Settlement Area 1 in Sultan Kudarat, Region XII along with the corresponding number of POs using the ggplot command in R.

vSKSA1_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSKSA1_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 2), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vSKSA1_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To view the Settlement Areas in Sultan Kudarat, Region XII with SP Types table within the database, you can follow the command below:

vRegion <- "Region XII"
vProvince <- "Sultan Kudarat"
vSettlementArea <- "Sultan Kudarat Settlement Area 2"
vMunicipality <-  "Bagumbayan"

vBAG_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vBAG_MUN_SPTypes_tmp <- vBAG_MUN_SPTypes_tmp %>%
  filter(vBAG_MUN_SPTypes_tmp$Region == vRegion, 
         vBAG_MUN_SPTypes_tmp$Province == vProvince,
         vBAG_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vBAG_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vBAG_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Bagumbayan Sultan Kudarat with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	1	277	268.1033
Agro-forestry	AGRO	Has	1	26	143.1100
Crop Intensification	CI	Has	2	NA	786.0000
Farm to Market Road	FMR	Kms	2	0	13.0500
Post Harvest Facilities	PHF	Units	3	NA	4.0000
Total:			9	NA	1214.2633

vMunicipality <-  "Palimbang"

vPAL_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vPAL_MUN_SPTypes_tmp <- vPAL_MUN_SPTypes_tmp %>%
  filter(vPAL_MUN_SPTypes_tmp$Region == vRegion, 
         vPAL_MUN_SPTypes_tmp$Province == vProvince,
         vPAL_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vPAL_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vPAL_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Palimbang Sultan Kudarat with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	3	580	563.0170
Agro-forestry	AGRO	Has	1	37	204.4482
Bridge	BRDG	Lms	1	0	60.0000
Crop Intensification	CI	Has	2	NA	250.0000
Farm to Market Road	FMR	Kms	1	0	9.8000
Post Harvest Facilities	PHF	Units	6	NA	7.0000
Rural Water Systems	RWS	HHs	2	NA	641.0000
Total:			16	NA	1735.2652

vMunicipality <-  "Senator Ninoy Aquino"

vSNA_MUN_SPTypes_tmp <- vLocations_SPTypes %>%
  select(-Barangay)

vSNA_MUN_SPTypes_tmp <- vSNA_MUN_SPTypes_tmp %>%
  filter(vSNA_MUN_SPTypes_tmp$Region == vRegion, 
         vSNA_MUN_SPTypes_tmp$Province == vProvince,
         vSNA_MUN_SPTypes_tmp$SettlementArea == vSettlementArea,
         vSNA_MUN_SPTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(SPType, Code, UnitMeasure)  %>%
  summarise(SPs = sum(SPs), 
            Beneficiaries = sum(Beneficiaries), Qty = sum(Qty),
            .groups = 'drop')

results_df <- data.frame(vSNA_MUN_SPTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("SP Type", "Code", 
                       "UnitMeasure", "SPs", 
                       "Beneficiaries", "Qty", 
                       stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, vProvince, "with SP Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first colum

Senator Ninoy Aquino Sultan Kudarat with SP Types table

SP Type	Code	UnitMeasure	SPs	Beneficiaries	Qty
Agribusiness	AGBiz	Has	1	277	268.1033
Agro-forestry	AGRO	Has	2	74	408.0787
Crop Intensification	CI	Has	3	NA	424.0000
Farm to Market Road	FMR	Kms	1	0	7.4800
Irrigation	IRRIG	Has	1	0	36.0000
Post Harvest Facilities	PHF	Units	5	NA	6.0000
Total:			13	NA	1149.6620

To display a chart illustrating the records of Municipalities in Davao del Sur, Region XI along with the corresponding number of SPs using the ggplot command in R.

vSK2_SA_SPTypes_tmp <- vLocations_SPTypes %>%
  filter(vLocations_SPTypes$Region == vRegion, 
         vLocations_SPTypes$Province == vProvince,
         vLocations_SPTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, SPType, Code)  %>%
  summarise(SPs = sum(SPs), .groups = 'drop')

ggplot(vSK2_SA_SPTypes_tmp, aes(x = Code, y = as.integer(SPs), fill=SPType)) +
  geom_col() +
  geom_text(aes(label = as.integer(SPs), y= as.integer(SPs) / 2)) + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#AABB22", "#EEFF77",
                               "#BACCC1", "#3C8D53", 
                               "#BE2A3E", "#3388DD", 
                               "#CC11BB", "#EC754A", 
                               "#AAFF23")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vSK2_SA_SPTypes_tmp) + 
  xlab("SP Types") + ylab("Number of SPs")  +
  labs(title= paste("Municipalities in", vProvince, vRegion, "and SP Types w/ No. of SPs"),  
       caption="Data Collected by: Junald A. Lagod")

To view the Sultan Kudarat Settlement Area 2 in Sultan Kudarat, Region XII table within the database, you can follow the command below:

#Bagumbayan
# Make dataframe
vMunicipality <-  "Bagumbayan"

vBAG_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vBAG_POTypes_tmp <- vBAG_POTypes_tmp %>%
  filter(vBAG_POTypes_tmp$Region == vRegion, 
         vBAG_POTypes_tmp$Province == vProvince,
         vBAG_POTypes_tmp$SettlementArea == vSettlementArea,
         vBAG_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vBAG_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Bagumbayan in Sultan Kudarat with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	77	62	11	10	0	0	0	0
Farmers Association	FA	11	252	167	130	59	2	1	0	0
Total:		12	329	229	141	69	2	1	0	0

#Palimbang
# Make dataframe
vMunicipality <-  "Palimbang"

vPAL_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vPAL_POTypes_tmp <- vPAL_POTypes_tmp %>%
  filter(vPAL_POTypes_tmp$Region == vRegion, 
         vPAL_POTypes_tmp$Province == vProvince,
         vPAL_POTypes_tmp$SettlementArea == vSettlementArea,
         vPAL_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vPAL_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Palimbang in Sultan Kudarat with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	1	31	15	0	0	0	0	0	0
Farmers Association	FA	4	14	2	27	19	0	0	0	0
Water Users Association	WUA	2	0	0	0	0	0	0	0	0
Womens Organization	WO	2	0	0	0	0	0	0	0	0
Total:		9	45	17	27	19	0	0	0	0

#Senator Ninoy Aquino
# Make dataframe
vMunicipality <-  "Senator Ninoy Aquino"

vSNA_POTypes_tmp <- vLocations_POTypes %>%
  select( -Barangay) %>%
  drop_na()

vSNA_POTypes_tmp <- vSNA_POTypes_tmp %>%
  filter(vSNA_POTypes_tmp$Region == vRegion, 
         vSNA_POTypes_tmp$Province == vProvince,
         vSNA_POTypes_tmp$SettlementArea == vSettlementArea,
         vSNA_POTypes_tmp$Municipality == vMunicipality)  %>%
  group_by(POType, Code)  %>%
  summarise(POs = sum(POs), ARBMale = sum(ARBMale), ARBFemale = sum(ARBFemale),
            NonARBMale = sum(NonARBMale), NonARBFemale = sum(NonARBFemale), 
            IPMale = sum(IPMale), IPFemale = sum(IPFemale),
            NonIPMale = sum(NonIPMale), NonIPFemale = sum(NonIPFemale), 
            .groups = 'drop')

results_df <- data.frame(vSNA_POTypes_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("PO Type", "Code", "POs", "ARB Male", "ARB Female", 
                       "Non-ARB Male", "Non-ARB Female", "IP Male", "IP Female", 
                       "Non-IP Male", "Non-IP Female", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = paste(vMunicipality, "in", vProvince, "with PO Types table"), booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Senator Ninoy Aquino in Sultan Kudarat with PO Types table

PO Type	Code	POs	ARB Male	ARB Female	Non-ARB Male	Non-ARB Female	IP Male	IP Female	Non-IP Male	Non-IP Female
Cooperative	COOP	3	200	120	0	0	0	0	0	0
Farmers Association	FA	12	724	202	17	0	0	0	299	14
Irrigators Association	IA	1	22	6	0	0	0	0	0	0
Womens Organization	WO	6	0	448	0	116	0	0	0	0
Total:		22	946	776	17	116	0	0	299	14

To display a chart illustrating the records of Sultan Kudarat Settlement Area 2 in Sultan Kudarat, Region XII along with the corresponding number of POs using the ggplot command in R.

vSKSA2_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vRegion, 
         vLocations_POTypes$Province == vProvince,
         vLocations_POTypes$SettlementArea == vSettlementArea)  %>%
  group_by(Municipality, POType, Code)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vSKSA2_POTypes_tmp, aes(x = Code, y = as.integer(POs), fill=POType)) +
  geom_col() +
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(~Municipality, scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vSKSA2_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste(vSettlementArea, "in", vProvince, "with PO Types table"), 
       caption="Data Collected by: Junald A. Lagod")

To retrieve a list of covered Municipalities records along with the corresponding number of ARCs and Barangays

To view the Municipalities table within the database, you can follow the command below:

vLocations_ARCs_tmp <- vLocations_ARCs %>%
  select(-ARC) %>%
  group_by(Region, Province, Settlement, Municipality, MunCode)%>%
  summarise(ARCs = sum(ARCs), .groups = 'drop')

vLocations_BRGYS_tmp <- vLocations_BRGYS %>%
  select(-Barangay) %>%
  group_by(Region, Province, Settlement, Municipality, MunCode)%>%
  summarise(Barangays = sum(Barangays), .groups = 'drop')


vMunicipalities <- merge(vLocations_ARCs_tmp, vLocations_BRGYS_tmp)
vMunicipalities_tmp <- vMunicipalities

# Make dataframe
results_df <- data.frame(vMunicipalities_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Region", "Province", "Settlement", "Municipality", "Code", "No. of ARCs", "No. of Barangays", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Covered Municipalities table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Covered Municipalities table

Region	Province	Settlement	Municipality	Code	No. of ARCs	No. of Barangays
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	2	7
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	1	8
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	1	24
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Maigo	MAI	1	4
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Munai	MUN	0	6
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	1	8
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	1	18
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tubod	TUB	1	3
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	Sapad	SAP	1	6
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	1	25
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Sultan Naga Dimaporo	SND	1	3
Region XI	Davao de Oro	Karagan Valley Settlement Area	Maragusan	MAR	0	3
Region XI	Davao de Oro	Karagan Valley Settlement Area	New Bataan	NEW	0	1
Region XI	Davao del Sur	B’laan Settlement Area	Magsaysay	MAG2	0	6
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	0	13
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	2	20
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	1	8
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	1	19
Region XII	North Cotabato	North Cotabato Settlement Area 2	Libungan	LIB	0	6
Region XII	North Cotabato	North Cotabato Settlement Area 2	Pigcawayan	PIG	0	5
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	1	13
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	1	15
Region XII	South Cotabato	Ned Settlement Area	Lake Sebu	LAKE	0	1
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	0	18
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	0	7
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Palimbang	PAL	0	3
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	0	20
Total:					17	270

To display a chart illustrating the records of Municipalities along with the corresponding number of ARCs and Barangays using the ggplot command in R.

vLocations_ARCs_tmp <- vLocations_ARCs_tmp %>%
  mutate(Location='ARCs')
vLocations_BRGYS_tmp <- vLocations_BRGYS_tmp %>%
  mutate(Location='Barangays')

vMunicipalities_initial <- data.frame(vLocations_ARCs_tmp)
vMunicipalities <- vMunicipalities_initial
vMunicipalities[(nrow(vMunicipalities) + 1):(nrow(vMunicipalities) + 
                                               nrow(vLocations_BRGYS_tmp)),] <- vLocations_BRGYS_tmp

ggplot(data=vMunicipalities, aes(x=Municipality, y=as.integer(ARCs), fill=factor(Location))) +
  geom_bar(position="dodge",stat="identity") + 
  coord_flip() +
  scale_y_continuous(
    breaks = seq(0, 30, by = 2), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="Covered Areas")) +
  xlab("Municipalities") + ylab("Number of ARCs and Barangays") + 
  labs(title= "Covered Municipalities with Number of ARCs and Barangays", 
       caption="Data Collected by: Junald A. Lagod")  

To retrieve a list of covered Barangays records along with the corresponding number of Sub-Projects

To view the Barangays table within the database, you can follow the command below:

vLocBarangays_tmp <- vLocations_BRGYS %>%
  select(-RegCode, -ProvCode, -SACode, -Barangays)
  
# Make dataframe
results_df <- data.frame(vLocBarangays_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Region", "Province", "Settlement", "Municipality", "Code", "Barangay Name", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Covered Barangays table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Covered Barangays table

Region	Province	Settlement	Municipality	Code	Barangay Name
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	Pinamangguan
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	Matampay
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	Mabuhay
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	Kibalagon
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	Cabadiangan
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	Balaoro
Region X	Bukidnon	Kadingilan Settlement Area	Kadingilan	KAD	Bagongbayan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Sucodan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Small Banisilon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Pantaon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Palao
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Lumbac
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Inudaran
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Caromatan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Kolambugan	KOL	Bubong
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Upper Caningag (Taguitingan)
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Tombador
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Tipaan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Tawinian
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Tambacon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Talambo
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Somiorang
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Rarab
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Poblacion (Bago-A-Ingud)
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Pelingkingan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Pangao
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Olango
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Mapantao
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Malabaogan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Lumbac
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Lubo
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Lower Caningag (Perimbangan)
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Lemoncret
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Lamigadato
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Ilihan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Durianon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Daan Campo
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Baguiguicon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Magsaysay	MAG1	Babasalon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Maigo	MAI	Poblacion
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Maigo	MAI	Mentring
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Maigo	MAI	Maliwanag
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Maigo	MAI	Inoma
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Munai	MUN	Cadayonan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Munai	MUN	Tambo
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Munai	MUN	Pantao-Munai
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Munai	MUN	Pantao
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Munai	MUN	Maganding
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Munai	MUN	Madaya
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Poblacion
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Tangcal
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Pantao Marug
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Pansor
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Matampay
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Culubun
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Cabasagan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Pantao Ragat	PAN	Banday
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Tangcal Proper
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Somiorang
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Small Meladoc
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Small Banisilon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Punod
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Poona Kapatagan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Poblacion
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Pelingkingan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Papan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Lingco-an
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Lindongan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Linao
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Lamaosa
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Bubong
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Big Meladoc
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Big Banisilon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Berwar
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tangcal	TAN	Bayabao
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tubod	TUB	Palao
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tubod	TUB	Dalama
Region X	Lanao del Norte	Lanao del Norte Settlement Area 1	Tubod	TUB	Bualan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	Sapad	SAP	Poblacion
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	Sapad	SAP	Pili
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	Sapad	SAP	Lower Sapad
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	Sapad	SAP	Gamal
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	Sapad	SAP	Dansalan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 2	Sapad	SAP	Baning
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Taraka
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Songgod
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Rebucon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Raraban
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Rarab
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Poblacion
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Petadun
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Paride
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Pantar
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Panganapan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Notongan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Masibay
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Mangan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Malaig
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Lupitan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Liangan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Katubuan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Kaludan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Inayawan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Dimayon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Carcum
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Canibongan
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Cabasaran (Laya)
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Bangko
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Nunungan	NUN	Abaga
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Sultan Naga Dimaporo	SND	Rebucon
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Sultan Naga Dimaporo	SND	Dalama
Region X	Lanao del Norte	Lanao del Norte Settlement Area 3	Sultan Naga Dimaporo	SND	Bangco
Region XI	Davao de Oro	Karagan Valley Settlement Area	Maragusan	MAR	Cheng Shihan
Region XI	Davao de Oro	Karagan Valley Settlement Area	Maragusan	MAR	Langgawisan
Region XI	Davao de Oro	Karagan Valley Settlement Area	Maragusan	MAR	Bahi
Region XI	Davao de Oro	Karagan Valley Settlement Area	New Bataan	NEW	Andap
Region XI	Davao del Sur	B’laan Settlement Area	Magsaysay	MAG2	Tagaytay
Region XI	Davao del Sur	B’laan Settlement Area	Magsaysay	MAG2	San Miguel
Region XI	Davao del Sur	B’laan Settlement Area	Magsaysay	MAG2	Malawanit
Region XI	Davao del Sur	B’laan Settlement Area	Magsaysay	MAG2	Glamang
Region XI	Davao del Sur	B’laan Settlement Area	Magsaysay	MAG2	Balnate
Region XI	Davao del Sur	B’laan Settlement Area	Magsaysay	MAG2	Bacungan
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Towak
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Tamlangon
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Saub
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Saboy
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	New Katipunan
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Manga
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Kapok
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Dongan-Pekong
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Colonsabak
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Cabasagan
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Bangkal
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Asinan
Region XI	Davao del Sur	B’laan Settlement Area	Matanao	MAT	Asbang
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Wadya
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Tumbao-Camalig
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Tinimbakan
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Thailand
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Salama
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Puting-bato
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Poblacion 2
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Poblacion 1
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Pinamulaan
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Paradise
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Pantar
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Miguel Macasarte
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Malinao
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Malagap
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Kiaring
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Kalawaig
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Gastav
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Carugmanan
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Capayangan
Region XII	North Cotabato	North Cotabato Settlement Area 1	Banisilan	BAN	Busaon
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Ma Kwok Ming
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Tambad
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Palanggalan
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Malapag
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Macabenban
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Liliongan
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Cadiis
Region XII	North Cotabato	North Cotabato Settlement Area 1	Carmen	CAR	Bentangan
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Lu Xiaoming
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Laura Scott
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Upper Dado
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Raradangan
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Rangayen
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Polayagan
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Pigcawaran
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Paruayan
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Pacao
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Mirasol
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Mapurok
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Malitubog
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Macabasa
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Lower Dado
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Kitacubong
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Guiling
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Camansi
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Barangiran
Region XII	North Cotabato	North Cotabato Settlement Area 2	Alamada	ALA	Bao
Region XII	North Cotabato	North Cotabato Settlement Area 2	Libungan	LIB	Nicholas Ortiz
Region XII	North Cotabato	North Cotabato Settlement Area 2	Libungan	LIB	Sinapangan
Region XII	North Cotabato	North Cotabato Settlement Area 2	Libungan	LIB	Palao
Region XII	North Cotabato	North Cotabato Settlement Area 2	Libungan	LIB	Kitubod
Region XII	North Cotabato	North Cotabato Settlement Area 2	Libungan	LIB	Kiloyao
Region XII	North Cotabato	North Cotabato Settlement Area 2	Libungan	LIB	Cabpangi
Region XII	North Cotabato	North Cotabato Settlement Area 2	Pigcawayan	PIG	Renibon
Region XII	North Cotabato	North Cotabato Settlement Area 2	Pigcawayan	PIG	Payong-Payong
Region XII	North Cotabato	North Cotabato Settlement Area 2	Pigcawayan	PIG	Midpapan 2
Region XII	North Cotabato	North Cotabato Settlement Area 2	Pigcawayan	PIG	Kimarayang
Region XII	North Cotabato	North Cotabato Settlement Area 2	Pigcawayan	PIG	Anick
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Taluntalunan
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Villaflores
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Sto. Nino
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Sta. Felomina
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Rodero
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	New Baguio
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Malungon
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Malabuan
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Luayon
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Kawayanon
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Guangan
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Cabilao
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Makilala	MAK	Bato
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Tuburan
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Paraiso
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	New Caridad
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	New Bunawan
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Nabundasan
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Maybula
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Magbok
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Lampagang
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Kanibong
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	G. Baynosa
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Daig
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Bituan
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Batang
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Banayal
Region XII	North Cotabato	Sultan Kudarat Settlement Area 1 Phase 2	Tulunan	TUL	Bacong
Region XII	South Cotabato	Ned Settlement Area	Lake Sebu	LAKE	Ned
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Wada Hikaru
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Anthony Harrison
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Telafas
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Sucob
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Sinapulan
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Polomolok
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Poblacion
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Natividad
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Mayo
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Maligaya
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Makat
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Lomoyon
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Libertad
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Lasak
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Eday
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Datalblao
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Bunawan
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 1	Columbio	COL	Bantangan
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	Sumilil
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	Santo Nino
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	Monteverde
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	Masiag
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	Kanulay
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	Kabulanan
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Bagumbayan	BAG	Daluga
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Palimbang	PAL	Molon
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Palimbang	PAL	Kalibuhan
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Palimbang	PAL	Baluan
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Tinalon
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Tacupis
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Sewod
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Nati
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Midtungok
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Malegdeg
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Limuhay
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Langgal
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Lagubang
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Kulaman
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Kuden
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Kiadsam
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Kapatagan
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Kadi
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Gapok
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Buklod
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Bugso
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Buenaflores
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Basag
Region XII	Sultan Kudarat	Sultan Kudarat Settlement Area 2	Senator Ninoy Aquino	SNA	Banali
Total:

To display a chart illustrating the records of Barangays in Kadingilan along with the corresponding number of POs using the ggplot command in R.

#Kadingilan
# Make dataframe
vMUN_Region <-  "Region X"
vMUN_Province <-  "Bukidnon"
vMUN_Settlement <-  "Kadingilan Settlement Area"
vMUN_Municipality <-  "Kadingilan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Lanao del Norte Settlement Area 1 along with the corresponding number of POs using the ggplot command in R.

#Kolambugan
vMUN_Region <-  "Region X"
vMUN_Province <-  "Lanao del Norte"
vMUN_Settlement <-  "Lanao del Norte Settlement Area 1"
vMUN_Municipality <-  "Magsaysay"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Kolambugan
vMUN_Municipality <-  "Kolambugan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Magsaysay
vMUN_Municipality <-  "Magsaysay"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Maigo
vMUN_Municipality <-  "Maigo"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Munai
vMUN_Municipality <-  "Munai"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Pantao Ragat
vMUN_Municipality <-  "Pantao Ragat"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Tangcal
vMUN_Municipality <-  "Tangcal"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Tubod
vMUN_Municipality <-  "Tubod"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Lanao del Norte Settlement Area 2 along with the corresponding number of POs using the ggplot command in R.

#Sapad
vMUN_Region <-  "Region X"
vMUN_Province <-  "Lanao del Norte"
vMUN_Settlement <-  "Lanao del Norte Settlement Area 2"
vMUN_Municipality <-  "Sapad"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Lanao del Norte Settlement Area 3 along with the corresponding number of POs using the ggplot command in R.

#Nunungan
vMUN_Region <-  "Region X"
vMUN_Province <-  "Lanao del Norte"
vMUN_Settlement <-  "Lanao del Norte Settlement Area 3"
vMUN_Municipality <-  "Nunungan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Sultan Naga Dimaporo
vMUN_Municipality <-  "Sultan Naga Dimaporo"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Karagan Valley Settlement Area along with the corresponding number of POs using the ggplot command in R.

#Maragusan
vMUN_Region <-  "Region XI"
vMUN_Province <-  "Davao de Oro"
vMUN_Settlement <-  "Karagan Valley Settlement Area"
vMUN_Municipality <-  "Maragusan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#New Bataan
vMUN_Municipality <-  "New Bataan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in B’laan Settlement Area along with the corresponding number of POs using the ggplot command in R.

#Magsaysay
vMUN_Region <-  "Region XI"
vMUN_Province <-  "Davao del Sur"
vMUN_Settlement <-  "B'laan Settlement Area"
vMUN_Municipality <-  "Magsaysay"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Matanao
vMUN_Municipality <-  "Matanao"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in North Cotabato Settlement Area 1 along with the corresponding number of POs using the ggplot command in R.

#Banisilan
vMUN_Region <-  "Region XII"
vMUN_Province <-  "North Cotabato"
vMUN_Settlement <-  "North Cotabato Settlement Area 1"
vMUN_Municipality <-  "Banisilan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 3) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Carmen
vMUN_Municipality <-  "Carmen"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in North Cotabato Settlement Area 2 along with the corresponding number of POs using the ggplot command in R.

#Alamada
vMUN_Region <-  "Region XII"
vMUN_Province <-  "North Cotabato"
vMUN_Settlement <-  "North Cotabato Settlement Area 2"
vMUN_Municipality <-  "Alamada"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 3) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Libungan
vMUN_Municipality <-  "Libungan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Pigcawayan
vMUN_Municipality <-  "Pigcawayan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Sultan Kudarat Settlement Area 1 Phase 2 along with the corresponding number of POs using the ggplot command in R.

#Makilala
vMUN_Region <-  "Region XII"
vMUN_Province <-  "North Cotabato"
vMUN_Settlement <-  "Sultan Kudarat Settlement Area 1 Phase 2"
vMUN_Municipality <-  "Makilala"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 2) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Tulunan
vMUN_Municipality <-  "Tulunan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 4) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Ned Settlement Area along with the corresponding number of POs using the ggplot command in R.

#Lake Sebu
vMUN_Region <-  "Region XII"
vMUN_Province <-  "South Cotabato"
vMUN_Settlement <-  "Ned Settlement Area"
vMUN_Municipality <-  "Lake Sebu"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Sultan Kudarat Settlement Area 1 along with the corresponding number of POs using the ggplot command in R.

#Columbio
vMUN_Region <-  "Region XII"
vMUN_Province <-  "Sultan Kudarat"
vMUN_Settlement <-  "Sultan Kudarat Settlement Area 1"
vMUN_Municipality <-  "Columbio"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To display a chart illustrating the records of Barangays in Sultan Kudarat Settlement Area 2 along with the corresponding number of POs using the ggplot command in R.

#Bagumbayan
vMUN_Region <-  "Region XII"
vMUN_Province <-  "Sultan Kudarat"
vMUN_Settlement <-  "Sultan Kudarat Settlement Area 2"
vMUN_Municipality <-  "Bagumbayan"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Palimbang
vMUN_Region <-  "Region XII"
vMUN_Province <-  "Sultan Kudarat"
vMUN_Settlement <-  "Sultan Kudarat Settlement Area 2"
vMUN_Municipality <-  "Palimbang"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 5) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

#Senator Ninoy Aquino
vMUN_Region <-  "Region XII"
vMUN_Province <-  "Sultan Kudarat"
vMUN_Settlement <-  "Sultan Kudarat Settlement Area 2"
vMUN_Municipality <-  "Senator Ninoy Aquino"

vBRGYS_POTypes_tmp <- vLocations_POTypes %>%
  filter(vLocations_POTypes$Region == vMUN_Region, 
         vLocations_POTypes$Province == vMUN_Province,
         vLocations_POTypes$SettlementArea == vMUN_Settlement,
         vLocations_POTypes$Municipality == vMUN_Municipality)  %>%
  group_by(Municipality, POType, Code, Barangay)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vBRGYS_POTypes_tmp, aes(x = Barangay, y = as.integer(POs), fill=factor(POType))) +
  geom_col(position="dodge") + 
  coord_flip() +  
  scale_y_continuous(
    breaks = seq(0, 30, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  geom_text(aes(label = as.integer(POs), y = as.integer(POs) / 2),
            position = position_dodge(width = 1 , preserve = "total"), size = 3) +
  guides(fill=guide_legend(title="")) + 
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("#BE2A3E", "#EC754A", "#ABC123", 
                               "#EACF65", "#3C8D53", "#FFF123", "#BACCC1")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vBRGYS_POTypes_tmp) + 
  xlab("PO Types") + ylab("Number of POs")  +
  labs(title= paste("Barangays in", vMUN_Municipality, 
                    ",", vMUN_Province, "and PO Types with No. of POs"), 
       caption="Data Collected by: Junald A. Lagod")

To retrieve a list of Major Status records along with the corresponding number of Detailed Status.

This SQL query uses a JOIN operation to combine the Major Status table with the Municipalities table based on a shared MajorStatusID_FK. It then calculates the count of ProjStatusID_PK for each Major Status using the COUNT function and aliases it as ‘Number Of Detailed Status’. The GROUP BY clause ensures that the count is calculated per Major Status. By executing this query, you will retrieve a result set that includes the Major Status records along with the corresponding number of Detailed Status for each Major Status, you can use the following SQL statement:

vMajorStatus <- dbGetQuery(con, 'SELECT
                                    projentities.EntityID_PK AS MajorStatusID, 
                                    projentities.EntityName AS MajorStatus, 
                                    projstatus.Component, 
                                    Count(projstatus.ProjStatusID_PK) AS DetailedStatus
                                FROM
                                    projentities
                                    INNER JOIN
                                    projstatus
                                    ON 
                                        projstatus.MajorStatusID_FK = projentities.EntityID_PK
                                WHERE
                                    GroupCategory = "Status Category"
                                GROUP BY
                                    projentities.EntityID_PK, 
                                    projentities.EntityID_PK, 
                                    projentities.EntityName, 
                                    projstatus.Component
                                ORDER BY
                                    projstatus.Component ASC, 
                                    projentities.EntityID_PK ASC;')

To view the Major Status table within the database, you can follow the command below:

vMajorStatus_no_ID <- vMajorStatus %>%
  select(-MajorStatusID)#To hide columns in the table

# Make dataframe
results_df <- data.frame(vMajorStatus_no_ID, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Major Status", "Component", "Detailed Status", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Major Status table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Major Status table

Major Status	Component	Detailed Status
Pre-Implementation	AAAD/INFRA	2
Actual-Implementation	AAAD/INFRA	3
Post-Implementation	AAAD/INFRA	2
Actual-Implementation	INSTI	9
Total:		16

To display a chart illustrating the records of Major Status along with the corresponding Detailed Status using the ggplot command in R.

vMajorStatus_with_ID <- vMajorStatus %>%
  select(MajorStatusID)#To show hide columns in the table

ggplot(vMajorStatus, aes(x = Component, y = as.integer(DetailedStatus), fill=Component)) +
  geom_col() +
  geom_text(aes(label = as.integer(DetailedStatus), y=as.integer(DetailedStatus)/ 2)) +
  scale_y_continuous(
    breaks = seq(0, 10, by = 1), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="Detailed Status")) + 
  scale_fill_manual(values = c("#3C8D53", "#EC754A")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_wrap(facets =~reorder(MajorStatus, MajorStatusID), scales = "free_x", drop = TRUE) +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vMajorStatus) + 
  xlab("Major Status") + ylab("Number of Detailed Status")  +
  labs(title= "Major Status with Number of Detailed Status",  
       caption="Data Collected by: Junald A. Lagod")

To retrieve a list of Detailed Status records for INSTI along with the corresponding number of POs

This SQL query uses a JOIN operation to combine the Detailed Status table with the POs table based on a shared POID_PK It then calculates the count of POID_FK for each Detailed Status using the COUNT function and aliases it as ‘Number Of POs’. The GROUP BY clause ensures that the count is calculated per Detailed Status. By executing this query, you will retrieve a result set that includes the Detailed Status records along with the corresponding number of POs for each Municipality area, you can use the following SQL statement:

To view the Detailed Status for INSTITUTIONAL DEVELOPMENT table within the database, you can follow the command below:

#Organizational Capacity
vPOTypeStatus_OCR_tmp <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Component == "INSTI", vPOTypeStatus$Rating == "Organizational Capacity")%>%
  group_by(MajorStatus, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

# Make dataframe
results_df <- data.frame(vPOTypeStatus_OCR_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Major Status", "Rating", "Detailed Status", "No. of PO Group", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Organizational Capacity Status table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Organizational Capacity Status table

Major Status	Rating	Detailed Status	No. of PO Group
Actual-Implementation	Organizational Capacity	Highly Operational	104
Actual-Implementation	Organizational Capacity	Not Operational	5
Actual-Implementation	Organizational Capacity	Not yet assessed	70
Actual-Implementation	Organizational Capacity	Operational	170
Total:			349

#*******************************------------------------------------------------------------------------------------------------
#Enterprise Readiness
vPOTypeStatus_ERR_tmp <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Component == "INSTI", vPOTypeStatus$Rating == "Enterprise Readiness")%>%
  group_by(MajorStatus, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

# Make dataframe
results_df <- data.frame(vPOTypeStatus_ERR_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Major Status", "Rating", "Detailed Status", "No. of PO Group", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Enterprise Readiness Status table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Enterprise Readiness Status table

Major Status	Rating	Detailed Status	No. of PO Group
Actual-Implementation	Enterprise Readiness	Highly Ready	59
Actual-Implementation	Enterprise Readiness	Not Ready	53
Actual-Implementation	Enterprise Readiness	Not yet assessed	70
Actual-Implementation	Enterprise Readiness	Ready	167
Total:			349

#*******************************------------------------------------------------------------------------------------------------
#Maturity Level
vPOTypeStatus_MLR_tmp <- vPOTypeStatus %>%
  filter(vPOTypeStatus$Component == "INSTI", vPOTypeStatus$Rating == "Maturity Level")%>%
  group_by(MajorStatus, Rating, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

# Make dataframe
results_df <- data.frame(vPOTypeStatus_MLR_tmp, stringsAsFactors = FALSE)

# Sum the last row of each column if numeric 
func <- function(z) if (is.numeric(z)) sum(z) else '' 
sumrow <- as.data.frame(lapply(results_df, func))

# Give name to the first element of the new data frame created above
sumrow[1] <- "Total:"

# Add the original and new data frames together
summed_results_df <- rbind(results_df, sumrow)

# Name the columns
colnames <- data.frame("Major Status", "Rating", "Detailed Status", "No. of PO Group", stringsAsFactors = FALSE)
colnames(summed_results_df) <- colnames

# Make Table
kable(summed_results_df, caption = "Maturity Level Status table", booktabs = TRUE) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  row_spec(dim(summed_results_df)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) # format first column

Maturity Level Status table

Major Status	Rating	Detailed Status	No. of PO Group
Actual-Implementation	Maturity Level	Functional	98
Actual-Implementation	Maturity Level	Not Functional	171
Actual-Implementation	Maturity Level	Not yet assessed	80
Total:			349

To display a chart illustrating the records of Detailed Status for INSTI along with the corresponding number of POs using the ggplot command in R.

vPOTypeStatus <- union(vPOTypeStatus_OCR, vPOTypeStatus_ERR)
vPOTypeStatus <- union(vPOTypeStatus, vPOTypeStatus_MLR)
vPOTypeStatus <- vPOTypeStatus %>%
  group_by(Component, MajorStatus, Rating, StatusID, Status)  %>%
  summarise(POs = sum(POs), .groups = 'drop')

ggplot(vPOTypeStatus, aes(x = str_wrap(Status, width = 10) , y = as.integer(POs), fill=reorder(Status, StatusID))) +
  geom_col() +  
  geom_text(aes(label = as.integer(POs), y= as.integer(POs) / 2)) + 
  scale_y_continuous(
    breaks = seq(0, 200, by = 50), 
    expand = c(0, 0), # The horizontal axis does not extend to either side
  )  +
  guides(fill=guide_legend(title="", position="bottom")) + 
  scale_fill_manual(values = c("#EE754A", "#AABB11", "#3C8D53",  
                               "#EC754A", "#AABB11", "#3C8D53", 
                               "#BE2A3E", "#AABB11", "#EC754A", "#BE2A3E")) +
  # independent x-axis scale in each facet, 
  # drop absent factor levels (not the case here)
  facet_grid(cols = vars(Rating), scales = "free_x", space = "free_x") +
  force_panelsizes(cols = c(1, 1, 1)) +
  theme_bw() +
  # use named character vector to replace x-axis labels
  scale_x_discrete(labels = vPOTypeStatus) + 
  xlab("Detailed Status") + ylab("Number of POs")  +
  labs(title= "Status for Institutional Development with Number of People Organizations (POs)",  
       caption="Data Collected by: Junald A. Lagod")