---
title: "Weekly Report Dashboard"
output:
flexdashboard::flex_dashboard:
theme: cerulean
orientation: rows
vertical_layout: fill
social: [ "whatsapp","email", "menu"]
source_code: embed
---
```{r setup, include=FALSE}
setwd("C:/Users/rwand/Documents/REPORTS/Dashboard")
library(flexdashboard)
library(knitr)
library(plotly)
library(openintro)
library(tidyverse)
library(highcharter)
library(ggplot2)
library(ggridges)
library(DT)
library(readr)
library(readxl)
library(plotrix) # for 3D pie chart
library(pagedown) # so as to convert html in pdf
library(lattice)
library(gganimate)
library(htmltools)
library(htmlwidgets)
weekly = Sys.Date() -7
data1 <- read_csv("HAPINIIRwanda_DATA.csv") %>% rename("id" ="record_id")
school <- read_csv("HAPINRwandaSchedulin_DATA.csv")
logbook <- read_excel("~/REPORTS/Dashboard/logbook.xlsx")
logbook<-logbook %>% select(id,TreatmentArm) %>% rename("Treatment Arm" =TreatmentArm)
```
```{r, include=FALSE}
# Removing blank rows
HAPINIIRwanda_DATA=data1[-(1:8),]
# Extracting C33 in all CRFs
C33_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("c33_"))
#Extracting complete cases
C33_CRF_all=C33_CRF_all[complete.cases(C33_CRF_all$c33_date_2),]
# Yearly data set
#c33_60 month
c33_60M_crf=C33_CRF_all %>% filter(redcap_event_name=="year_5_arm_1")
## Exporting in excel
write.csv(c33_60M_crf,"c33_60M_crf.csv",row.names = FALSE)
#C31 CRFS
#============================================================================
# Extracting C31 in all CRFs
C31_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("c31_"))
#Extracting complete cases
C31_CRF_all=C31_CRF_all[complete.cases(C31_CRF_all$c31_date_2),]
# Yearly data set
#c33_60 month
c31_60M_crf=C31_CRF_all %>% filter(redcap_event_name=="year_5_arm_1")
c31_54M_crf=C31_CRF_all %>% filter(redcap_event_name=="year_4_q3_arm_1")
## Exporting in excel
write.csv(c31_60M_crf,"c31_60M_crf.csv",row.names = FALSE )
#=========================================================================
# Extracting C32 in all CRFs
C32_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("c32_"))
#Extracting complete cases
C32_CRF_all=C32_CRF_all[complete.cases(C32_CRF_all$c32_date_2),]
# Yearly data set
#c32_60 month
c32_60M_crf=C32_CRF_all %>% filter(redcap_event_name=="year_5_arm_1")
## Exporting in excel
write.csv(c32_60M_crf,"c32_60M_crf.csv",row.names = FALSE )
# C35 CRFs
#=======================================================================#
# Extracting C35 in all CRFs
C35_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("c35_"))
#Extracting complete cases
C35_CRF_all=C35_CRF_all[complete.cases(C35_CRF_all$c35_date_2),]
# Yearly data set
#c35_60 month
c35_60M_crf=C35_CRF_all %>% filter(redcap_event_name=="year_5_arm_1")
## Exporting in excel
write.csv(c35_60M_crf,"c35_60M_crf.csv",row.names = FALSE )
#=======================================================================#
# Extracting B11a in all CRFs
B11a_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("b11a_"))
#Extracting complete cases
B11a_CRF_all=B11a_CRF_all[complete.cases(B11a_CRF_all$b11a_date),]
# Yearly data set
#B11a_60 month
b11a_60M_crf=B11a_CRF_all %>% filter(redcap_event_name =="year_5_arm_1")
## Exporting in excel
write.csv(b11a_60M_crf,"b11a_60M_crf.csv", row.names = FALSE )
#=======================================================================#
# Extracting C85 in all CRFs
C85_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("c85_"))
#Extracting complete cases
C85_CRF_all=C85_CRF_all[complete.cases(C85_CRF_all$c85_date),]
# Yearly data set
c85_crf=C85_CRF_all
## Exporting in excel
write.csv(c85_crf,"c85_crf.csv",row.names = FALSE)
#==============================================================================
#c85_second assessment
#==========================================================================
# Extracting C85_second in all CRFs
C85_sec_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, ends_with("_2"))
#Extracting complete cases
C85_sec_CRF_all=C85_sec_CRF_all[complete.cases(C85_sec_CRF_all$c85_date_2 ),]
C85_sec2_CRF_all=select(C85_sec_CRF_all,id,redcap_event_name, starts_with("c85_"))
#MDAT second assessment all
c85_second_assessment_crf=C85_sec2_CRF_all
## Exporting in excel
write.csv(c85_second_assessment_crf,"c85_second_assessment_crf.csv",row.names = FALSE )
#==================================================================================
#e3 CRFS
#============================================================================
# Extracting e3 in all CRFs
e3_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("e3_"))
#Extracting complete cases
e3_CRF_all=e3_CRF_all[complete.cases(e3_CRF_all$e3_date),]
# Yearly data set
#E3_24_36_48 and 60 month
e3=e3_CRF_all
## Exporting in excel
write.csv(e3,"e3_crf.csv",row.names = FALSE)
#=========================================================================
#H41 CRFS
#============================================================================
# Extracting H41 in all CRFs
H41_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("h41_"))
#Extracting complete cases
H41_all=H41_CRF_all[complete.cases(H41_CRF_all$h41_date_v2),]
# Yearly data set
#h41_60 month
h41_60M_crf=H41_all %>% filter(redcap_event_name =="year_5_arm_1")
view(h41_60M_crf)
h41_54M_crf=H41_all %>% filter(redcap_event_name== "year_4_q3_arm_1")
## Exporting in excel
write.csv(h41_60M_crf,"h41_60M_crf.csv",row.names = FALSE)
#=========================================================================
# H42 CRF
#_____________________________________#
# Extracting H42 in all CRFs
H42_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("h42_"))
#Extracting complete cases
H42_CRF_all=H42_CRF_all[complete.cases(H42_CRF_all$h42_date_2),]
# Yearly data set
#H42_60 month
h42_60M_crf=H42_CRF_all %>% filter(redcap_event_name =="year_5_arm_1")
h42_54M_crf=H42_CRF_all %>% filter(redcap_event_name == "year_4_q3_arm_1")
## Exporting in excel
write.csv(h42_60M_crf,"h42_60M_crf.csv",row.names = FALSE)
#-----------------------------------------------------------------------------
# H43 CRF
#_____________________________________#
# Extracting H43 in all CRFs
H43_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("h43_"))
#Extracting complete cases
H43_all=H43_CRF_all[complete.cases(H43_CRF_all$h43_date_2),]
# Yearly data set
#H43_60 month
h43_60M_crf=H43_CRF_all %>% filter(redcap_event_name =="year_5_arm_1")
## Exporting in excel
write.csv(h43_60M_crf,"h43_60M_crf.csv", row.names = FALSE )
# M10 CRF
#_____________________________________#
# Extracting M10 in all CRFs
M10_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("m10_"))
#Extracting complete cases
M10_CRF_all=M10_CRF_all[complete.cases(M10_CRF_all$m10_date),]
# Yearly data set
#m19 all month
m10_crf=M10_CRF_all
## Exporting in excel
write.csv(m10_crf,"m10_crf.csv",row.names = FALSE)
#_____________________________________________________________________
# M11 CRF
#_____________________________________#
# Extracting M11 in all CRFs
M11_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("m11_"))
#Extracting complete cases
M11_CRF_all=M11_CRF_all[complete.cases(M11_CRF_all$m11_date),]
# Yearly data set
#m19 all month
m11_crf=M11_CRF_all
## Exporting in excel
write.csv(m11_crf,"m11_crf.csv",row.names = FALSE)
#_____________________________________________________________________
# M19 CRF
#_____________________________________#
# Extracting M19 in all CRFs
M19_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("m19_"))
#Extracting complete cases
M19_CRF_all=M19_CRF_all[complete.cases(M19_CRF_all$m19_date),]
# Yearly data set
#m19 all month
m19_crf=M19_CRF_all
## Exporting in excel
write.csv(m19_crf,"m19_crf.csv",row.names = FALSE)
#_____________________________________________________________________
# M14b CRF
#_____________________________________#
# Extracting M14b in all CRFs
M14b_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with(c("m14_","m14b_")))
#Extracting complete cases
M14b_CRF_all=M14b_CRF_all[complete.cases(M14b_CRF_all$m14b_date),]
# Yearly data set
#m19 all month
m14b_crf=M14b_CRF_all
## Exporting in excel
write.csv(m14b_crf,"m14b_crf.csv",row.names = FALSE)
#_____________________________________________________________________
# s4 CRF
#_____________________________________#
# Extracting S4 in all CRFs
S4_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("s4_"))
#Extracting complete cases
S4_CRF_all=S4_CRF_all[complete.cases(S4_CRF_all$s4_date),]
# Yearly data set
#S4 for all month
s4_crf=S4_CRF_all
## Exporting in excel
write.csv(s4_crf,"s4_crf.csv",row.names = FALSE)
#_____________________________________________________________________
# c88 CRF
#_____________________________________#
# Extracting H41 in all CRFs
c88_CRF_all=select(HAPINIIRwanda_DATA,id,redcap_event_name, starts_with("c88_"))
#Extracting complete cases
c88_CRF_all=c88_CRF_all[complete.cases(c88_CRF_all$c88_date),]
# Yearly data set
#c88 for all month
c88_crf=c88_CRF_all
## Exporting in excel
write.csv(c88_crf,"c88_crf.csv",row.names = FALSE)
```
```{r, include=FALSE}
###____ Total number of MDAT completed _____###
## INTERVENTION ##
c85_logbook =merge(c85_crf,logbook, by="id")
# Completed in Intervention
intervention = c85_logbook %>%
filter(`Treatment Arm`=="Intervention", redcap_event_name=="year_5_arm_1")
length(intervention$id)
## Previous wk
interventionWK = c85_logbook %>%
filter(`Treatment Arm`=="Intervention", redcap_event_name=="year_5_arm_1") %>%
filter(c85_date >= weekly)
length(interventionWK$id)
# Completed in Control
control = c85_logbook %>%
filter(`Treatment Arm`=="Control", redcap_event_name=="year_5_arm_1")
length(control$id)
## Previous wk
controlWK = c85_logbook %>%
filter(`Treatment Arm`=="Control", redcap_event_name=="year_5_arm_1") %>%
filter(c85_date >= weekly)
length(controlWK$id)
## Total number of children completed MDAT_Social
c85_60 =c85_logbook %>% filter(redcap_event_name =="year_5_arm_1")
soc = c85_logbook %>%
select(c85_spoon_self_2:c85_toilet_self_2) %>% drop_na()
all_social <- soc[apply(soc, 1, function(row) all(row == 1)), ]
length(all_social$c85_spoon_self_2)
## Social week
socWK = c85_logbook %>%
select(c85_date, c85_spoon_self_2:c85_toilet_self_2) %>%
filter(c85_date>= weekly, c85_date < Sys.Date())
so1 = socWK %>% select(-c85_date)
all_socialWK <- so1[apply(so1, 1, function(row) all(row == 1)), ]
length(all_socialWK$c85_spoon_self_2)
## Total number of children completed MDAT_language
language = c85_60 %>% select(c85_5_identify:c85_place)
all_language <- language[apply(language, 1, function(row) all(row == 1)), ]
length(all_language$c85_5_identify)
# Language week
languageWK = c85_60 %>% select(c85_date, c85_5_identify:c85_place) %>%
filter(c85_date >= weekly)
language1 = languageWK %>% select(-c85_date)
all_languagewk <- language1[apply(language1, 1, function(row) all(row == 1)), ]
length(all_languagewk$c85_5_identify)
## Total number of children completed MDAT_Fine Moto
fine = c85_60 %>%
select(c85_4block:c85_top_sq)
all_fine <- fine[apply(fine, 1, function(row) all(row == 1)), ]
length(all_fine$c85_4block)
# Fine Moto week
fineWK = c85_60 %>%
select(c85_date,c85_4block:c85_top_sq) %>%
drop_na() %>%
filter(c85_date>= weekly)
fi1 = fineWK %>% select(-c85_date)
all_finewk <- fi1[apply(fi1, 1, function(row) all(row == 1)), ]
length(all_finewk$c85_4block)
## Total number of children completed MDAT_Gross Moto
gross = c85_60 %>% select(c85_run:c85_basket_2m) %>%
drop_na()
all_gross <- gross[apply(gross, 1, function(row) all(row == 1)), ]
length(all_gross$c85_run)
# Gross Moto week
grosswk = c85_60 %>% select(c85_date, c85_run:c85_basket_2m) %>%
drop_na() %>%
filter(c85_date >= weekly)
go1 = grosswk %>% select(-c85_date)
all_grossWK <- go1[apply(go1, 1, function(row) all(row == 1)), ]
length(all_grossWK$c85_run)
domain = data.frame(MDAT_Domain = c("Number of children who completed MDAT_Intervention",
"Number of children who completed MDAT_control",
"Total number of children who completed MDAT_Social",
"Total number of children who completed MDAT_Language",
"Total number of children who completed MDAT_Fine Motor",
"Total number of children who completed MDAT_Gross Motor"),
Since_the_start = c(length(intervention$id),
length(control$id),
length(all_social$c85_spoon_self_2),
length(all_language$c85_5_identify),
length(all_fine$c85_4block),
length(all_gross$c85_run)),
This_week = c(length(interventionWK$id),
length(controlWK$id),
length(all_socialWK$c85_spoon_self_2),
length(all_languagewk$c85_5_identify),
length(all_finewk$c85_4block),
length(all_grossWK$c85_run)))
```
```{r}
c33_60M_crf$id = as.numeric(c33_60M_crf$id)
c33_60 <- left_join(logbook, c33_60M_crf, by = "id")
c33_60_complete <- c33_60[complete.cases(c33_60$redcap_event_name), ]
c33_60_complete <- c33_60_complete %>% distinct(id, .keep_all = TRUE)
```
Weekly report dashboard
=======================================================================
Row
-----------------------------------------------------------------------
### All completed HH
```{r}
#valueBox(length(c33_60_complete$id), icon = "fa-building")
gauge(length(c33_60_complete$id), min = 0, max = 750,
gaugeSectors(success = c(500,750),
colors = "blue"))
```
### Control
```{r}
control <- c33_60_complete %>%
filter(`Treatment Arm`== "Control")
valueBox(length(control$id),
icon = "fa-socks")
```
### Intervention
```{r}
intervention <- c33_60_complete %>%
filter(`Treatment Arm`== "Intervention")
valueBox(length(intervention$id),
icon = "fa-truck")
```
### Completed E3
```{r}
e = e3 %>% filter(redcap_event_name=="year_5_arm_1")
valueBox(length(e$id),color = "#E52356", icon = "fa-house")
```
### Completed BP for child
```{r}
bp = c88_crf %>% select(id,c88_date)
valueBox(length(bp$id), icon = "fa-baby")
```
### Completed MDAT (60M)
```{r}
mdat = C85_CRF_all %>% filter(redcap_event_name=="year_5_arm_1")
valueBox(length(mdat$c85_date), icon = "fa-child")
```
Row
-----------------------------------------------------------------------
### Pie chart for control vs intervention
```{r, fig.dim=0.1}
CI <- c33_60_complete %>%
group_by(`Treatment Arm`) %>%
summarise(Total = n())
plot_ly(CI) %>%
add_pie(CI, labels=~`Treatment Arm`,values=~`Total`,
type="pie", hole=0.5)
```
### Anthropo (average,max,min, SD of weight measurement)
```{r}
c33_60_complete$c33_ave_wt_2 <- c33_60_complete$c33_ave_wt_2[(c33_60_complete$c33_wt1_2+c33_60_complete$c33_wt2_2)/2]
c33_60_complete$c33_ave_ht_2 <- c33_60_complete$c33_ave_ht_2[(c33_60_complete$c33_ht1_2+c33_60_complete$c33_ht2_2)/2]
antr_weight <- data.frame(
measurement = c("Average", "Maximum", "Minimum", "Standard Deviation"),
value = c(round(mean(c33_60_complete$c33_ave_wt_2), digits = 2),
max(c33_60_complete$c33_ave_wt_2),
min(c33_60_complete$c33_ave_wt_2),
round(sd(c33_60_complete$c33_ave_wt_2), digits = 2)))
# Create the bar plot
ggplot(antr_weight, aes(x = measurement, y = value, fill = measurement)) +
geom_bar(stat = "identity", show.legend = TRUE, width = 0.6) + # Create bars with legend
geom_text(aes(label = value), vjust = -0.3, size = 4) + # Add value on top of each bar
scale_fill_brewer(palette = "Set2") + # Use a visually pleasing palette
labs(
title = "Summary Statistics Bar Plot",
x = "Measurement",
y = "Value",
fill = "Measurement Type"
) +
ylim(0,25) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1), # Rotate x-axis labels for readability
legend.position = "bottom"
)
# Assuming c33_60_complete is already defined and contains c33_ave_wt_2
# Create a data frame with raw data
#raw_data <- data.frame(value = c33_60_complete$c33_ave_wt_2)
# Calculate summary statistics
#summary_stats <- data.frame(
# stat = c("Mean", "Maximum", "Minimum", "Standard Deviation"),
# value = c(
# round(mean(c33_60_complete$c33_ave_wt_2), 2),
# max(c33_60_complete$c33_ave_wt_2),
# min(c33_60_complete$c33_ave_wt_2),
# round(sd(c33_60_complete$c33_ave_wt_2), 2)
# )
#)
# Create the box plot
#ggplot(raw_data, aes(x = "", y = value)) +
# geom_boxplot(fill = "skyblue", outlier.color = "red", width = 0.5) +
# geom_point(data = summary_stats, aes(x = "", y = value, color = stat), size = 3) +
# labs(
# title = "Box Plot with Summary Statistics",
# y = "Values",
# x = "",
# caption = "Red points represent summary statistics."
# ) +
# theme_minimal() +
# theme(legend.position = "bottom")
```
### Anthropo (average,max,min, SD of height measurement)
```{r}
antr_height <- data.frame(
measurement = c("Average", "Maximum", "Minimum", "Standard Deviation"),
value = c(round(mean(c33_60_complete$c33_ave_ht_2), digits = 2),
max(c33_60_complete$c33_ave_ht_2),
min(c33_60_complete$c33_ave_ht_2),
round(sd(c33_60_complete$c33_ave_ht_2), digits = 2)))
# Create the bar plot
ggplot(antr_height, aes(x = measurement, y = value, fill = measurement)) +
geom_bar(stat = "identity", show.legend = TRUE, width = 0.6) + # Create bars with legend
geom_text(aes(label = value), vjust = -0.3, size = 4) + # Add value on top of each bar
scale_fill_brewer(palette = "Set2") + # Use a visually pleasing palette
labs(
title = "Summary Statistics Bar Plot",
x = "Measurement",
y = "Value",
fill = "Measurements"
) +
ylim(0,120) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1), # Rotate x-axis labels for readability
legend.position = "bottom"
)
```
### Completed CRFs by visit
```{r}
co <- H41_all %>%
select(redcap_event_name, h41_date) %>%
mutate(
event_name = case_when(
redcap_event_name == "year_3q1_arm_1" ~ "Year 3",
redcap_event_name == "year_3_q3_arm_1" ~ "Year 3 Subsample",
redcap_event_name == "year_4_q1_arm_1" ~ "Year 4",
redcap_event_name == "year_4_q3_arm_1" ~ "Year 4 Subsample",
redcap_event_name == "year_5_arm_1" ~ "Year 5",
TRUE ~ "Other")) %>%
group_by(event_name) %>%
summarise(Total = n(), .groups = "drop")
# Create a bar chart
bar <- ggplot(data = co, aes(x = event_name, y = Total, fill = event_name)) +
geom_col(position = "dodge") + # Create the bars
geom_text(aes(label = Total), vjust = -0.5, size = 4) + # Add values on top of bars
labs(
title = "Completed CRFs by visits",
x = "Event Name",
y = "Total Count",
fill = "Legend") + # Title for the legend
scale_y_continuous(limits = c(0, 800)) +
theme_minimal() + # Clean theme
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels
# Display the plot
print(bar)
```
### Box plot to show the relationship between weight and height
```{r}
c88 = c88_crf %>%
mutate(Gender = ifelse(c88_sex==1, "Male", "Female")) %>%
group_by(Gender) #%>% summarise(Systolic = max(c88_ave_sbp, na.rm = T))
bwplot(Gender ~ c88_ave_sbp, data=c88, xlab = "BP systolic")
```
THIS WEEK
=======================================================================
Row
-----------------------------------------------------------------------
### Completed CRFs this week
```{r}
allwk = c33_60_complete %>% filter(c33_date_2 >= weekly, c33_date_2 < Sys.Date())
gauge(length(allwk$id), min = 0, max = 16, symbol = "HHDs",
gaugeSectors(success = c(4,16),
warning = c(2, 3),
danger = c(0, 1),
colors = "green"))
```
### Control for this week
```{r}
controlwkl <- c33_60_complete %>%
filter(`Treatment Arm`== "Control", c33_date_2 >= weekly, c33_date_2 < Sys.Date())
valueBox(length(controlwkl$id),
icon = "fa-socks")
```
### Intervention for this week
```{r}
interventionwkl <- c33_60_complete %>%
filter(`Treatment Arm`== "Intervention", c33_date_2 >= weekly, c33_date_2 < Sys.Date())
valueBox(length(interventionwkl$id),
icon = "fa-truck")
```
### Anthopo completed this week
```{r}
#class(c33_60_complete$c33_date)
c33wk = c33_60_complete %>% select(id,c33_date_2) %>%
filter(c33_date_2 >= weekly, c33_date_2 < Sys.Date())
valueBox(length(c33wk$id), icon = "fa-building")
```
### E3 completed this week
```{r}
ewk = e3 %>% filter(redcap_event_name=="year_5_arm_1", e3_date >= weekly)
valueBox(length(ewk$id),color = "#F31C11", icon = "fa-house")
```
MDAT
=======================================================================
Row
-----------------------------------------------------------------------
### Completed MDAT this week
```{r}
class(c85_crf$c85_date)
mdatwk = c85_crf %>% select(id,redcap_event_name,c85_date) %>%
filter(redcap_event_name == "year_5_arm_1" & c85_date >= weekly & c85_date < Sys.Date())
valueBox(length(mdatwk$id),color = "magenta", icon = "fa-child")
```
Row
-----------------------------------------------------------------------
### Completed MDAT by domain (this week)
```{r}
barchart(MDAT_Domain ~ This_week, data=domain,
xlab = "Level",
main = "Completed MDAT by domain (this week)",aspect = "fill",
panel = lattice.getOption("panel.barchart"),
default.prepanel = lattice.getOption("prepanel.default.barchart"),
box.ratio = 2)
```
### Chart of completed MDAT by domain since the start
```{r}
# Replace with relevant columns for your dataset
ggplot(domain, aes(x = MDAT_Domain, y = Since_the_start, fill = MDAT_Domain)) +
geom_col() +
labs(title = "Bar Chart of completed MDAT by domain", x = "MDAT_Domain", y = "Count") +
theme_minimal()
```
### Chart of completed MDAT by domain this week
```{r}
ggplot(domain, aes(x = MDAT_Domain, y = This_week, fill = MDAT_Domain)) +
geom_col() +
geom_text(aes(label = This_week), vjust = -0.5, size = 4) +
labs(title = "Bar Chart of completed MDAT by domain this week",
x = NULL, # Removes x-axis label
y = "Count") +
theme_minimal() +
theme(legend.title = element_blank()) # Optionally remove the legend title
```
### Data table
```{r}
datatable(domain)
```
Blood Pressure
=======================================================================
Row
-----------------------------------------------------------------------
### Child BP by heights on scatter plot
```{r}
c88_child = c88_crf %>%
mutate(Gender = ifelse(c88_sex==1, "Male", "Female"))
s=plot_ly(c88_child, x=~c88_ave_sbp, y=~c88_ht) %>%
layout(title=" Scatter plot of child BP by heights",
xaxis= list(title="Average BP"),
yaxis= list(title= "Heights"))
s
```
### Child BP by heights on line plot
```{r}
s %>% add_paths(linetype= ~Gender)
```
### Child BP by heights on box plot
```{r}
b=c88_child %>%
plot_ly(orientation="h", line=list(color="grey"), height=700, weight=600) %>%
add_boxplot(x=~c88_ave_sbp, y=~c88_ht)
b
```
THIS WEEK
=======================================================================
