In this hands-on exercise, you will learn how to handle geospatial data in R by using appropriate R packages.
By the end of this hands-on exercise, you should aquire the following competencies:
Before you can start using R, you are required to extract the necessary data sets from the appropriate source
Before we get started, it is important for us to ensure that all the R packages we need have been installed and launched in RStudio by using the code chunk below.
packages = c('sf', 'tmap', 'tidyverse')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}One of the key package uses in the process below is sf. A detail discussion of the core features of sf package and its syntax guild are available here.
In this hands-on exercise, you are going to import two geospatial data in shapefile format into R’s simple feature data.frame:
If you are new to the concept of simple feature data.frame, you are advised to this article.
In this section, we will perform the following task in R:
Barangays <- st_read(dsn = "data/geospatial/Demography_GoLeadDog_Phils/MBI_PH_R2019_Micro_SHP", layer = "MBI_PH_R2019_Barangays") %>%
st_transform(crs = 32651)## Reading layer `MBI_PH_R2019_Barangays' from data source `D:\20-Research\PH\PH\data\geospatial\Demography_GoLeadDog_Phils\MBI_PH_R2019_Micro_SHP' using driver `ESRI Shapefile'
## Simple feature collection with 41851 features and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 116.8711 ymin: 4.586942 xmax: 126.6054 ymax: 21.12236
## geographic CRS: WGS 84The short report printed gives the input file name, mentions that there are 41851 features (records, represented as rows) and 3 fields (attributes, represented as columns), states that the spatial data file is a MULTIPOLYGON, provides the bounding box coordinates, and identifies the projection method (which we will discuss later).
We can list Barangays simple feature data.frame by using the code chunk below.
Barangays## Simple feature collection with 41851 features and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -176511.6 ymin: 507973.6 xmax: 898220.2 ymax: 2336052
## projected CRS: WGS 84 / UTM zone 51N
## First 10 features:
## CTRYCODE MICROCODE NAME geometry
## 1 PH RP012801001 Adams (Pob.) MULTIPOLYGON (((285745.4 20...
## 2 PH RP012802001 Bani MULTIPOLYGON (((247813.2 20...
## 3 PH RP012802002 Buyon MULTIPOLYGON (((249654.7 20...
## 4 PH RP012802003 Cabaruan MULTIPOLYGON (((244208.3 20...
## 5 PH RP012802004 Cabulalaan MULTIPOLYGON (((245262.1 20...
## 6 PH RP012802005 Cabusligan MULTIPOLYGON (((247465.5 20...
## 7 PH RP012802006 Cadaratan MULTIPOLYGON (((246476.3 20...
## 8 PH RP012802007 Calioet-Libong MULTIPOLYGON (((244992.6 20...
## 9 PH RP012802008 Casilian MULTIPOLYGON (((243740.2 20...
## 10 PH RP012802009 Corocor MULTIPOLYGON (((246563.6 20...In the output we see:
In this section, we will perform the following task in R:
restrnts <- st_read(dsn = "data/geospatial/POI_HERE_Phils",
layer = "Restrnts") %>%
st_transform(crs = 32651)## Reading layer `Restrnts' from data source `D:\20-Research\PH\PH\data\geospatial\POI_HERE_Phils' using driver `ESRI Shapefile'
## Simple feature collection with 38353 features and 33 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 117.6286 ymin: 5.99147 xmax: 126.5643 ymax: 20.44993
## geographic CRS: WGS 84In general, the output above looks similar to the earlier section except the geometry type: is indicates as POINT.
Likewise, we can display the content of restrnts simple feature data.frame using the code chunk below.
restrnts## Simple feature collection with 38353 features and 33 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -91745.66 ymin: 662528.9 xmax: 893639.9 ymax: 2261605
## projected CRS: WGS 84 / UTM zone 51N
## First 10 features:
## LINK_ID POI_ID SEQ_NUM FAC_TYPE POI_NAME POI_LANGCD
## 1 1124830252 1203363416 1 5800 CENTERPLAY ENG
## 2 1125953016 1203363412 1 5800 CURRY BEE ENG
## 3 830013699 1203363413 1 5800 TSOKO NUT BATIROL ENG
## 4 1024878793 1203363419 1 5800 SUSHI NORI ENG
## 5 1125976472 1195880240 1 5800 TIM HO WAN ENG
## 6 1205242737 1195880945 1 9996 FARRON CAFE ENG
## 7 962623194 1048253528 1 9996 JEACA'S COFFEE & CO. ENG
## 8 830114837 1201357214 1 5800 SBARRO ENG
## 9 830114837 1201357212 1 5800 RED RIBBON ENG
## 10 830114837 1201357219 1 9996 TOBY'S ESTATE ENG
## POI_NMTYPE POI_ST_NUM ST_NUM_FUL ST_NFUL_LC ST_NAME ST_LANGCD
## 1 B <NA> <NA> <NA> <NA> <NA>
## 2 B <NA> <NA> <NA> <NA> <NA>
## 3 B <NA> <NA> <NA> MCKINLEY HILL DR ENG
## 4 B <NA> <NA> <NA> MCKINLEY PKWY ENG
## 5 B <NA> <NA> <NA> PACIFIC DR ENG
## 6 B <NA> <NA> <NA> <NA> <NA>
## 7 B <NA> <NA> <NA> NATIONAL HWY ENG
## 8 B <NA> <NA> <NA> SHAW BLVD ENG
## 9 B <NA> <NA> <NA> SHAW BLVD ENG
## 10 B <NA> <NA> <NA> SHAW BLVD ENG
## POI_ST_SD ACC_TYPE PH_NUMBER CHAIN_ID NAT_IMPORT PRIVATE IN_VICIN NUM_PARENT
## 1 L <NA> <NA> 0 N N N 0
## 2 N <NA> <NA> 0 N N N 0
## 3 R <NA> <NA> 0 N N N 0
## 4 L <NA> <NA> 0 N N N 0
## 5 L <NA> <NA> 0 N N N 0
## 6 R <NA> <NA> 0 N N N 0
## 7 R <NA> <NA> 0 N N N 0
## 8 R <NA> <NA> 1591 N N N 1
## 9 R <NA> <NA> 8094 N N N 1
## 10 R <NA> <NA> 0 N N N 1
## NUM_CHILD PERCFRREF FOOD_TYPE ALT_FOOD REG_FOOD RSTR_TYPE
## 1 0 34 SNACKS & BEVERAGES <NA> <NA> <NA>
## 2 0 25 JAPANESE FOOD <NA> <NA> <NA>
## 3 0 11 JAPANESE FOOD <NA> <NA> <NA>
## 4 0 2 JAPANESE FOOD <NA> <NA> <NA>
## 5 0 49 CHINESE FOOD <NA> <NA> <NA>
## 6 0 20 <NA> <NA> <NA> <NA>
## 7 0 64 <NA> <NA> <NA> <NA>
## 8 0 62 PIZZA <NA> <NA> <NA>
## 9 0 62 FILIPINO FOOD <NA> <NA> <NA>
## 10 0 62 <NA> <NA> <NA> <NA>
## VANCITY_ID ACT_ADDR ACT_LANGCD ACT_ST_NAM ACT_ST_NUM ACT_ADMIN ACT_POSTAL
## 1 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 2 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 4 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 5 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 6 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 7 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 8 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 9 0 <NA> <NA> <NA> <NA> <NA> <NA>
## 10 0 <NA> <NA> <NA> <NA> <NA> <NA>
## geometry
## 1 POINT (283756.7 1606833)
## 2 POINT (283467 1608214)
## 3 POINT (290323.8 1607918)
## 4 POINT (290202.5 1608995)
## 5 POINT (282671.6 1607864)
## 6 POINT (287789.6 1621249)
## 7 POINT (297674.3 1542246)
## 8 POINT (290368.3 1612882)
## 9 POINT (290368.3 1612882)
## 10 POINT (290368.3 1612882)Although there are a total of 33 fields in the data.frame but not all of them are useful. In view of this we can revise the initial read_st() code as shown in the code chunk below by using select() of dplyr package.
restrnts <- st_read(dsn = "data/geospatial/POI_HERE_Phils",
layer = "Restrnts") %>%
st_transform(crs = 32651) %>%
select(1:5, 7, 23)## Reading layer `Restrnts' from data source `D:\20-Research\PH\PH\data\geospatial\POI_HERE_Phils' using driver `ESRI Shapefile'
## Simple feature collection with 38353 features and 33 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 117.6286 ymin: 5.99147 xmax: 126.5643 ymax: 20.44993
## geographic CRS: WGS 84restrnts## Simple feature collection with 38353 features and 7 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -91745.66 ymin: 662528.9 xmax: 893639.9 ymax: 2261605
## projected CRS: WGS 84 / UTM zone 51N
## First 10 features:
## LINK_ID POI_ID SEQ_NUM FAC_TYPE POI_NAME POI_NMTYPE
## 1 1124830252 1203363416 1 5800 CENTERPLAY B
## 2 1125953016 1203363412 1 5800 CURRY BEE B
## 3 830013699 1203363413 1 5800 TSOKO NUT BATIROL B
## 4 1024878793 1203363419 1 5800 SUSHI NORI B
## 5 1125976472 1195880240 1 5800 TIM HO WAN B
## 6 1205242737 1195880945 1 9996 FARRON CAFE B
## 7 962623194 1048253528 1 9996 JEACA'S COFFEE & CO. B
## 8 830114837 1201357214 1 5800 SBARRO B
## 9 830114837 1201357212 1 5800 RED RIBBON B
## 10 830114837 1201357219 1 9996 TOBY'S ESTATE B
## FOOD_TYPE geometry
## 1 SNACKS & BEVERAGES POINT (283756.7 1606833)
## 2 JAPANESE FOOD POINT (283467 1608214)
## 3 JAPANESE FOOD POINT (290323.8 1607918)
## 4 JAPANESE FOOD POINT (290202.5 1608995)
## 5 CHINESE FOOD POINT (282671.6 1607864)
## 6 <NA> POINT (287789.6 1621249)
## 7 <NA> POINT (297674.3 1542246)
## 8 PIZZA POINT (290368.3 1612882)
## 9 FILIPINO FOOD POINT (290368.3 1612882)
## 10 <NA> POINT (290368.3 1612882)Our past experience working with HERE’s POI data reminded us that there is the possibility some records are duplicated. In view of this, we will use the code chunk below to identify if there are any duplicated records in restrnts simple feature data.frame.
restrnts %>%
filter(duplicated(`POI_ID`) |
duplicated(`POI_ID`,
fromLast = TRUE))## Simple feature collection with 1492 features and 7 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 31757.22 ymin: 676415.8 xmax: 852516.1 ymax: 2014079
## projected CRS: WGS 84 / UTM zone 51N
## First 10 features:
## LINK_ID POI_ID SEQ_NUM FAC_TYPE
## 1 982361202 1130399225 1 5800
## 2 982361202 1130399225 2 5800
## 3 1213565778 1122959278 1 5800
## 4 1213565778 1122959278 2 5800
## 5 1032681096 1130854132 1 5800
## 6 1032681096 1130854132 2 5800
## 7 1204064887 1130854133 1 5800
## 8 1204064887 1130854133 2 5800
## 9 905373757 1104512754 1 5800
## 10 905373757 1104512754 2 5800
## POI_NAME POI_NMTYPE FOOD_TYPE
## 1 SHEKINAH LOMI HOUSE BULALOHAN AND RESTAURANT B FILIPINO FOOD
## 2 SHEKINAH LOMI HOUSE BULALOHAN J FILIPINO FOOD
## 3 GREEN PASTURES FASTFOOD & BAKESHOPPE B FAST FOOD
## 4 GREEN PASTURES FASTFOOD J FAST FOOD
## 5 YUMMY TEPPAN YAKI & KOREAN RESTAURANT B KOREAN FOOD
## 6 YUMMY TEPPAN RESTAURANT J KOREAN FOOD
## 7 VERY GOOD HOUSE GRILL BUFFET RESTAURANT B GRILL
## 8 VERY GOOD HOUSE RESTAURANT J GRILL
## 9 GOLDEN CHICKEN HOUSE & CHINESE FOOD CENTER B CHINESE FOOD
## 10 GOLDEN CHICKEN HOUSE J CHINESE FOOD
## geometry
## 1 POINT (301785.6 1549796)
## 2 POINT (301785.6 1549796)
## 3 POINT (731945.7 878878.3)
## 4 POINT (731945.7 878878.3)
## 5 POINT (610342.6 1139256)
## 6 POINT (610342.6 1139256)
## 7 POINT (610513.9 1139444)
## 8 POINT (610513.9 1139444)
## 9 POINT (287334.8 1615783)
## 10 POINT (287334.8 1615783)The report indicates that there are 1492 duplicated records in restrnts simple feature data.frame.
By examine the report carefully, it is clear that the duplicated records can be dropped by referring to either SEQ_NUM or POI_NMTYPE. In view of this, we can further refine the original code chunk to import and convert the shapefile into simple feature data.frame by using filter() of dplyr package as shown below.
restrnts <- st_read(dsn = "data/geospatial/POI_HERE_Phils",
layer = "Restrnts") %>%
st_transform(crs = 32651) %>%
select(1:5, 7, 23) %>%
filter(`SEQ_NUM` == 1)## Reading layer `Restrnts' from data source `D:\20-Research\PH\PH\data\geospatial\POI_HERE_Phils' using driver `ESRI Shapefile'
## Simple feature collection with 38353 features and 33 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 117.6286 ymin: 5.99147 xmax: 126.5643 ymax: 20.44993
## geographic CRS: WGS 84restrnts## Simple feature collection with 37606 features and 7 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -91745.66 ymin: 662528.9 xmax: 893639.9 ymax: 2261605
## projected CRS: WGS 84 / UTM zone 51N
## First 10 features:
## LINK_ID POI_ID SEQ_NUM FAC_TYPE POI_NAME POI_NMTYPE
## 1 1124830252 1203363416 1 5800 CENTERPLAY B
## 2 1125953016 1203363412 1 5800 CURRY BEE B
## 3 830013699 1203363413 1 5800 TSOKO NUT BATIROL B
## 4 1024878793 1203363419 1 5800 SUSHI NORI B
## 5 1125976472 1195880240 1 5800 TIM HO WAN B
## 6 1205242737 1195880945 1 9996 FARRON CAFE B
## 7 962623194 1048253528 1 9996 JEACA'S COFFEE & CO. B
## 8 830114837 1201357214 1 5800 SBARRO B
## 9 830114837 1201357212 1 5800 RED RIBBON B
## 10 830114837 1201357219 1 9996 TOBY'S ESTATE B
## FOOD_TYPE geometry
## 1 SNACKS & BEVERAGES POINT (283756.7 1606833)
## 2 JAPANESE FOOD POINT (283467 1608214)
## 3 JAPANESE FOOD POINT (290323.8 1607918)
## 4 JAPANESE FOOD POINT (290202.5 1608995)
## 5 CHINESE FOOD POINT (282671.6 1607864)
## 6 <NA> POINT (287789.6 1621249)
## 7 <NA> POINT (297674.3 1542246)
## 8 PIZZA POINT (290368.3 1612882)
## 9 FILIPINO FOOD POINT (290368.3 1612882)
## 10 <NA> POINT (290368.3 1612882)The report shows that the output restrnts simple feature data.frame consists of 37606 point features and 7 fields
We can re-run the checking duplicated records code chunk to confirm that the restrnts simple feature data.frame no longer contain any duplicated records.
restrnts %>%
filter(duplicated(`POI_ID`) |
duplicated(`POI_ID`,
fromLast = TRUE))## Simple feature collection with 0 features and 7 fields
## bbox: xmin: NA ymin: NA xmax: NA ymax: NA
## projected CRS: WGS 84 / UTM zone 51N
## [1] LINK_ID POI_ID SEQ_NUM FAC_TYPE POI_NAME POI_NMTYPE FOOD_TYPE
## [8] geometry
## <0 rows> (or 0-length row.names)The report shows that there are no more duplicated records in restrnts simple feature data.frame.
Aspatial data are attribute data contain geographical information but not in GIS feature data format. One of the good example in our project is the store_information data file. In this data file, we can find the Latitude and Longitude fields provide the location information of the stores but are not in GIS data format.
The code chunk below is used to perform the following operations:
store_info <- read_csv("data/aspatial/store_location.csv",
col_types = cols(`Local Code` = col_character(),
`JDE Code` = col_character())) %>%
select(2:4,7:11, 18:19) %>%
st_as_sf(coords = c("Longitude",
"Latitude"),
crs= 4326) %>%
st_transform(crs = 32651)
store_info## Simple feature collection with 220 features and 8 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 207753.7 ymin: 676579.7 xmax: 791102.9 ymax: 2011750
## projected CRS: WGS 84 / UTM zone 51N
## # A tibble: 220 x 9
## Status Milestone `Local Code` `Store Name` `Latest Asset T~ `Facility Type`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Relo ~ Closed 51005648 Delta Delco In Line
## 2 New o~ Opened 51005651 Sm City RR Mall
## 3 New o~ Opened 51005653 C.M. Recto RBD In Line
## 4 New o~ Opened 51005656 Araneta Col~ Express In Line
## 5 New o~ Opened 51005661 Wilson Delco In Line
## 6 New o~ Opened 51005660 Ermita Delco In Line
## 7 New o~ Opened 51005662 Centrepoint RR Mall
## 8 New o~ Opened 51005665 Banawe (Del~ Delco In Line
## 9 New o~ Opened 51005669 Megamall RR Mall
## 10 New o~ Opened 51005677 Marcos High~ Delco In Line
## # ... with 210 more rows, and 3 more variables: `Open Date` <chr>, `Close
## # Date` <chr>, geometry <POINT [m]>The report reveals that there area total of 220 point features and 8 fields in store_info simple feature data.frame.
In this section, we will perform GIS operations such as counting points in polygon and overlaying point and polygon by using sf package.
For our project, we are interested to find out the numbers of POI such as restaurant with a geographical unit such as barangay. By combining lengths() and st_intersects(), the code chunk below counts the numbers of selected POI such as restaurant within a barangay and save the result in a newly created field of Barangays simple feature data.frame.
(Barangays$restaurant <- lengths(st_intersects(Barangays, restrnts)))## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [55] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [73] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [91] 0 0 0 0 0 0 0 4 0 0 0 0 0 0 4 5 0 0
## [109] 0 0 4 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0
## [127] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
## [145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## [163] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [181] 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [199] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [217] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## [235] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [253] 0 0 0 0 0 0 1 0 0 0 0 0 3 0 0 1 0 0
## [271] 6 0 0 0 9 2 1 0 0 2 0 0 1 0 0 0 0 3
## [289] 5 6 0 0 6 1 0 16 0 0 0 1 0 0 0 0 0 0
## [307] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [325] 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0
## [343] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [361] 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
## [379] 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0
## [397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [415] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [451] 0 0 0 0 2 1 0 14 0 1 0 0 0 3 0 0 0 0
## [469] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [487] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [505] 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0
## [523] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [541] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [559] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [577] 0 0 0 0 1 0 2 0 0 4 0 0 0 0 1 0 0 0
## [595] 1 0 0 0 0 0 0 0 0 0 3 0 0 0 4 1 0 0
## [613] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [631] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [667] 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
## [685] 1 0 0 0 0 2 0 0 0 3 0 0 0 0 0 0 0 0
## [703] 0 0 5 0 5 5 0 0 0 0 0 0 0 0 0 0 0 0
## [721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [739] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [757] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [775] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [811] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [829] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [847] 0 0 0 1 0 0 0 2 0 0 0 3 0 0 0 0 0 0
## [865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [883] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [901] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [919] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [937] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## [955] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [973] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [991] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1009] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1027] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
## [1045] 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
## [1063] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1081] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [1099] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [1117] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1135] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1153] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1171] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [1189] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [1207] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [1243] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 7
## [1261] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## [1279] 0 1 0 1 0 0 0 0 13 0 17 0 14 0 0 0 0 0
## [1297] 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0
## [1315] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## [1333] 0 0 0 0 0 0 0 1 0 0 0 0 7 0 0 0 0 0
## [1351] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1387] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1405] 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [1423] 0 0 0 0 0 0 0 0 0 7 0 0 0 1 0 1 0 0
## [1441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1459] 0 0 0 0 0 0 0 0 2 0 0 0 0 0 3 0 0 0
## [1477] 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1495] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1513] 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0
## [1531] 0 0 0 0 0 0 0 1 0 2 2 0 0 2 0 0 0 0
## [1549] 1 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0
## [1567] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1603] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## [1621] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [41851] 0The report shows the results of the counting operation. Notice that there are many barangay withouta single restaurant.
We can also reveal the summary statistics of restaurant field by using the code chunk below.
summary(Barangays$restaurant)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.8985 0.0000 908.0000It will be good if we can include administrative information in the store_info simple feature data.frame. The code chunk below can be used to accomplish the objective.
store_info_1 <- store_info %>%
mutate(intersection =as.integer(st_intersects(geometry, Barangays)),
MICROCODE = if_else(is.na(intersection), '', Barangays$MICROCODE[intersection])) %>%
select(-intersection)
store_info_1## Simple feature collection with 220 features and 9 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 207753.7 ymin: 676579.7 xmax: 791102.9 ymax: 2011750
## projected CRS: WGS 84 / UTM zone 51N
## # A tibble: 220 x 10
## Status Milestone `Local Code` `Store Name` `Latest Asset T~ `Facility Type`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Relo ~ Closed 51005648 Delta Delco In Line
## 2 New o~ Opened 51005651 Sm City RR Mall
## 3 New o~ Opened 51005653 C.M. Recto RBD In Line
## 4 New o~ Opened 51005656 Araneta Col~ Express In Line
## 5 New o~ Opened 51005661 Wilson Delco In Line
## 6 New o~ Opened 51005660 Ermita Delco In Line
## 7 New o~ Opened 51005662 Centrepoint RR Mall
## 8 New o~ Opened 51005665 Banawe (Del~ Delco In Line
## 9 New o~ Opened 51005669 Megamall RR Mall
## 10 New o~ Opened 51005677 Marcos High~ Delco In Line
## # ... with 210 more rows, and 4 more variables: `Open Date` <chr>, `Close
## # Date` <chr>, geometry <POINT [m]>, MICROCODE <chr>micro_reference <- read_csv("data/geospatial/Demography_GoLeadDog_Phils/MBI_PH_R2019_Micro_SHP/Micro_reference_cleaned.csv")## Parsed with column specification:
## cols(
## Barangays_CODE = col_character(),
## Barangays_NAME = col_character(),
## Municipal_Cities_CODE = col_character(),
## Municipal_Cities_NAME = col_character()
## )store_info_1<- store_info_1 %>%
left_join(micro_reference,
by = c("MICROCODE" = "Barangays_CODE"))
store_info_1## Simple feature collection with 220 features and 12 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 207753.7 ymin: 676579.7 xmax: 791102.9 ymax: 2011750
## projected CRS: WGS 84 / UTM zone 51N
## # A tibble: 220 x 13
## Status Milestone `Local Code` `Store Name` `Latest Asset T~ `Facility Type`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Relo ~ Closed 51005648 Delta Delco In Line
## 2 New o~ Opened 51005651 Sm City RR Mall
## 3 New o~ Opened 51005653 C.M. Recto RBD In Line
## 4 New o~ Opened 51005656 Araneta Col~ Express In Line
## 5 New o~ Opened 51005661 Wilson Delco In Line
## 6 New o~ Opened 51005660 Ermita Delco In Line
## 7 New o~ Opened 51005662 Centrepoint RR Mall
## 8 New o~ Opened 51005665 Banawe (Del~ Delco In Line
## 9 New o~ Opened 51005669 Megamall RR Mall
## 10 New o~ Opened 51005677 Marcos High~ Delco In Line
## # ... with 210 more rows, and 7 more variables: `Open Date` <chr>, `Close
## # Date` <chr>, geometry <POINT [m]>, MICROCODE <chr>, Barangays_NAME <chr>,
## # Municipal_Cities_CODE <chr>, Municipal_Cities_NAME <chr>