pacman::p_load(sf, tidyverse)Hands-on Exercise 1A: Geospatial Data Wrangling with R
Overview
In this hands-on exercise, I learn how to import and wrangle geospatial data using appropriate R packages.
Getting Started
In this hands-on exercise, two R packages will be used. They are:
sf for importing, managing, and processing geospatial data, and
tidyverse for performing data science tasks such as importing, wrangling and visualising data (not specific to geospatial data).
The code chunk below installs and load sf and tidyverse packages into R environment.
Furthermore, the tidyverse package consists of a family of R packages. In this hands-on exercise, the following packages will be used:
readr for importing csv data,
readxl for importing Excel worksheet,
tidyr for manipulating data,
dplyr for transforming data, and
ggplot2 for visualising data
Note: In the above code chunk, p_load function pf pacman package is used to install and load sf and tidyverse packages into R environment.
Data Sources
We will extract the following data sets from these sources:
Master Plan 2014 Subzone Boundary (Web) from data.gov.sg
Pre-Schools Location from data.gov.sg
Cycling Path from LTADataMall
Latest version of Singapore Airbnb listing data from Inside Airbnb
Extracting the geospatial data sets
At the Hands-on_Ex1 folder, we create a sub-folder called data. Then, inside the data sub-folder, we create two sub-folders and name them geospatial and aspatial respectively.
We place Master Plan 2014 Subzone Boundary (Web), Pre-Schools Location, and Cycling Path zipped files into the geospatial sub-folder and unzip them. Then, copy the unzipped files from their respective sub-folders and place them inside geospatial sub-folder.
Extracting the aspatial data set
We place the Singapore AirBnB listing data (listing.csv) into the aspatial sub-folder.
Importing Geospatial Data
In this section, we will learn how to import the following geospatial data into R by using st_read() of sf package:
MP14_SUBZONE_WEB_PL, a polygon feature layer in ESRI shapefile format,CyclingPath, a line feature layer in ESRI shapefile format, andPreSchool, a point feature layer in kml file format.
Importing polygon feature data
The code chunk below uses st_read() function of sf package to import MP14_SUBZONE_WEB_PL shapefile into R as a polygon feature data frame. When the input geospatial data is in shapefile format, two arguments will be used:
dsnto define the data pathlayerto provide the shapefile name
No extension such as .shp, .dbf, .prj and .shx are needed.
mpsz <- st_read(dsn = "data/geospatial", layer = "MP14_SUBZONE_WEB_PL")Reading layer `MP14_SUBZONE_WEB_PL' from data source
`C:\PeiShan0502\ISSS624\Hands-on_Ex1\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21The message above reveals that the geospatial objects are multipolygon features. There are a total of 323 multipolygon features and 15 fields in mpsz simple feature data frame. mpsz is in svy21 projected coordinates systems. The bounding box provides the x extend and y extend of the data.
Importing polyline feature data in shapefile form
The code chunk below uses st_read() function of sf package to import Cycling Path shapefile into R as line feature data frame.
cyclingpath = st_read(dsn = 'data/geospatial',
layer = 'CyclingPathGazette')Reading layer `CyclingPathGazette' from data source
`C:\PeiShan0502\ISSS624\Hands-on_Ex1\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 2558 features and 2 fields
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 11854.32 ymin: 28347.98 xmax: 42626.09 ymax: 48948.15
Projected CRS: SVY21The message above reveals that there are a total of 2558 features and 2 fields in cyclingpath linestring feature data frame and it is in svy21 projected coordinates system too.
Importing GIS data in kml format
The PreSchoolsLocation is in kml format. The code chunk below will be used to import it into R. Notice that the kml file extension is provided:
preschool = st_read("data/geospatial/PreSchoolsLocation.kml")Reading layer `PRESCHOOLS_LOCATION' from data source
`C:\PeiShan0502\ISSS624\Hands-on_Ex1\data\geospatial\PreSchoolsLocation.kml'
using driver `KML'
Simple feature collection with 2290 features and 2 fields
Geometry type: POINT
Dimension: XYZ
Bounding box: xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range: zmin: 0 zmax: 0
Geodetic CRS: WGS 84The message above reveals that preschool is a point feature data frame. There are a total of 2290 features and 2 fields. preschool is also in wgs84 coordinates system (different from the previous two simple feature data frame).
Checking the Content of a Simple Feature Data Frame
In this sub-section, we will look at different ways to retrieve information related to the content of a single feature data frame.
Working with st_geometry()
The column in the sf data frame that contains the geometries is a list, of class sfc. We can retrieve this list by using st_geometry() as shown in the code chunk below:
st_geometry(mpsz)Geometry set for 323 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21
First 5 geometries:MULTIPOLYGON (((31495.56 30140.01, 31980.96 296...MULTIPOLYGON (((29092.28 30021.89, 29119.64 300...MULTIPOLYGON (((29932.33 29879.12, 29947.32 298...MULTIPOLYGON (((27131.28 30059.73, 27088.33 297...MULTIPOLYGON (((26451.03 30396.46, 26440.47 303...Working with glimpse()
We use glimpse() of the dplyr package to learn more about the associated attribute information in the data frame:
glimpse(mpsz)Rows: 323
Columns: 16
$ OBJECTID <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
$ SUBZONE_NO <int> 1, 1, 3, 8, 3, 7, 9, 2, 13, 7, 12, 6, 1, 5, 1, 1, 3, 2, 2, …
$ SUBZONE_N <chr> "MARINA SOUTH", "PEARL'S HILL", "BOAT QUAY", "HENDERSON HIL…
$ SUBZONE_C <chr> "MSSZ01", "OTSZ01", "SRSZ03", "BMSZ08", "BMSZ03", "BMSZ07",…
$ CA_IND <chr> "Y", "Y", "Y", "N", "N", "N", "N", "Y", "N", "N", "N", "N",…
$ PLN_AREA_N <chr> "MARINA SOUTH", "OUTRAM", "SINGAPORE RIVER", "BUKIT MERAH",…
$ PLN_AREA_C <chr> "MS", "OT", "SR", "BM", "BM", "BM", "BM", "SR", "QT", "QT",…
$ REGION_N <chr> "CENTRAL REGION", "CENTRAL REGION", "CENTRAL REGION", "CENT…
$ REGION_C <chr> "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR",…
$ INC_CRC <chr> "5ED7EB253F99252E", "8C7149B9EB32EEFC", "C35FEFF02B13E0E5",…
$ FMEL_UPD_D <date> 2014-12-05, 2014-12-05, 2014-12-05, 2014-12-05, 2014-12-05…
$ X_ADDR <dbl> 31595.84, 28679.06, 29654.96, 26782.83, 26201.96, 25358.82,…
$ Y_ADDR <dbl> 29220.19, 29782.05, 29974.66, 29933.77, 30005.70, 29991.38,…
$ SHAPE_Leng <dbl> 5267.381, 3506.107, 1740.926, 3313.625, 2825.594, 4428.913,…
$ SHAPE_Area <dbl> 1630379.27, 559816.25, 160807.50, 595428.89, 387429.44, 103…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((31495.56 30..., MULTIPOLYGON (…glimpse() report reveals the data type of each fields. For example FMEL-UPD_D field is in date data type and X_ADDR, Y_ADDR, SHAPE_L and SHAPE_AREA fields are all in double-precision values.
Working with head()
We use head() to reeal complete information of a feature object:
head(mpsz, n=5)Simple feature collection with 5 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 25867.68 ymin: 28369.47 xmax: 32362.39 ymax: 30435.54
Projected CRS: SVY21
OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
1 1 1 MARINA SOUTH MSSZ01 Y MARINA SOUTH
2 2 1 PEARL'S HILL OTSZ01 Y OUTRAM
3 3 3 BOAT QUAY SRSZ03 Y SINGAPORE RIVER
4 4 8 HENDERSON HILL BMSZ08 N BUKIT MERAH
5 5 3 REDHILL BMSZ03 N BUKIT MERAH
PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
1 MS CENTRAL REGION CR 5ED7EB253F99252E 2014-12-05 31595.84
2 OT CENTRAL REGION CR 8C7149B9EB32EEFC 2014-12-05 28679.06
3 SR CENTRAL REGION CR C35FEFF02B13E0E5 2014-12-05 29654.96
4 BM CENTRAL REGION CR 3775D82C5DDBEFBD 2014-12-05 26782.83
5 BM CENTRAL REGION CR 85D9ABEF0A40678F 2014-12-05 26201.96
Y_ADDR SHAPE_Leng SHAPE_Area geometry
1 29220.19 5267.381 1630379.3 MULTIPOLYGON (((31495.56 30...
2 29782.05 3506.107 559816.2 MULTIPOLYGON (((29092.28 30...
3 29974.66 1740.926 160807.5 MULTIPOLYGON (((29932.33 29...
4 29933.77 3313.625 595428.9 MULTIPOLYGON (((27131.28 30...
5 30005.70 2825.594 387429.4 MULTIPOLYGON (((26451.03 30...Note: n=5 specifies the number of records to display.
Plotting the Geospatial Data
In geospatial data science, by looking at the feature information is not enough. We are also interested to visualise the geospatial features. We use plot() of R Graphic to do this:
plot(mpsz)Warning: plotting the first 9 out of 15 attributes; use max.plot = 15 to plot
all
The default plot of an sf object is a multi-plot of all attributes, up to a reasonable maximum as shown above. We can, however, choose to plot only the geometry by using the code chunk below:
plot(st_geometry(mpsz))
Alternatively, we can also choose the plot the sf object by using a specific attribute as shown in the code chunk below:
plot(mpsz['PLN_AREA_N'])
Note: plot() is meant for plotting the geospatial object for quick look.
Working with Projection
To perform geoprocessing using two geospatial data, need to ensure both geospatial data are projected using similar coordinate system.
Here we will learn how to project a simple feature data frame from one coordinate system to another coordinate system. This is known as projection transformation.
Assigning EPSG code to a simple feature data frame
One of the common issue that can happen during importing geospatial data into R is that the coordinate system of the source data was either missing (such as due to missing .proj for ESRI shapefile) or wrongly assigned during the importing process.
This is an example the coordinate system of mpsz simple feature data frame by using st_crs() of sf package as shown in the code chunk below:
st_crs(mpsz)Coordinate Reference System:
User input: SVY21
wkt:
PROJCRS["SVY21",
BASEGEOGCRS["SVY21[WGS84]",
DATUM["World Geodetic System 1984",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ID["EPSG",6326]],
PRIMEM["Greenwich",0,
ANGLEUNIT["Degree",0.0174532925199433]]],
CONVERSION["unnamed",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]]]Although mpsz data frame is projected in svy21 but when we read until the end of the print, it indicates that the EPSG is 9001. This is a wrong EPSG code because the correct EPSG code for svy21 should be 3414.
In order to assign the correct EPSG code to mpsz data frame, st_set_crs() of sf package is used as shown in the code chunk below:
mpsz3414 <- st_set_crs(mpsz, 3414)Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
thatThen let’s check the CSR again:
st_crs(mpsz3414)Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]Notice that the EPSG code is 3414 now.
Transforming the projection of preschool from wgs84 to svy21
In geospatial analytics, it is very common for us to transform the original data from geographic coordinate system to projected coordinate system. This is because geographic coordinate system is not appropriate if the analysis need to use distance or/and area measurements.
Let us take preschool simple feature data frame as an example. Running the code chunk below reveals that it is in wgs84 coordinate system:
st_geometry(preschool)Geometry set for 2290 features
Geometry type: POINT
Dimension: XYZ
Bounding box: xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range: zmin: 0 zmax: 0
Geodetic CRS: WGS 84
First 5 geometries:POINT Z (103.8072 1.299333 0)POINT Z (103.826 1.312839 0)POINT Z (103.8409 1.348843 0)POINT Z (103.8048 1.435024 0)POINT Z (103.839 1.33315 0)This is a scenario that st_set_crs() is not appropriate and st_transform() of sf package should be used. This is because we need to reproject preschool from one coordinate system to another coordinate system mathematically.
Let us perform the projection transformation by using the code chunk below:
preschool3414 <- st_transform(preschool, crs = 3414)Now displaying the content of preschool3414 sf data frame by running the code chunk below:
st_geometry(preschool3414)Geometry set for 2290 features
Geometry type: POINT
Dimension: XYZ
Bounding box: xmin: 11810.03 ymin: 25596.33 xmax: 45404.24 ymax: 49300.88
z_range: zmin: 0 zmax: 0
Projected CRS: SVY21 / Singapore TM
First 5 geometries:POINT Z (25089.46 31299.16 0)POINT Z (27189.07 32792.54 0)POINT Z (28844.56 36773.76 0)POINT Z (24821.92 46303.16 0)POINT Z (28637.82 35038.49 0)Notice that it is in svy21 projected coordinate system now.
Importing and Converting An Aspatial Data
For the Singapore Airbnb listing data, this is aspatial data. This is because it is not a geospatial data but among the data fields, there are two fields that capture the x- and y-coordinates of the data points.
We will learn how to import an aspatial data into R environment and save it as a tibble data frame. Then convert it into a simple feature data frame.
The listings.csv data downloaded from Airbnb will be used.
Importing the aspatial data
Since listings data set is in csv file format, we will use read_csv() of readr package to import listing.csv as shown the code chunk below. The output R object is called listings and it is a tibble data frame.
listings <- read_csv('data/aspatial/listings.csv')Rows: 3483 Columns: 18
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): name, host_name, neighbourhood_group, neighbourhood, room_type, l...
dbl (11): id, host_id, latitude, longitude, price, minimum_nights, number_o...
date (1): last_review
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.After importing the data file into R, it is important for us to examine if the data file has been imported correctly.
The code chunk below shows list() of Base R instead of glimpse() is used to do the job.
list(listings)[[1]]
# A tibble: 3,483 × 18
id name host_id host_name neighbourhood_group neighbourhood latitude
<dbl> <chr> <dbl> <chr> <chr> <chr> <dbl>
1 71609 Villa in… 367042 Belinda East Region Tampines 1.35
2 71896 Home in … 367042 Belinda East Region Tampines 1.35
3 71903 Home in … 367042 Belinda East Region Tampines 1.35
4 275343 Rental u… 1439258 Kay Central Region Bukit Merah 1.29
5 275344 Rental u… 1439258 Kay Central Region Bukit Merah 1.29
6 289234 Home in … 367042 Belinda East Region Tampines 1.34
7 294281 Rental u… 1521514 Elizabeth Central Region Newton 1.31
8 324945 Rental u… 1439258 Kay Central Region Bukit Merah 1.29
9 330095 Rental u… 1439258 Kay Central Region Bukit Merah 1.29
10 369141 Place to… 1521514 Elizabeth Central Region Newton 1.31
# ℹ 3,473 more rows
# ℹ 11 more variables: longitude <dbl>, room_type <chr>, price <dbl>,
# minimum_nights <dbl>, number_of_reviews <dbl>, last_review <date>,
# reviews_per_month <dbl>, calculated_host_listings_count <dbl>,
# availability_365 <dbl>, number_of_reviews_ltm <dbl>, license <chr>The output reveals that listing tibble data frame consists of 3,483 rows and 18 columns. Two useful fields we are going to use in the next phase are latitude and longitude. Note that they are in decimal degree format. As a best guess, we will assume that the data is in wgs84 Geographic Coordinate System.
Creating a simple feature data frame from an aspatial data frame
This converts listing data frame into a simple feature data frame by using st_as_sf() of sf packages:
listings_sf <- st_as_sf(listings, coords = c('longitude', 'latitude'),
crs=4326) %>%
st_transform(crs = 3414)Useful notes:
coords argument requires you to provide the column name of the x-coordinates first then followed by the column name of the y-coordinates.
crs argument requires you to provide the coordinates system in epsg format. EPSG: 4326 is wgs84 Geographic Coordinate System and EPSG: 3414 is Singapore SVY21 Projected Coordinate System. You can search for other country’s epsg code by referring to epsg.io.
%>% is used to nest st_transform() to transform the newly created simple feature data frame into svy21 projected coordinates system.
We examine the content of this newly created simple feature data frame:
glimpse(listings_sf)Rows: 3,483
Columns: 17
$ id <dbl> 71609, 71896, 71903, 275343, 275344, 28…
$ name <chr> "Villa in Singapore · ★4.44 · 2 bedroom…
$ host_id <dbl> 367042, 367042, 367042, 1439258, 143925…
$ host_name <chr> "Belinda", "Belinda", "Belinda", "Kay",…
$ neighbourhood_group <chr> "East Region", "East Region", "East Reg…
$ neighbourhood <chr> "Tampines", "Tampines", "Tampines", "Bu…
$ room_type <chr> "Private room", "Private room", "Privat…
$ price <dbl> 150, 80, 80, 55, 69, 220, 85, 75, 45, 7…
$ minimum_nights <dbl> 92, 92, 92, 60, 60, 92, 92, 60, 60, 92,…
$ number_of_reviews <dbl> 20, 24, 47, 22, 17, 12, 133, 18, 6, 81,…
$ last_review <date> 2020-01-17, 2019-10-13, 2020-01-09, 20…
$ reviews_per_month <dbl> 0.14, 0.16, 0.31, 0.17, 0.12, 0.09, 0.9…
$ calculated_host_listings_count <dbl> 5, 5, 5, 52, 52, 5, 7, 52, 52, 7, 7, 1,…
$ availability_365 <dbl> 89, 89, 89, 275, 274, 89, 365, 365, 365…
$ number_of_reviews_ltm <dbl> 0, 0, 0, 0, 3, 0, 0, 1, 3, 0, 0, 0, 0, …
$ license <chr> NA, NA, NA, "S0399", "S0399", NA, NA, "…
$ geometry <POINT [m]> POINT (41972.5 36390.05), POINT (…Notice that a new column called geometry has been added into the data frame. On the other hand, the longitude and latitude columns have been dropped from the data frame.
Geoprocessing with sf package
In this section, you will learn how to perform two commonly used geoprocessing functions, namely buffering and point in polygon count.
Buffering
Consider the following scenario: The authority is planning to upgrade the existing cycling path. To do so, they need to acquire 5 metres of reserved land on both sides of the current cycling path. You are tasked to determine the extent of the land required to be acquired and their total area.
Solution:
Firstly, st_buffer() of sf package is used to compute the 5-meter buffers around cycling paths
buffer_cycling <- st_buffer(cyclingpath, dist=5, nQuadSegs = 30)Then we calculate the area of the buffers:
buffer_cycling$AREA <- st_area(buffer_cycling)Lastly, derive the total land involved:
sum(buffer_cycling$AREA)1774367 [m^2]Mission Accomplished!!! ^_^
Point-in-polygon count
How to find the number of pre-schools in each Planning Subzone?
The code chunk below first identifies pre-schools located inside each planning subzone by using st_intersects(), then length() is used to calculate the number of pre-schools that fall inside each planning subzone.
mpsz3414$`PreSch Count` <- lengths(st_intersects(mpsz3414, preschool3414))Note: don’t confuse with st_intersection().
To check summary statistics of the newly derived PreSch Count field:
summary(mpsz3414$`PreSch Count`) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 0.00 4.00 7.09 10.00 72.00 To list the planning subzone with the most number of pre-schools, the top_n() of dplyr package is used:
top_n(mpsz3414, 1, `PreSch Count`)Simple feature collection with 1 feature and 16 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 39655.33 ymin: 35966 xmax: 42940.57 ymax: 38622.37
Projected CRS: SVY21 / Singapore TM
OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N PLN_AREA_C
1 189 2 TAMPINES EAST TMSZ02 N TAMPINES TM
REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR Y_ADDR SHAPE_Leng
1 EAST REGION ER 21658EAAF84F4D8D 2014-12-05 41122.55 37392.39 10180.62
SHAPE_Area geometry PreSch Count
1 4339824 MULTIPOLYGON (((42196.76 38... 72To calculate the density of pre-schools by planning subzone:
- Derive the area of each planning subzone
mpsz3414$Area <- mpsz3414 %>%
st_area()mpsz3414 <- mpsz3414 %>%
mutate(`PreSch Density` = `PreSch Count`/Area * 1000000)Exploratory Data Analysis (EDA)
Here, we will learn how to use appropriate ggplot2 functions to create graphs for EDA purposes.
Firstly, we will plot a histogram to reveal the distribution of PreSch Density. Conventionally, hist() of R Graphics will be used as shown in the code chunk below.
hist(mpsz3414$`PreSch Density`)
This is easy to use but the output is far from presentable and this function has limited room for further customisation.
In the code chunk below, we use appropriate ggplot2 functions:
ggplot(data=mpsz3414,
aes(x= as.numeric(`PreSch Density`)))+
geom_histogram(bins=20,
color="black",
fill="light blue") +
labs(title = "Are pre-schools even distributed in Singapore?",
subtitle= "There are many planning sub-zones with a single pre-school, on the other hand, \nthere are two planning sub-zones with at least 20 pre-schools",
x = "Pre-school density (per km sq)",
y = "Frequency")
We use ggplot2 method to plot a scatterplot showing the relationship between Pre-school Density and Pre-school count:
ggplot(data=mpsz3414,
aes(y = `PreSch Count`,
x= as.numeric(`PreSch Density`)))+
geom_point(color="black",
fill="light blue") +
xlim(0, 40) +
ylim(0, 40) +
labs(title = "",
x = "Pre-school density (per km sq)",
y = "Pre-school count")Warning: Removed 2 rows containing missing values (`geom_point()`).