pacman::p_load(sf, tidyverse)Hands-on Exercise 1.1 : Geospatial Data Wrangling with R
Overview
This hands-on exercise covers 1 Geospatial Data Science with R.
Getting Started
1. Install and launching R packages
The code chunk below uses p_load() of pacman package to check if sf and tidyverse packages are installed into the R environment. If they are, then they will be launched into R.
2.1 Importing Geospatial Data
Data are key to data analytics including geospatial analytics. Hence, before analysing, I need to assemble the necessary data. In this hands-on exercise, I am required to extract the necessary data sets from the following sources:
Master Plan 2014 Subzone Boundary (Web) from data.gov.sg
Pre-Schools Location from data.gov.sg
Cycling Path from LTADataMall
2.1.1 Importing polygon feature data in shapefile format - Master Plan 2014 Subzone Boundary (Web)
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. Note that when the input geospatial data is in shapefile format, two arguments will be used, namely: dsn to define the data path and layer to provide the shapefile name.
mpsz = st_read(dsn = "data/geospatial",
layer = "MP14_SUBZONE_WEB_PL")Reading layer `MP14_SUBZONE_WEB_PL' from data source
`C:\Users\user\OneDrive - Singapore Management University\MITB\6. Geospatial Analytics and Applications\jeffleesl\ISSS626-GAA\Hands-on_Ex\Hands-on_Ex01\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: SVY212.1.2 Importing polyline feature data in shapefile form - Cycling Path
The code chunk below uses st_read() function of sf package to import CyclingPath shapefile into R as line feature data frame.
cyclingpath = st_read(dsn = "data/geospatial",
layer = "CyclingPathGazette")Reading layer `CyclingPathGazette' from data source
`C:\Users\user\OneDrive - Singapore Management University\MITB\6. Geospatial Analytics and Applications\jeffleesl\ISSS626-GAA\Hands-on_Ex\Hands-on_Ex01\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 3138 features and 2 fields
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 11854.32 ymin: 28347.98 xmax: 42644.17 ymax: 48948.15
Projected CRS: SVY212.1.3 Importing GIS data in kml format - Pre-Schools Location
The PreSchoolsLocation is in kml format. The code chunk below will be used to import the kml into R. Notice that in the code chunk below, the complete path and the kml file extension were provided.
preschool = st_read("data/geospatial/PreSchoolsLocation.kml")Reading layer `PRESCHOOLS_LOCATION' from data source
`C:\Users\user\OneDrive - Singapore Management University\MITB\6. Geospatial Analytics and Applications\jeffleesl\ISSS626-GAA\Hands-on_Ex\Hands-on_Ex01\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 843 Checking the Content of A Simple Feature Data Frame
In this sub-section, you will learn different ways to retrieve information related to the content of a simple feature data frame.
3.1 Working with st_geometry()
The column in the sf data.frame that contains the geometries is a list, of class sfc. We can retrieve the geometry list-column in this case by mpsz$geom or mpsz[[1]], but the more general way uses 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...3.2 Working with glimpse()
Beside the basic feature information, we also would like to learn more about the associated attribute information in the data frame. This is the time I will find glimpse() of dplyr. very handy as shown in the code chunk below.
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 (…3.3 Working with head()
Sometimes we would like to reveal complete information of a feature object, this is the job of head() of Base R
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...The result returns the first 5 objects by default, but the number of objects can be adjusted by specifying a value for n.
4 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. This is the time I will find plot() of R Graphic comes in very handy as shown in the code chunk below.
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"])
5 Working with Projection
Map projection is an important property of a geospatial data. In order to perform geoprocessing using two geospatial data, we need to ensure that both geospatial data are projected using similar coordinate system.
In this section, I will learn how to project a simple feature data frame from one coordinate system to another coordinate system. The technical term of this process is called projection transformation.
5.1 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. (The common issue I saw in my industry is that some analyst didn’t identify this issue and they don’t even know the correct code).
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)Now, let us check the CSR again by using the code chunk below.
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.
5.2 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. The print below reveals that it is in wgs84 coordinate system.
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)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 mathemetically.
Let us perform the projection transformation by using the code chunk below.
preschool3414 <- st_transform(preschool,
crs = 3414)Next, let us display the content of preschool3414 sf data frame as shown below.
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. Furthermore, if you refer to Bounding box:, the values are greater than 0-360 range of decimal degree commonly used by most of the geographic coordinate systems.
#6 Importing and Converting An Aspatial Data
In this hands-on exercise, I am required to extract the necessary data sets from the following sources:
- Latest version of Singapore Airbnb listing data from Inside Airbnb
In practice, it is not unusual that we will come across data such as listing of Inside Airbnb. We call this kind of data 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.
In this section, you will learn how to import an aspatial data into R environment and save it as a tibble data frame. Next, you will convert it into a simple feature data frame.
For the purpose of this exercise, the listings.csv data downloaded from AirBnb will be used.
6.1 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: 3540 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: 3,540 × 18
id name host_id host_name neighbourhood_group neighbourhood latitude
<dbl> <chr> <dbl> <chr> <chr> <chr> <dbl>
1 71609 Ensuite … 367042 Belinda East Region Tampines 1.35
2 71896 B&B Roo… 367042 Belinda East Region Tampines 1.35
3 71903 Room 2-n… 367042 Belinda East Region Tampines 1.35
4 275343 10min wa… 1439258 Kay Central Region Bukit Merah 1.29
5 275344 15 mins … 1439258 Kay Central Region Bukit Merah 1.29
6 289234 Booking … 367042 Belinda East Region Tampines 1.34
7 294281 5 mins w… 1521514 Elizabeth Central Region Newton 1.31
8 324945 Comforta… 1439258 Kay Central Region Bukit Merah 1.29
9 330095 Relaxing… 1439258 Kay Central Region Bukit Merah 1.29
10 344803 Budget s… 367042 Belinda East Region Tampines 1.35
# ℹ 3,530 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 4252 rows and 16 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.
6.2 Creating a simple feature data frame from an aspatial data frame
The code chunk below 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)Things to learn from the arguments above:
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.
Let us examine the content of this newly created simple feature data frame.
glimpse(listings_sf)Rows: 3,540
Columns: 17
$ id <dbl> 71609, 71896, 71903, 275343, 275344, 28…
$ name <chr> "Ensuite Room (Room 1 & 2) near EXPO", …
$ 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> NA, 80, 80, 50, 50, NA, 85, 65, 45, 54,…
$ minimum_nights <dbl> 92, 92, 92, 180, 180, 92, 92, 180, 180,…
$ number_of_reviews <dbl> 19, 24, 46, 20, 16, 12, 131, 17, 5, 60,…
$ last_review <date> 2020-01-17, 2019-10-13, 2020-01-09, 20…
$ reviews_per_month <dbl> 0.12, 0.15, 0.29, 0.15, 0.11, 0.08, 0.8…
$ calculated_host_listings_count <dbl> 6, 6, 6, 49, 49, 6, 7, 49, 49, 6, 7, 7,…
$ availability_365 <dbl> 89, 148, 90, 62, 0, 88, 365, 0, 0, 365,…
$ number_of_reviews_ltm <dbl> 0, 0, 0, 0, 2, 0, 0, 1, 1, 1, 0, 0, 0, …
$ license <chr> NA, NA, NA, "S0399", "S0399", NA, NA, "…
$ geometry <POINT [m]> POINT (41972.5 36390.05), POINT (…Table above shows the content of listing_sf. 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.
7 Geoprocessing with sf package
Besides providing functions to handling (i.e. importing, exporting, assigning projection, transforming projection etc) geospatial data, sf package also offers a wide range of geoprocessing (also known as GIS analysis) functions.
In this section, you will learn how to perform two commonly used geoprocessing functions, namely buffering and point in polygon count.
7.1 Buffering
The scenario:
The authority is planning to upgrade the exiting cycling path. To do so, they need to acquire 5 metres of reserved land on the both sides of the current cycling path. You are tasked to determine the extend of the land need to be acquired and their total area.
The 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)This is followed by calculating the area of the buffers as shown in the code chunk below.
buffer_cycling$AREA <- st_area(buffer_cycling)Lastly, sum() of Base R will be used to derive the total land involved
sum(buffer_cycling$AREA)2218855 [m^2]Mission Accomplished!
7.2 Point-in-polygon count
The scenario:
A pre-school service group want to find out the numbers of pre-schools in each Planning Subzone.
The solution:
The code chunk below performs two operations at one go. Firstly, identify pre-schools located inside each Planning Subzone by using st_intersects(). Next, length() of Base R is used to calculate numbers of pre-schools that fall inside each planning subzone.
mpsz3414$`PreSch Count`<- lengths(st_intersects(mpsz3414, preschool3414))Warning: You should not confuse with st_intersection().
You can check the summary statistics of the newly derived PreSch Count field by using summary() as shown in the code chunk below.
summary(mpsz3414$`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-school, the top_n() of dplyr package is used as shown in the code chunk below.
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... 72The solution:
Firstly, the code chunk below uses st_area() of sf package to derive the area of each planning subzone.
mpsz3414$Area <- mpsz3414 %>%
st_area()Next, mutate() of dplyr package is used to compute the density by using the code chunk below.
mpsz3414 <- mpsz3414 %>%
mutate(`PreSch Density` = `PreSch Count`/Area * 1000000)8. Exploratory Data Analysis (EDA)
In practice, many geospatial analytics start with Exploratory Data Analysis. In this section, you will learn how to use appropriate ggplot2 functions to create functional and yet truthful statistical graphs for EDA purposes.
Firstly, we will plot a histogram to reveal the distribution of PreSch Density. Conventionally, hist() of R Graphics will be used as shown in the code chunk below.
hist(mpsz3414$`PreSch Density`)
Although the syntax is very easy to use however the output is far from meeting publication quality. Furthermore, the function has limited room for further customisation.
In the code chunk below, appropriate ggplot2 functions will be used.
ggplot(data=mpsz3414,
aes(x= as.numeric(`PreSch Density`)))+
geom_histogram(bins=20,
color="black",
fill="light blue") +
labs(title = "Are pre-school 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")
The solution: Using ggplot2 method, 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 or values outside the scale range
(`geom_point()`).