pacman::p_load(sf, tmap, tidyverse)Hands-on Exercise 1.2: Choropleth Mapping with R
Overview
Choropleth mapping involves the symbolisation of enumeration units (such as countries, provinces, states, counties or census units) using area patterns or graduated colors.
In this hands-on exercise, I will learn to plot functional and truthful choropleth maps by using tmap package.
Installing & Launching R Packages
Beside tmap package, four other R packages will be used:
readr for importing delimited text file
tidyr for tidying data
dplyr for wrangling data
sf for handling geospatial data
Among the four packages, readr, tidyr and dplyr are part of tidyverse package.
The code chunk below installs and loads these packages into R environment.
Importing Data into R
The Data
Two data set will be used to create the choropleth map. They are:
Master Plan 2014 Subzone Boundary (Web) (i.e.
MP14_SUBZONE_WEB_PL) in ESRI shapefile format. It can be downloaded at data.gov.sg This is a geospatial data. It consists of the geographical boundary of Singapore at the Planning Subzone level. The data is based on URA Master Plan 2014.Singapore Residents by Planning Area / Subzone, Age Group, Sex and Type of Dwelling, June 2011-2020 in csv format (i.e.
respopagesextod2011to2020.csv). This is an aspatial data file. It can be downloaded at Department of Statistics, Singapore. Although it does not contain any coordinates values, but its PA and SZ fields can be used as unique identifiers to geocode toMP14_SUBZONE_WEB_PLshapefile.
Importing Geospatial Data into R
The code chunk below uses the st_read() function of sf package to import MP14_SUBZONE_WEB_PL shapefile into R as a simple feature data frame called mpsz.
mpsz <- st_read(dsn = "data/geospatial",
layer = "MP14_SUBZONE_WEB_PL")Reading layer `MP14_SUBZONE_WEB_PL' from data source
`C:\Jacobche\ISSS624\Hands-on_Ex\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: SVY21We can examine the content of mpsz by using the code chunk below.
mpszSimple 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: SVY21
First 10 features:
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
6 6 7 ALEXANDRA HILL BMSZ07 N BUKIT MERAH
7 7 9 BUKIT HO SWEE BMSZ09 N BUKIT MERAH
8 8 2 CLARKE QUAY SRSZ02 Y SINGAPORE RIVER
9 9 13 PASIR PANJANG 1 QTSZ13 N QUEENSTOWN
10 10 7 QUEENSWAY QTSZ07 N QUEENSTOWN
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
6 BM CENTRAL REGION CR 9D286521EF5E3B59 2014-12-05 25358.82
7 BM CENTRAL REGION CR 7839A8577144EFE2 2014-12-05 27680.06
8 SR CENTRAL REGION CR 48661DC0FBA09F7A 2014-12-05 29253.21
9 QT CENTRAL REGION CR 1F721290C421BFAB 2014-12-05 22077.34
10 QT CENTRAL REGION CR 3580D2AFFBEE914C 2014-12-05 24168.31
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...
6 29991.38 4428.913 1030378.8 MULTIPOLYGON (((25899.7 297...
7 30230.86 3275.312 551732.0 MULTIPOLYGON (((27746.95 30...
8 30222.86 2208.619 290184.7 MULTIPOLYGON (((29351.26 29...
9 29893.78 6571.323 1084792.3 MULTIPOLYGON (((20996.49 30...
10 30104.18 3454.239 631644.3 MULTIPOLYGON (((24472.11 29...Importing Attribute Data into R
The code chunk below uses read_csv() function of readr package to import the attribute data into RStudio and save the file into an R data frame called popagsex.
popdata <- read_csv("data/aspatial/respopagesextod2011to2020.csv")Rows: 984656 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): PA, SZ, AG, Sex, TOD
dbl (2): Pop, Time
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.Data Preparation
Before a thematic map can be prepared, we are required to prepare a data table with year 2020 values. The data table should include the variables PA, SZ, YOUNG, ECONOMY ACTIVE, AGED, TOTAL, DEPENDENCY.
YOUNG: age group 0 to 4 until age groyup 20 to 24,ECONOMY ACTIVE: age group 25-29 until age group 60-64,AGED: age group 65 and above,TOTAL: all age group, andDEPENDENCY: the ratio between young and aged against economy active group
Data wrangling
The code chunk below uses pivot_wider() of tidyr package as well as mutate(), filter(), group_by() and select() of dplyr package for data wrangling and transformation.
popdata2020 <- popdata %>%
filter(Time == 2020) %>%
group_by(PA, SZ, AG) %>%
summarise(`POP` = sum(`Pop`)) %>%
ungroup()%>%
pivot_wider(names_from=AG,
values_from=POP) %>%
mutate(YOUNG = rowSums(.[3:6])
+rowSums(.[12])) %>%
mutate(`ECONOMY ACTIVE` = rowSums(.[7:11])+
rowSums(.[13:15]))%>%
mutate(`AGED`=rowSums(.[16:21])) %>%
mutate(`TOTAL`=rowSums(.[3:21])) %>%
mutate(`DEPENDENCY` = (`YOUNG` + `AGED`)
/`ECONOMY ACTIVE`) %>%
select(`PA`, `SZ`, `YOUNG`,
`ECONOMY ACTIVE`, `AGED`,
`TOTAL`, `DEPENDENCY`)`summarise()` has grouped output by 'PA', 'SZ'. You can override using the
`.groups` argument.Joining the attribute data and geospatial data
Before we can perform the georelational join, one extra step is required. That is to convert the values in PA and SZ fields to uppercase in popdata2020 since they are in upper- and lowercase originally. Whereas, the values in SUBZONE_N and PLN_AREA_N from mpsz are all in uppercase.
popdata2020 <- popdata2020 %>%
mutate_at(.vars = vars(PA, SZ),
.funs = funs(toupper)) %>%
filter(`ECONOMY ACTIVE` > 0)Warning: `funs()` was deprecated in dplyr 0.8.0.
ℹ Please use a list of either functions or lambdas:
# Simple named list: list(mean = mean, median = median)
# Auto named with `tibble::lst()`: tibble::lst(mean, median)
# Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))The code chunk below uses left_join() of dplyr to join the geographical data and attribute table with SUBZONE_N and SZ as the common identifier.
mpsz_pop2020 <- left_join(mpsz, popdata2020,
by = c("SUBZONE_N" = "SZ"))Note that left_join() of dplyr package is used with mpsz simple feature data frame as the left data table, which is to ensure that the output will be a simple feature data frame.
mpsz_pop2020 is being written as a rds file using the code chunk below.
write_rds(mpsz_pop2020, "data/rds/mpszpop2020.rds")Choropleth Mapping Geospatial Data Using tmap
Two approaches can be used to prepare thematic map using tmap:
Plotting a thematic map quickly by using qtm()
Plotting a highly customisable thematic map by using tmap elements
Plotting a choropleth map quickly by using qtm()
The code chunk below uses qtm() to draw a cartographic standard choropleth map, which serves as a good default visualisation.
tmap_mode("plot")tmap mode set to plottingqtm(mpsz_pop2020,
fill = "DEPENDENCY")
Things to lnote:
tmap_mode() with “plot” option is used to produce a static map. For interactive mode, “view” option should be used.
fill argument is used to map the attribute (i.e.
DEPENDENCY)
Creating a choropleth map by using tmap elements
Despite its usefulness in drawing a choropleth map quickly and easily, the disadvantge of qtm() is that it makes aesthetics of individual layers harder to control.
To draw a high quality cartographic choropleth map, tmap drawing elements should be used as below.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
title = "Dependency Ratio") +
tm_layout(main.title = "Distribution of Dependency Ratio by Planning Subzone",
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_compass(type="8star", size = 2, position = c("left", "top")) +
tm_scale_bar(position = c("right", "top")) +
tm_grid(alpha =0.2) +
tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and Population data from Department of Statistics DOS",
position = c("left", "bottom"))
In the following sub-section, we will look at some of the tmap functions that are used to plot these elements.
Drawing a base map
The basic building block of tmap is tm_shape() followed by one or more layer elements such as tm_fill() and tm_polygons().
In the code chunk below, tm_shape() is used to define the input data (i.e mpsz_pop2020) and tm_polygons() is used to draw the planning subzone polygons
tm_shape(mpsz_pop2020) +
tm_polygons()
Drawing a choropleth map using tm_polygons()
To draw a choropleth map showing the geographical distribution of a selected variable by planning subzone, we just need to assign the target variable such as Dependency to tm_polygons().
tm_shape(mpsz_pop2020)+
tm_polygons("DEPENDENCY")
Things to learn from tm_polygons():
The default interval binning used to draw the choropleth map is called “pretty”. A detailed discussion of the data classification methods supported by tmap will be provided in later sub-section on Data classification methods of tmap.
The default colour scheme used is
YlOrRdof ColorBrewer. We will learn more about this in later sub-section on Colour Scheme.By default, Missing value will be shaded in grey.
Drawing a choropleth map using tm_fill() and tm_border()
Actually, tm_polygons() is a wrapper of tm_fill() and tm_border(). tm_fill() shades the polygons by using the default colour scheme while tm_borders() adds the borders of the shapefile onto the choropleth map.
The code chunk below draws a choropleth map by using tm_fill() alone.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY")
Notice that the planning subzones are shaded according to the respective dependency values.
To add the boundary of the planning subzones, tm_borders() will be used as shown in the code chunk below.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY") +
tm_borders(lwd = 0.1, alpha = 1)
Notice that light-gray border lines have been added on the choropleth map.
The alpha argument is used to define transparency number between 0 (totally transparent) and 1 (not transparent). By default, the alpha value of the col is used (normally 1).
Beside alpha argument, there are three other arguments for tm_borders():
col = border colour
lwd = border line width where the default is 1
lty = border line type where the default is “solid”
Data classification methods of tmap
Most choropleth maps employ some methods of data classification. The point of classification is to take a large number of observations and group them into data ranges or classes. tmap provides a total ten data classification methods, namely: fixed, sd, equal, pretty (default), quantile, kmeans, hclust, bclust, fisher, and jenks.
To define a data classification method, the style argument of tm_fill() or tm_polygons() will be used.
Plotting choropleth maps with built-in classification methods
The code chunk below shows a quantile data classification that used 5 classes.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "jenks") +
tm_borders(alpha = 0.5)
The code chunk below uses an equal data classification method.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5)
Notice that the distribution of quantile data classification method are more evenly distributed than equal data classification method.
The code chunk below uses a sd data classification method.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "sd") +
tm_borders(alpha = 0.5)
The code chunks below use the same sd data classification method but different numbers of classes (i.e. 2, 10).
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 2,
style = "sd") +
tm_borders(alpha = 0.5)
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 10,
style = "sd") +
tm_borders(alpha = 0.5)
The map output shows that by assigning different numbers of classes, the colour distribution of the map can be largely affected.
Plotting choropleth map with custom break
For all the built-in styles, the category breaks are computed internally. In order to override these defaults, the breakpoints can be set explicitly by means of the breaks argument to the tm_fill(). It is important to note that in tmap, the breaks include a minimum and maximum. As a result, in order to end up with n categories, n+1 elements must be specified in the breaks option (the values must be in increasing order).
Before we get started, it is always a good practice to get some descriptive statistics on the variable before setting the break points. Code chunk below is used to compute and display the descriptive statistics of DEPENDENCY field.
summary(mpsz_pop2020$DEPENDENCY) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.6519 0.7025 0.7742 0.7645 19.0000 92 With reference to the results above, we set break point at 0.60, 0.70, 0.80, and 0.90. In addition, we also need to include a minimum and maximum, which we set at 0 and 100. Our breaks vector is thus c(0, 0.60, 0.70, 0.80, 0.90, 1.00).
Now, we will plot the choropleth map by using the code chunk below.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) +
tm_borders(alpha = 0.5)Warning: Values have found that are higher than the highest break
Colour Scheme
tmap supports colour ramps either defined by the user or a set of predefined colour ramps from the RColorBrewer package.
Using ColourBrewer palette
To change the colour, we assign the preferred colour to palette argument of tm_fill() as shown in the code chunk below.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 6,
style = "quantile",
palette = "Blues") +
tm_borders(alpha = 0.5)
The code chunk below shades the choropleth map in green, and a “-” prefix is added to reverse the colour shading.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "-Greens") +
tm_borders(alpha = 0.5)
Map Layouts
Map layout refers to the combination of all map elements into a cohesive map. Map elements include among others the objects to be mapped, the title, the scale bar, the compass, the margins and the aspects ratios. Colour settings and data classification methods covered in the previous section related to the palette and break-points are used to affect how the map looks.
Map Legend
In tmap, several legend options are provided to change the placement, format and appearance of the legend.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "jenks",
palette = "Blues",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_layout(main.title = "Distribution of Dependency Ratio by Planning Subzone \n(Jenks Classification)",
main.title.position = "center",
main.title.size = 1,
legend.height = 0.45,
legend.width = 0.35,
legend.outside = FALSE,
legend.position = c("right", "bottom"),
frame = FALSE) +
tm_borders(alpha = 0.5)
Map style
map allows a wide variety of layout settings to be changed. They can be called by using tmap_style().
The code chunk below shows the classic style being used.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "-Greens") +
tm_borders(alpha = 0.5) +
tmap_style("classic")tmap style set to "classic"other available styles are: "white", "gray", "natural", "cobalt", "col_blind", "albatross", "beaver", "bw", "watercolor" 
Cartographic Furniture
Beside map style, tmap also provides arguments to draw other map furniture such as compass, scale bar and grid lines.
In the code chunk below, tm_compass(), tm_scale_bar() and tm_grid() are used to add compass, scale bar and grid lines onto the choropleth map.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
title = "No. of Persons") +
tm_layout(main.title = "Distribution of Dependency Ratio \nby Planning Subzone",
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_compass(type="8star", size = 2) +
tm_scale_bar(width = 0.15) +
tm_grid(lwd = 0.1, alpha = 0.2) +
tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and Population data from Department of Statistics DOS",
position = c("left", "bottom"))
To reset the default style, refer to the code chunk below.
tmap_style("white")tmap style set to "white"other available styles are: "gray", "natural", "cobalt", "col_blind", "albatross", "beaver", "bw", "classic", "watercolor" Drawing Small Multiple Choropleth Maps
Small multiple maps, also referred as facet maps, are composed of many maps arrange side-by-side, and sometimes stacked vertically. Small multiple maps enable the visualisation of how spatial relationships change with respect to another variable, such as time.
In tmap, small multiple maps can be plotted in three ways:
by assigning multiple values to at least one of the aesthetic arguments
by defining a group-by variable in tm_facets()
by creating multiple stand-alone maps with tmap_arrange()
By assigning multiple values to at least one of the aesthetic arguments
The code chunk below creates small multiple choropleth maps by defining ncols in tm_fill().
tm_shape(mpsz_pop2020)+
tm_fill(c("YOUNG", "AGED"),
style = "equal",
palette = "Blues") +
tm_layout(legend.position = c("right", "bottom")) +
tm_borders(alpha = 0.5) +
tmap_style("white")tmap style set to "white"other available styles are: "gray", "natural", "cobalt", "col_blind", "albatross", "beaver", "bw", "classic", "watercolor" 
The code chunk below creates small multiple choropleth maps by assigning multiple values to at least one of the aesthetic arguments.
tm_shape(mpsz_pop2020)+
tm_polygons(c("DEPENDENCY","AGED"),
style = c("equal", "quantile"),
palette = list("Blues","Greens")) +
tm_layout(legend.position = c("right", "bottom"))
By defining a group-by variable in tm_facets()
The code chunk below creates small multiple choropleth maps by using tm_facets().
tm_shape(mpsz_pop2020) +
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
thres.poly = 0) +
tm_facets(by="REGION_N",
free.coords=TRUE,
drop.shapes=TRUE) +
tm_layout(legend.show = FALSE,
title.position = c("center", "center"),
title.size = 20) +
tm_borders(alpha = 0.5)Warning: The argument drop.shapes has been renamed to drop.units, and is
therefore deprecated
By creating multiple stand-alone maps with tmap_arrange()
The code chunk below creates small multiple choropleth maps by creating multiple stand-alone maps with tmap_arrange().
youngmap <- tm_shape(mpsz_pop2020)+
tm_polygons("YOUNG",
style = "quantile",
palette = "Blues")
agedmap <- tm_shape(mpsz_pop2020)+
tm_polygons("AGED",
style = "quantile",
palette = "Blues")
tmap_arrange(youngmap, agedmap, asp=1, ncol=2)
Mappping Spatial Object Meeting a Selection Criterion
Instead of creating small multiple choropleth map, we can also use selection function to map spatial objects meeting the selection criterion.
tm_shape(mpsz_pop2020[mpsz_pop2020$REGION_N=="CENTRAL REGION", ])+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_layout(legend.outside = TRUE,
legend.height = 0.45,
legend.width = 5.0,
legend.position = c("right", "bottom"),
frame = FALSE) +
tm_borders(alpha = 0.5)Warning in pre_process_gt(x, interactive = interactive, orig_crs =
gm$shape.orig_crs): legend.width controls the width of the legend within a map.
Please use legend.outside.size to control the width of the outside legend