Chapter 3 Making Maps in R

Learning Objectives

  • plot an sf object
  • create a choropleth map with ggplot
  • plot a raster map with ggplot
  • use RColorBrewer to improve legend colors
  • use classIntto improve legend breaks
  • create a choropleth map with tmap
  • plot a raster map with tmap
  • create an interactive map with leaflet
  • customize a leaflet map with popups and layer controls

In the preceding examples we have used the base plot command to take a quick look at our spatial objects.

In this section we will explore several alternatives to map spatial data with R. For more packages see the “Visualisation” section of the CRAN Task View.

3.1 Choropleth Mapping with ggplot2

ggplot2 is a widely used and powerful plotting library for R. It is not specifically geared towards mapping, it is possible to create quite nice maps.

For an introduction to ggplot check out this site for more pointers.

ggplot can plot sf objects directly by using the geom geom_sf. So all we have to do is:

library(ggplot2)
# if you need to read this in again:
# philly_crimes_sf <- st_read("data/PhillyCrimerate") 
ggplot(philly_crimes_sf) + 
  geom_sf(aes(fill=homic_rate))

Homicide rate is a continuous variable and is plotted by ggplot as such. If we wanted to plot our map as a ‘true’ choropleth map we need to convert our continuous variable into a categorical one, according to whichever brackets we want to use.

This requires two steps:

  • Determine the quantile breaks.
  • Add a categorical variable to the object which assigns each continious vaule to a bracket.

We will use the classInt package to explicitly determine the quantile breaks.

library(classInt)
# get quantile breaks. Add .00001 offset to catch the lowest value
breaks_qt <- classIntervals(c(min(philly_crimes_sf$homic_rate) - .00001,
                              philly_crimes_sf$homic_rate), n = 7, style = "quantile")

str(breaks_qt)
#> List of 2
#>  $ var : num [1:385] 0.3 14.2 10.5 12.7 38.9 ...
#>  $ brks: num [1:8] 0.3 1.86 4.81 8.5 16.14 ...
#>  - attr(*, "style")= chr "quantile"
#>  - attr(*, "nobs")= int 385
#>  - attr(*, "call")= language classIntervals(var = c(min(philly_crimes_sf$homic_rate) - 1e-05, philly_crimes_sf$homic_rate),      n = 7, style = "quantile")
#>  - attr(*, "intervalClosure")= chr "left"
#>  - attr(*, "class")= chr "classIntervals"

Ok. We can retrieve the breaks with breaks$brks.

We use cut to divicde homic_rate into intervals and code them according to which interval they are in.

Lastly, we can use scale_fill_brewer and add our color palette.

philly_crimes_sf %>% 
  mutate(homic_rate_cat = cut(homic_rate, breaks_qt$brks)) %>% 
  ggplot() + 
    geom_sf(aes(fill=homic_rate_cat)) +
    scale_fill_brewer(palette = "OrRd") 

3.2 Raster and ggplot

To visualize raster data using ggplot2, we will use the raster with the values for the digital terrain model (DTM).

Before using ggplot we need to convert it to a dataframe. The terra package has an built-in function for conversion to a plotable dataframe.

# If you need to read it in again:
# HARV_DTM <- rast("data/HARV_dtmCrop.tif")

HARV_DTM_df <- as.data.frame(HARV_DTM, xy = TRUE)
str(HARV_DTM_df)
#> 'data.frame':    2319798 obs. of  3 variables:
#>  $ x           : num  731454 731454 731456 731456 731458 ...
#>  $ y           : num  4713838 4713838 4713838 4713838 4713838 ...
#>  $ HARV_dtmCrop: num  389 390 389 389 389 ...

We can now use ggplot() to plot this data frame. We will set the color scale to scale_fill_viridis_c which is a color-blindness friendly color scale. Here is more about the viridis color maps. We will also use the coord_fixed() function with the default, ratio = 1, which ensures that one unit on the x-axis is the same length as one unit on the y-axis.

ggplot() +
    geom_raster(data = HARV_DTM_df , aes(x = x, y = y, fill = HARV_dtmCrop)) +
    scale_fill_viridis_c() +
    coord_fixed()

3.3 Choropleth with tmap

tmap is specifically designed to make creation of thematic maps more convenient. It borrows from the ggplot syntax and takes care of a lot of the styling and aesthetics. This reduces our amount of code significantly. We only need:

  • tm_shape() where we provide
    • the sf object
  • tm_polygons() where we set
    • the attribute variable to map,
    • the break style, and
    • a title.
library(tmap)
tm_shape(philly_crimes_sf) +
  tm_polygons("homic_rate", 
              style="quantile", 
              title="Philadelphia \nhomicide density \nper sqKm")

tmap has a very nice feature that allows us to give basic interactivity to the map. We can switch from “plot” mode into “view” mode and call the last plot, like so:

tmap_mode("view")
tmap_last()

Cool huh?

The tmap library also includes functions for simple spatial operations, geocoding and reverse geocoding using OSM. For more check vignette("tmap-getstarted").

3.4 Raster with tmap

tmap can also plot raster files natively, for example:

tmap_mode("plot")
#> tmap mode set to plotting
tm_shape(HARV_DTM)+
    tm_raster(style = "cont", palette = "viridis")+
    tm_layout(legend.outside = TRUE)
#> stars object downsampled to 1114 by 897 cells. See tm_shape manual (argument raster.downsample)

See Elegant and informative maps with tmap for more options.

3.5 Web mapping with leaflet

leaflet provides bindings to the ‘Leaflet’ JavaScript library, “the leading open-source JavaScript library for mobile-friendly interactive maps”. We have already seen a simple use of leaflet in the tmap example.

The good news is that the leaflet library gives us loads of options to customize the web look and feel of the map.

The bad news is that the leaflet library gives us loads of options to customize the web look and feel of the map.

Let’s build up the map step by step.

First we load the leaflet library. Use the leaflet() function with an sp or Spatial* object and pipe it to addPolygons() function. It is not required, but improves readability if you use the pipe operator %>% to chain the elements together when building up a map with leaflet.

And while tmap was tolerant about our AEA projection of philly_crimes_sf, leaflet does require us to explicitly reproject the sf object.

library(leaflet) 

# reproject
philly_WGS84 <- st_transform(philly_crimes_sf, 4326)

leaflet(philly_WGS84) %>%
  addPolygons()

To map the homicide density we use addPolygons() and:

  • remove stroke (polygon borders)
  • set a fillColor for each polygon based on homic_rate and make it look nice by adjusting fillOpacity and smoothFactor (how much to simplify the polyline on each zoom level). The fill color is generated using leaflet’s colorQuantile() function, which takes the color scheme and the desired number of classes. To constuct the color scheme colorQuantile() returns a function that we supply to addPolygons() together with the name of the attribute variable to map.
  • add a popup with the homic_rate values. We will create as a vector of strings, that we then supply to addPolygons().
pal_fun <- colorQuantile("YlOrRd", NULL, n = 5)

p_popup <- paste0("<strong>Homicide Rate: </strong>", philly_WGS84$homic_rate)

leaflet(philly_WGS84) %>%
  addPolygons(
    stroke = FALSE, # remove polygon borders
    fillColor = ~pal_fun(homic_rate), # set fill color with function from above and value
    fillOpacity = 0.8, smoothFactor = 0.5, # make it nicer
    popup = p_popup)  # add popup

Here we add a basemap, which defaults to OSM, with addTiles()

leaflet(philly_WGS84) %>%
  addPolygons(
    stroke = FALSE, 
    fillColor = ~pal_fun(homic_rate),
    fillOpacity = 0.8, smoothFactor = 0.5,
    popup = p_popup) %>%
  addTiles()