# 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 `classInt`to 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:
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",

`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) %>%

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) %>%
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) %>%