## Choropleths

Making choropleths with leaflet is easy. In this example, we’ll duplicate the step-by-step choropleth tutorial from the Leaflet.js website.

The final result will look like this (scroll to the end to see the completed code):

## Registered S3 method overwritten by 'geojsonsf':
##   method        from
##   print.geojson geojson

### Data source

We’ll start by loading the data from JSON. While the Leaflet.js example loads the JSON directly into JavaScript, with the Leaflet R package we instead want to load the data into R.

In this case, we’ll use the geojsonio package to load the data into sp objects, which will let us easily manipulate the geographic features, and their properties, in R.

states <- geojsonio::geojson_read("https://rstudio.github.io/leaflet/json/us-states.geojson", what = "sp")
class(states)
## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"
names(states)
## [1] "id"      "name"    "density"

As you can see, we now have a SpatialPolygonsDataFrame with name (state name) and density (population density in people/mi2) columns from the GeoJSON.

### Basic states map

Next, let’s make a basic map with just the outline of the states. For our basemap, we’ll use the same "mapbox.light" MapBox style that the example does; if you don’t have a MapBox account, you can just use addTiles() in place of the addProviderTiles call, or choose a free provider.

m <- leaflet(states) %>%
setView(-96, 37.8, 4) %>%
id = "mapbox.light",
accessToken = Sys.getenv('MAPBOX_ACCESS_TOKEN')))

We’ve saved the basic basemap as a separate variable m so we can easily iterate on the addPolygons call as we work through the rest of the tutorial.

To add uniform polygons with default styling, just call addPolygons with no additional arguments.

m %>% addPolygons()

Now, let’s color the states according to their population density. You have various options for mapping data to colors; for this example we’ll match the Leaflet.js tutorial by mapping a specific set of bins into RColorBrewer colors.

First, we’ll define the bins. This is a numeric vector that defines the boundaries between intervals ((0,10], (10,20], and so on).

Then, we’ll call colorBin to generate a palette function that maps the RColorBrewer "YlOrRd" colors to our bins.

Finally, we’ll modify addPolygons to use the palette function and the density values to generate a vector of colors for fillColor, and also add some other static style properties.

bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
pal <- colorBin("YlOrRd", domain = states$density, bins = bins) m %>% addPolygons( fillColor = ~pal(density), weight = 2, opacity = 1, color = "white", dashArray = "3", fillOpacity = 0.7) ### Adding interaction The next thing we’ll want is to make the polygons highlight as the mouse passes over them. The addPolygon function has a highlight argument that makes this simple. m %>% addPolygons( fillColor = ~pal(density), weight = 2, opacity = 1, color = "white", dashArray = "3", fillOpacity = 0.7, highlightOptions = highlightOptions( weight = 5, color = "#666", dashArray = "", fillOpacity = 0.7, bringToFront = TRUE)) (The Leaflet.js tutorial also adds an event handler that zooms into a state when it’s clicked. This isn’t currently possible with the Leaflet R package, except with either custom JavaScript or using Shiny, both of which are outside the scope of this example.) ### Custom info Now let’s expose the state names and values to the user. The Leaflet.js tutorial shows the hovered-over state’s information in a custom control. Again, that’s possible by adding custom JavaScript or using Shiny, but for this example we’ll use the built-in labels feature instead. We’ll generate the labels by handcrafting some HTML, and passing it to lapply(htmltools::HTML) so that Leaflet knows to treat each label as HTML instead of as plain text. We’ll also set some label options to improve the style of the label element itself. labels <- sprintf( "<strong>%s</strong><br/>%g people / mi<sup>2</sup>", states$name, states$density ) %>% lapply(htmltools::HTML) m <- m %>% addPolygons( fillColor = ~pal(density), weight = 2, opacity = 1, color = "white", dashArray = "3", fillOpacity = 0.7, highlightOptions = highlightOptions( weight = 5, color = "#666", dashArray = "", fillOpacity = 0.7, bringToFront = TRUE), label = labels, labelOptions = labelOptions( style = list("font-weight" = "normal", padding = "3px 8px"), textsize = "15px", direction = "auto")) m This is the final version of our polygon layer, so let’s save the result back to the m variable. ### Legend As our final step, let’s add a legend. Because we chose to color our map using colorBin, the addLegend function makes it particularly easy to add a legend with the correct colors and intervals. m %>% addLegend(pal = pal, values = ~density, opacity = 0.7, title = NULL, position = "bottomright") ### Complete code # From http://leafletjs.com/examples/choropleth/us-states.js states <- geojsonio::geojson_read("https://rstudio.github.io/leaflet/json/us-states.geojson", what = "sp") bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf) pal <- colorBin("YlOrRd", domain = states$density, bins = bins)

labels <- sprintf(
"<strong>%s</strong><br/>%g people / mi<sup>2</sup>",
states$name, states$density
) %>% lapply(htmltools::HTML)

leaflet(states) %>%
setView(-96, 37.8, 4) %>%
id = "mapbox.light",
accessToken = Sys.getenv('MAPBOX_ACCESS_TOKEN'))) %>%
fillColor = ~pal(density),
weight = 2,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlightOptions = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(pal = pal, values = ~density, opacity = 0.7, title = NULL,
position = "bottomright")