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Choropleth Maps in R

How to make a choropleth map in R. A choropleth map shades geographic regions by value.


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Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

A Choropleth Map is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build outline choropleth maps, but you can also build choropleth tile maps using our Mapbox trace types.

Base Map Configuration

Plotly figures made with plot_ly have a layout.geo object which can be used to control the appearance of the base map onto which data is plotted.

Introduction: main parameters for choropleth outline maps

Making choropleth maps requires two main types of input:

  1. Geometry information:
    1. This can either be a supplied GeoJSON file where each feature has either an id field or some identifying value in properties; or
    2. one of the built-in geometries within plot_ly: US states and world countries (see below)
  2. A list of values indexed by feature identifier.

The GeoJSON data is passed to the geojson argument, and the data is passed into the z argument of choropleth traces.

Note the geojson attribute can also be the URL to a GeoJSON file, which can speed up map rendering in certain cases.

GeoJSON with feature.id

Here we load a GeoJSON file containing the geometry information for US counties, where feature.id is a FIPS code.

library(plotly)
library(rjson)

data <- fromJSON(file="https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json")
data$features[[1]]

## $type
## [1] "Feature"
## 
## $properties
## $properties$GEO_ID
## [1] "0500000US01001"
## 
## $properties$STATE
## [1] "01"
## 
## $properties$COUNTY
## [1] "001"
## 
## $properties$NAME
## [1] "Autauga"
## 
## $properties$LSAD
## [1] "County"
## 
## $properties$CENSUSAREA
## [1] 594.436
## 
## 
## $geometry
## $geometry$type
## [1] "Polygon"
## 
## $geometry$coordinates
## $geometry$coordinates[[1]]
## $geometry$coordinates[[1]][[1]]
## [1] -86.49677  32.34444
## 
## $geometry$coordinates[[1]][[2]]
## [1] -86.71790  32.40281
## 
## $geometry$coordinates[[1]][[3]]
## [1] -86.81491  32.34080
## 
## $geometry$coordinates[[1]][[4]]
## [1] -86.89058  32.50297
## 
## $geometry$coordinates[[1]][[5]]
## [1] -86.91760  32.66417
## 
## $geometry$coordinates[[1]][[6]]
## [1] -86.71339  32.66173
## 
## $geometry$coordinates[[1]][[7]]
## [1] -86.71422  32.70569
## 
## $geometry$coordinates[[1]][[8]]
## [1] -86.41312  32.70739
## 
## $geometry$coordinates[[1]][[9]]
## [1] -86.41117  32.40994
## 
## $geometry$coordinates[[1]][[10]]
## [1] -86.49677  32.34444
## 
## 
## 
## 
## $id
## [1] "01001"

Data indexed by id

Here we load unemployment data by county, also indexed by FIPS code.

df = read.csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv", header = T, colClasses = c("fips"="character"))
head(df)

##    fips unemp
## 1 01001   5.3
## 2 01003   5.4
## 3 01005   8.6
## 4 01007   6.6
## 5 01009   5.5
## 6 01011   7.2

Choropleth Map Using GeoJSON

library(plotly)
library(rjson)

url <- 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
counties <- rjson::fromJSON(file=url)
url2<- "https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv"
df <- read.csv(url2, colClasses=c(fips="character"))
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
fig <- plot_ly()
fig <- fig %>% add_trace(
    type="choropleth",
    geojson=counties,
    locations=df$fips,
    z=df$unemp,
    colorscale="Viridis",
    zmin=0,
    zmax=12,
    marker=list(line=list(
      width=0)
    )
  )
fig <- fig %>% colorbar(title = "Unemployment Rate (%)")
fig <- fig %>% layout(
    title = "2016 US Unemployment by County"
)

fig <- fig %>% layout(
    geo = g
  )

fig