plotly · nicolaskruchten · Mar 1, 2020 · Feb 21, 2020 · Feb 25, 2020 · Feb 25, 2020
diff --git a/r/2017-04-12-county-level-choropleth.Rmd b/r/2017-04-12-county-level-choropleth.Rmd
diff --git a/r/2020-01-30-choropleth-rmapbox.Rmd b/r/2020-01-30-choropleth-rmapbox.Rmd
@@ -0,0 +1,132 @@
+---
+description: How to make a Mapbox Choropleth Map of US Counties in R with Plotly.
+display_as: maps
+language: r
+layout: base
+name: Choropleth mapbox
+order: 8
+output:
+  html_document:
+    keep_md: true
+page_type: u-guide
+permalink: r/mapbox-county-choropleth/
+thumbnail: thumbnail/mapbox-choropleth.png
+redirect_from: r/county-level-choropleth/
+---
+
+```{r, echo = FALSE, message=FALSE}
+knitr::opts_chunk$set(message = FALSE, warning=FALSE)
+```
+
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **tile-map** choropleth maps, but you can also build [**outline** choropleth maps using our non-Mapbox trace types](/r/choropleth-maps).
+
+Below we show how to create Choropleth Maps using Plotly `Choroplethmapbox` graph object.
+
+### Mapbox Access Token and Base Map Configuration
+
+To plot on Mapbox maps with Plotly you *may* need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/r/mapbox-layers/) documentation for more information. If you're using a Chart Studio Enterprise server, please see additional instructions [here](https://help.plot.ly/mapbox-atlas).
+
+### Introduction: main parameters for choropleth tile maps
+
+Making choropleth Mapbox maps requires two main types of input:
+
+1. GeoJSON-formatted geometry information where each feature has either an `id` field or some identifying value in `properties`.
+2. A list of values indexed by feature identifier.
+
+The GeoJSON data is passed to the `geojson` argument, and the data is passed into the `color` argument of `px.choropleth_mapbox` (`z` if using `graph_objects`), in the same order as the IDs are passed into the `location` argument.
+
+**Note** the `geojson` attribute can also be the URL to a GeoJSON file, which can speed up map rendering in certain cases.
+
+#### GeoJSON with `feature.id`
+
+Here we load a GeoJSON file containing the geometry information for US counties, where `feature.id` is a [FIPS code](https://en.wikipedia.org/wiki/FIPS_county_code).
+
+```{r}
+library(rjson)
+url = 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
+json_file <- rjson::fromJSON(file=url)
+json_file$features[[1]]$id
+```
+
+### Data indexed by `id`
+
+Here we load unemployment data by county, also indexed by [FIPS code](https://en.wikipedia.org/wiki/FIPS_county_code).
+
+```{r}
+df = read.csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv", header = T, colClasses = c("fips"="character"))
+head(df)
+```
+### Choropleth map using carto base map (no token needed)
+
+With `choroplethmapbox`, each row of the DataFrame is represented as a region of the choropleth.
+
+```{r}
+library(rjson)
+library(plotly)
+
+url <- 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
+counties <- rjson::fromJSON(file=url)
+url2<- "https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv"
+df <- read.csv(url2, colClasses=c(fips="character"))
+fig <- plot_ly() 
+fig <- fig %>% add_trace(
+    type="choroplethmapbox",
+    geojson=counties,
+    locations=df$fips,
+    z=df$unemp,
+    colorscale="Viridis",
+    zmin=0,
+    zmax=12,
+    marker=list(line=list(
+      width=0),
+      opacity=0.5
+    )
+  )
+fig <- fig %>% layout(
+    mapbox=list(
+      style="carto-positron",
+      zoom =2,
+      center=list(lon= -95.71, lat=37.09))
+  )
+fig
+```
+#### Mapbox Light base map: free token needed
+
+```{r}
+library(rjson)
+library(plotly)
+
+mapboxToken <- paste(readLines("../.mapbox_token"), collapse="")    # You need your own token
+Sys.setenv("MAPBOX_TOKEN" = mapboxToken) # for Orca
+
+url <- 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
+counties <- rjson::fromJSON(file = url)
+url2<- "https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv"
+df <- read.csv(url2,colClasses = c(fips = "character"))
+
+fig <- plot_ly()
+fig <- fig %>% add_trace(
+  type = "choroplethmapbox",
+  geojson = counties,
+  locations = df$fips,
+  z=df$unemp,
+  colorscale="Viridis",
+  zmin=0,
+  zmax=12,
+  marker=list(line = list(
+    width = 0),
+    opacity=0.5
+  )
+)
+fig <- fig %>% layout(
+  mapbox = list(
+    style = "light",
+    zoom =3,
+    center = list(lon = -95.7129, lat = 37.0902))
+) 
+fig <- fig %>% config(mapboxAccessToken = Sys.getenv("MAPBOX_TOKEN"))
+fig
+```
+#Reference
+
+See [https://plot.ly/r/reference/#scattermapbox](https://plot.ly/r/reference/#choroplethmapbox) for more information and options!