2015-07-30-choropleth.Rmd

---
title: Choropleth Maps in R | Examples | Plotly
name: Choropleth Maps
permalink: r/choropleth-maps/
description: How to make a choropleth map in R. A choropleth map shades geographic regions by value.
layout: base
thumbnail: thumbnail/choropleth.jpg
language: r
page_type: example_index
has_thumbnail: true
display_as: maps
order: 0
output:
  html_document:
    keep_md: true
---

```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE, warning=FALSE)
Sys.setenv("plotly_username"="RPlotBot")
Sys.setenv("plotly_api_key"="q0lz6r5efr")
```
### New to Plotly?

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[Get started](https://plot.ly/r/getting-started/) by downloading the client and [reading the primer](https://plot.ly/r/getting-started/).<br>
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We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/r_cheat_sheet.pdf) (new!) to help you get started!

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```{r}
library(plotly)
packageVersion('plotly')
```

# Choropleth Maps in R
```{r, results = 'hide'}
library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
                           "Fruits", total.fruits, "Veggies", total.veggies,
                           "<br>", "Wheat", wheat, "Corn", corn))
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

p <- plot_geo(df, locationmode = 'USA-states') %>%
  add_trace(
    z = ~total.exports, text = ~hover, locations = ~code,
    color = ~total.exports, colors = 'Purples'
  ) %>%
  colorbar(title = "Millions USD") %>%
  layout(
    title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
    geo = g
  )

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = api_create(p, filename="choropleth-ag")
chart_link
```

```{r, echo=FALSE}
chart_link
```


### World Choropleth Map

```{r, results = 'hide'}
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)

# specify map projection/options
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)

p <- plot_geo(df) %>%
  add_trace(
    z = ~GDP..BILLIONS., color = ~GDP..BILLIONS., colors = 'Blues',
    text = ~COUNTRY, locations = ~CODE, marker = list(line = l)
  ) %>%
  colorbar(title = 'GDP Billions US$', tickprefix = '$') %>%
  layout(
    title = '2014 Global GDP<br>Source:<a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">CIA World Factbook</a>',
    geo = g
  )

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = api_create(p, filename="choropleth-world")
chart_link
```

```{r, echo=FALSE}
chart_link
```

### Choropleth Inset Map

```{r, results = 'hide'}
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_ebola.csv')
# restrict from June to September
df <- subset(df, Month %in% 6:9)
# ordered factor variable with month abbreviations
df$abbrev <- ordered(month.abb[df$Month], levels = month.abb[6:9])
# September totals
df9 <- subset(df, Month == 9)

# common plot options
g <- list(
  scope = 'africa',
  showframe = F,
  showland = T,
  landcolor = toRGB("grey90")
)

g1 <- c(
  g,
  resolution = 50,
  showcoastlines = T,
  countrycolor = toRGB("white"),
  coastlinecolor = toRGB("white"),
  projection = list(type = 'Mercator'),
  list(lonaxis = list(range = c(-15, -5))),
  list(lataxis = list(range = c(0, 12))),
  list(domain = list(x = c(0, 1), y = c(0, 1)))
)

g2 <- c(
  g,
  showcountries = F,
  bgcolor = toRGB("white", alpha = 0),
  list(domain = list(x = c(0, .6), y = c(0, .6)))
)

p <- df %>%
  plot_geo(
    locationmode = 'country names', sizes = c(1, 600), color = I("black")
  ) %>%
  add_markers(
    y = ~Lat, x = ~Lon, locations = ~Country,
    size = ~Value, color = ~abbrev, text = ~paste(Value, "cases")
  ) %>%
  add_text(
    x = 21.0936, y = 7.1881, text = 'Africa', showlegend = F, geo = "geo2"
  ) %>%
  add_trace(
    data = df9, z = ~Month, locations = ~Country,
    showscale = F, geo = "geo2"
  ) %>%
  layout(
    title = 'Ebola cases reported by month in West Africa 2014<br> Source: <a href="https://data.hdx.rwlabs.org/dataset/rowca-ebola-cases">HDX</a>',
    geo = g1, geo2 = g2
  )

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = api_create(p, filename="choropleth-africa")
chart_link
```

```{r, echo=FALSE}
chart_link
```