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:
- Geometry information:
- This can either be a supplied GeoJSON file where each feature has either an
idfield or some identifying value inproperties; or - one of the built-in geometries within
plot_ly: US states and world countries (see below)
- This can either be a supplied GeoJSON file where each feature has either an
- 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$unemp,
colorscale="Viridis",
zmin=0,
zmax=12,
marker=list(line=list(
width=0)
)
)
fig <- fig %>% colorbar(title = "Unemployment Rate (%)")
fig <- fig %>% layout(
title = "2016 US Unemployment by County"
)
fig <- fig %>% layout(
geo = g
)
fig
Note In this example we set layout.geo.scope to usa to automatically configure the map to display USA-centric data in an appropriate projection.
Indexing by GeoJSON Properties
If the GeoJSON you are using either does not have an id field or you wish you use one of the keys in the properties field, you may use the featureidkey parameter to specify where to match the values of locations.
In the following GeoJSON object/data-file pairing, the values of properties.district match the values of the district column:
library(plotly)
library(rjson)
url <- 'https://raw.githubusercontent.com/plotly/datasets/master/election.geojson'
geojson <- rjson::fromJSON(file=url)
url2<- "https://raw.githubusercontent.com/plotly/datasets/master/election.csv"
df <- read.csv(url2)
g <- list(
fitbounds = "locations",
visible = FALSE
)
fig <- plot_ly()
fig <- fig %>% add_trace(
type="choropleth",
geojson=geojson,
locations=df$district,
z=df$Bergeron,
colorscale="Viridis",
featureidkey="properties.district"
)
fig <- fig %>% layout(
geo = g
)
fig <- fig %>% colorbar(title = "Bergeron Votes")
fig <- fig %>% layout(
title = "2013 Montreal Election"
)
fig
Using Built-in Country and State Geometries
Plotly comes with two built-in geometries which do not require an external GeoJSON file:
- USA States
- Countries as defined in the Natural Earth dataset.
Note and disclaimer: cultural (as opposed to physical) features are by definition subject to change, debate and dispute. Plotly includes data from Natural Earth "as-is" and defers to the Natural Earth policy regarding disputed borders which read:
Natural Earth Vector draws boundaries of countries according to defacto status. We show who actually controls the situation on the ground.
To use the built-in countries geometry, provide locations as three-letter ISO country codes.
library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv")
df
## COUNTRY GDP..BILLIONS. CODE
## 1 Afghanistan 21.71 AFG
## 2 Albania 13.40 ALB
## 3 Algeria 227.80 DZA
## 4 American Samoa 0.75 ASM
## 5 Andorra 4.80 AND
## 6 Angola 131.40 AGO
## 7 Anguilla 0.18 AIA
## 8 Antigua and Barbuda 1.24 ATG
## 9 Argentina 536.20 ARG
## 10 Armenia 10.88 ARM
## 11 Aruba 2.52 ABW
## 12 Australia 1483.00 AUS
## 13 Austria 436.10 AUT
## 14 Azerbaijan 77.91 AZE
## 15 Bahamas, The 8.65 BHM
## 16 Bahrain 34.05 BHR
## 17 Bangladesh 186.60 BGD
## 18 Barbados 4.28 BRB
## 19 Belarus 75.25 BLR
## 20 Belgium 527.80 BEL
## 21 Belize 1.67 BLZ
## 22 Benin 9.24 BEN
## 23 Bermuda 5.20 BMU
## 24 Bhutan 2.09 BTN
## 25 Bolivia 34.08 BOL
## 26 Bosnia and Herzegovina 19.55 BIH
## 27 Botswana 16.30 BWA
## 28 Brazil 2244.00 BRA
## 29 British Virgin Islands 1.10 VGB
## 30 Brunei 17.43 BRN
## 31 Bulgaria 55.08 BGR
## 32 Burkina Faso 13.38 BFA
## 33 Burma 65.29 MMR
## 34 Burundi 3.04 BDI
## 35 Cabo Verde 1.98 CPV
## 36 Cambodia 16.90 KHM
## 37 Cameroon 32.16 CMR
## 38 Canada 1794.00 CAN
## 39 Cayman Islands 2.25 CYM
## 40 Central African Republic 1.73 CAF
## 41 Chad 15.84 TCD
## 42 Chile 264.10 CHL
## 43 China 10360.00 CHN
## 44 Colombia 400.10 COL
## 45 Comoros 0.72 COM
## 46 Congo, Democratic Republic of the 32.67 COD
## 47 Congo, Republic of the 14.11 COG
## 48 Cook Islands 0.18 COK
## 49 Costa Rica 50.46 CRI
## 50 Cote d'Ivoire 33.96 CIV
## 51 Croatia 57.18 HRV
## 52 Cuba 77.15 CUB
## 53 Curacao 5.60 CUW
## 54 Cyprus 21.34 CYP
## 55 Czech Republic 205.60 CZE
## 56 Denmark 347.20 DNK
## 57 Djibouti 1.58 DJI
## 58 Dominica 0.51 DMA
## 59 Dominican Republic 64.05 DOM
## 60 Ecuador 100.50 ECU
## 61 Egypt 284.90 EGY
## 62 El Salvador 25.14 SLV
## 63 Equatorial Guinea 15.40 GNQ
## 64 Eritrea 3.87 ERI
## 65 Estonia 26.36 EST
## 66 Ethiopia 49.86 ETH
## 67 Falkland Islands (Islas Malvinas) 0.16 FLK
## 68 Faroe Islands 2.32 FRO
## 69 Fiji 4.17 FJI
## 70 Finland 276.30 FIN
## 71 France 2902.00 FRA
## 72 French Polynesia 7.15 PYF
## 73 Gabon 20.68 GAB
## 74 Gambia, The 0.92 GMB
## 75 Georgia 16.13 GEO
## 76 Germany 3820.00 DEU
## 77 Ghana 35.48 GHA
## 78 Gibraltar 1.85 GIB
## 79 Greece 246.40 GRC
## 80 Greenland 2.16 GRL
## 81 Grenada 0.84 GRD
## 82 Guam 4.60 GUM
## 83 Guatemala 58.30 GTM
## 84 Guernsey 2.74 GGY
## 85 Guinea-Bissau 1.04 GNB
## 86 Guinea 6.77 GIN
## 87 Guyana 3.14 GUY
## 88 Haiti 8.92 HTI
## 89 Honduras 19.37 HND
## 90 Hong Kong 292.70 HKG
## 91 Hungary 129.70 HUN
## 92 Iceland 16.20 ISL
## 93 India 2048.00 IND
## 94 Indonesia 856.10 IDN
## 95 Iran 402.70 IRN
## 96 Iraq 232.20 IRQ
## 97 Ireland 245.80 IRL
## 98 Isle of Man 4.08 IMN
## 99 Israel 305.00 ISR
## 100 Italy 2129.00 ITA
## 101 Jamaica 13.92 JAM
## 102 Japan 4770.00 JPN
## 103 Jersey 5.77 JEY
## 104 Jordan 36.55 JOR
## 105 Kazakhstan 225.60 KAZ
## 106 Kenya 62.72 KEN
## 107 Kiribati 0.16 KIR
## 108 Korea, North 28.00 PRK
## 109 Korea, South 1410.00 KOR
## 110 Kosovo 5.99 KSV
## 111 Kuwait 179.30 KWT
## 112 Kyrgyzstan 7.65 KGZ
## 113 Laos 11.71 LAO
## 114 Latvia 32.82 LVA
## 115 Lebanon 47.50 LBN
## 116 Lesotho 2.46 LSO
## 117 Liberia 2.07 LBR
## 118 Libya 49.34 LBY
## 119 Liechtenstein 5.11 LIE
## 120 Lithuania 48.72 LTU
## 121 Luxembourg 63.93 LUX
## 122 Macau 51.68 MAC
## 123 Macedonia 10.92 MKD
## 124 Madagascar 11.19 MDG
## 125 Malawi 4.41 MWI
## 126 Malaysia 336.90 MYS
## 127 Maldives 2.41 MDV
## 128 Mali 12.04 MLI
## 129 Malta 10.57 MLT
## 130 Marshall Islands 0.18 MHL
## 131 Mauritania 4.29 MRT
## 132 Mauritius 12.72 MUS
## 133 Mexico 1296.00 MEX
## 134 Micronesia, Federated States of 0.34 FSM
## 135 Moldova 7.74 MDA
## 136 Monaco 6.06 MCO
## 137 Mongolia 11.73 MNG
## 138 Montenegro 4.66 MNE
## 139 Morocco 112.60 MAR
## 140 Mozambique 16.59 MOZ
## 141 Namibia 13.11 NAM
## 142 Nepal 19.64 NPL
## 143 Netherlands 880.40 NLD
## 144 New Caledonia 11.10 NCL
## 145 New Zealand 201.00 NZL
## 146 Nicaragua 11.85 NIC
## 147 Nigeria 594.30 NGA
## 148 Niger 8.29 NER
## 149 Niue 0.01 NIU
## 150 Northern Mariana Islands 1.23 MNP
## 151 Norway 511.60 NOR
## 152 Oman 80.54 OMN
## 153 Pakistan 237.50 PAK
## 154 Palau 0.65 PLW
## 155 Panama 44.69 PAN
## 156 Papua New Guinea 16.10 PNG
## 157 Paraguay 31.30 PRY
## 158 Peru 208.20 PER
## 159 Philippines 284.60 PHL
## 160 Poland 552.20 POL
## 161 Portugal 228.20 PRT
## 162 Puerto Rico 93.52 PRI
## 163 Qatar 212.00 QAT
## 164 Romania 199.00 ROU
## 165 Russia 2057.00 RUS
## 166 Rwanda 8.00 RWA
## 167 Saint Kitts and Nevis 0.81 KNA
## 168 Saint Lucia 1.35 LCA
## 169 Saint Martin 0.56 MAF
## 170 Saint Pierre and Miquelon 0.22 SPM
## 171 Saint Vincent and the Grenadines 0.75 VCT
## 172 Samoa 0.83 WSM
## 173 San Marino 1.86 SMR
## 174 Sao Tome and Principe 0.36 STP
## 175 Saudi Arabia 777.90 SAU
## 176 Senegal 15.88 SEN
## 177 Serbia 42.65 SRB
## 178 Seychelles 1.47 SYC
## 179 Sierra Leone 5.41 SLE
## 180 Singapore 307.90 SGP
## 181 Sint Maarten 304.10 SXM
## 182 Slovakia 99.75 SVK
## 183 Slovenia 49.93 SVN
## 184 Solomon Islands 1.16 SLB
## 185 Somalia 2.37 SOM
## 186 South Africa 341.20 ZAF
## 187 South Sudan 11.89 SSD
## 188 Spain 1400.00 ESP
## 189 Sri Lanka 71.57 LKA
## 190 Sudan 70.03 SDN
## 191 Suriname 5.27 SUR
## 192 Swaziland 3.84 SWZ
## 193 Sweden 559.10 SWE
## 194 Switzerland 679.00 CHE
## 195 Syria 64.70 SYR
## 196 Taiwan 529.50 TWN
## 197 Tajikistan 9.16 TJK
## 198 Tanzania 36.62 TZA
## 199 Thailand 373.80 THA
## 200 Timor-Leste 4.51 TLS
## 201 Togo 4.84 TGO
## 202 Tonga 0.49 TON
## 203 Trinidad and Tobago 29.63 TTO
## 204 Tunisia 49.12 TUN
## 205 Turkey 813.30 TUR
## 206 Turkmenistan 43.50 TKM
## 207 Tuvalu 0.04 TUV
## 208 Uganda 26.09 UGA
## 209 Ukraine 134.90 UKR
## 210 United Arab Emirates 416.40 ARE
## 211 United Kingdom 2848.00 GBR
## 212 United States 17420.00 USA
## 213 Uruguay 55.60 URY
## 214 Uzbekistan 63.08 UZB
## 215 Vanuatu 0.82 VUT
## 216 Venezuela 209.20 VEN
## 217 Vietnam 187.80 VNM
## 218 Virgin Islands 5.08 VGB
## 219 West Bank 6.64 WBG
## 220 Yemen 45.45 YEM
## 221 Zambia 25.61 ZMB
## 222 Zimbabwe 13.74 ZWE
fig <- plot_ly(df, type='choropleth', locations=df$CODE, z=df$GDP..BILLIONS., text=df$COUNTRY, colorscale="Blues")
fig
To use the USA States geometry, set locationmode='USA-states' and provide locations as two-letter state abbreviations:
library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
"Fruits", total.fruits, "Veggies", total.veggies,
"<br>", "Wheat", wheat, "Corn", corn))
fig <- plot_geo(df, locationmode = 'USA-states')
fig <- fig %>% add_trace(
z = ~total.exports, text = ~hover, locations = ~code,
color = ~total.exports, colors = 'Purples'
)
fig <- fig %>% colorbar(title = "Millions USD")
fig <- fig %>% layout(
title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)'
)
fig
Customize choropleth chart
library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$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')
)
fig <- plot_geo(df, locationmode = 'USA-states')
fig <- fig %>% add_trace(
z = ~total.exports, text = ~hover, locations = ~code,
color = ~total.exports, colors = 'Purples'
)
fig <- fig %>% colorbar(title = "Millions USD")
fig <- fig %>% layout(
title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
geo = g
)
fig
Note In this example we set layout.geo.scope to usa to automatically configure the map to display USA-centric data in an appropriate projection.
World Choropleth Map
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')
)
fig <- plot_geo(df)
fig <- fig %>% add_trace(
z = ~GDP..BILLIONS., color = ~GDP..BILLIONS., colors = 'Blues',
text = ~COUNTRY, locations = ~CODE, marker = list(line = l)
)
fig <- fig %>% colorbar(title = 'GDP Billions US$', tickprefix = '$')
fig <- fig %>% 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
)
fig
Choropleth Inset Map
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)))
)
fig <- df %>% plot_geo(
locationmode = 'country names', sizes = c(1, 600), color = I("black")
)
fig <- fig %>% add_markers(
y = ~Lat, x = ~Lon, locations = ~Country,
size = ~Value, color = ~abbrev, text = ~paste(Value, "cases")
)
fig <- fig %>% add_text(
x = 21.0936, y = 7.1881, text = 'Africa', showlegend = F, geo = "geo2"
)
fig <- fig %>% add_trace(
data = df9, z = ~Month, locations = ~Country,
showscale = F, geo = "geo2"
)
fig <- fig %>% 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
)
fig
What About Dash?
Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn about how to install Dash for R at https://dashr.plot.ly/installation.
Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:
library(plotly)
fig <- plot_ly()
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... )
library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)
app <- Dash$new()
app$layout(
htmlDiv(
list(
dccGraph(figure=fig)
)
)
)
app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)