Geo-pie plot in R

Photo by L B on Unsplash

Geo-pie plots offer a refreshing way to represent data by combining the simplicity of pie charts with the spatial context provided by maps. They can be used effectively in various domains such as demographic analysis or market research to uncover new insights and make informed decisions based on geographic patterns.

In this post, we are going to make a quick geo-pie of the market shares of five companies using fictitious data.

So let’s cook up some data:

set.seed(123)
phones <- data.frame(Country = c("China", "India", "Bangladesh", "Malawi", "United States", "United Kingdom", "Brazil", "Indonesia", "Germany", "Russia", "Australia", "Malaysia", "South Africa", "Canada", "France", "Japan", "Mexico", "Nigeria")) %>%
      mutate(apple = round(abs(rnorm(18, 10, 30)), 1),
                             samsung = round(abs(rnorm(18, 50, 30)), 1),
                             xiaomi = round(abs(rnorm(18, 100, 30)), 1),
                             huawei = round(abs(rnorm(18, 150, 30)), 1),
                             oppo = round(abs(rnorm(18, 200, 30)), 1))



> phones
          Country apple samsung xiaomi huawei  oppo
1           China   6.8    71.0  116.6  143.2 230.2
2           India   3.1    35.8   98.1  195.5 178.7
3      Bangladesh  56.8    18.0   90.8  103.5 179.4
4          Malawi  12.1    43.5   88.6  167.5 230.8
5   United States  13.9    19.2   79.2  153.7 191.5
6  United Kingdom  61.5    28.1   93.8  156.5 163.4
7          Brazil  23.8    31.2   62.0  161.4 205.4
8       Indonesia  28.0     0.6  165.1  134.9 195.8
9         Germany  10.6    75.1  136.2  140.0 200.2
10         Russia   3.4    54.6   66.3  119.4 211.6
11      Australia  46.7    15.9   87.9  117.8 188.9
12       Malaysia  20.8    87.6   86.0  159.1 219.3
13   South Africa  22.0    62.8  123.4  163.4 193.4
14         Canada  13.3    41.1   97.5  151.6 210.0
15         France   6.7    76.9  107.6  177.7 232.9
16          Japan  63.6    76.3   99.1  211.5 213.1
17         Mexico  24.9    74.6   98.7  135.3 190.2
18        Nigeria  49.0    70.7  141.1   80.7 234.5

Data looks good, now time to get the great joinCountryData2Map function to work from the rworldmap package:

df <- joinCountryData2Map(phones, joinCode = "NAME", nameJoinColumn = "Country")@data

Now the plot

par(mai = c(0, 0, 0.6, 0), xaxs = "i", yaxs = "i")
mapPies(dF = df,
        nameX = "LON",
        nameY = "LAT",
        nameZs = c("apple", "samsung", "xiaomi", "huawei", "oppo"),
        zColours = c("#A2DED0", "#D4A2A2", "#708090", "#FFDAB9", "#000080"),
        oceanCol = "#D9E3DA",
        landCol = "#F3DCD4",
        addSizeLegend = FALSE,
        addCatLegend = FALSE,
        mapRegion = "world",
        xlim = c(-181, 181),
        ylim = c(-81, 80))
title(main = "Market share of Top 5 telecom companies", cex = 2)
legend(-180.1516, 70,
       legend = c("Apple", "Samsung", "Xiaomi", "Huawei", "Oppo"),
       col = c("#A2DED0", "#D4A2A2", "#708090", "#FFDAB9", "#000080"),
       pch = 15,
       cex = 1.2,
       pt.cex = 1.5,
       box.lty = 0,
       horiz = F,
       bg = "#00000002",
       x.intersp = 0.3)