Chroroplet

How to plot a simple choroplet map

We are using a map dataset containing the coordinates outlining the perimeter of every country’s territories in the world.

# import dataset
globeMap <- read.csv("output/globeMap.csv")

# show the structure of the dataframe
str(globeMap)

## 'data.frame':    104410 obs. of  13 variables:
##  $ X                        : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ long                     : num  -69.9 -69.9 -69.9 -70 -70.1 ...
##  $ lat                      : num  12.5 12.4 12.4 12.5 12.5 ...
##  $ group                    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ order                    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ region                   : chr  "Aruba" "Aruba" "Aruba" "Aruba" ...
##  $ subregion                : chr  NA NA NA NA ...
##  $ Three_Letter_Country_Code: chr  NA NA NA NA ...
##  $ Continent_Name           : chr  NA NA NA NA ...
##  $ Continent_Code           : chr  NA NA NA NA ...
##  $ Country_Name             : chr  NA NA NA NA ...
##  $ Two_Letter_Country_Code  : chr  NA NA NA NA ...
##  $ Country_Number           : int  NA NA NA NA NA NA NA NA NA NA ...

Now let’s create the map

# Load ggplot library
library(ggplot2)

# create ggplot object and save it in an object. The group parameter is very important because it groups all the coordinates by country
myMap <- ggplot(globeMap, aes(x=long, y=lat, group = as.factor(group)))
# add geometry
myMap <- myMap + geom_polygon(aes(fill=Continent_Name, color=Continent_Name))
# add color palettes
myMap <- myMap + scale_fill_viridis_d()
# customized color palette
myMap <- myMap + scale_colour_manual(values = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"))
# add labels
myMap <- myMap + labs(title = "Countries outlined by region", subtitle = "Using a mercator projection", caption = "Dataset retrieved from native ggplot map", x = "Longitude", y= "Latitude")
# add margins
myMap <- myMap + theme_bw()
# plot the map
myMap

Let’s create a Choroplet Map assigning values to each country

To do so let’s use the IHDI data from 2017

# Import the dataset. IMPORTANT  set na.strings to something different than "NA" because NA is used to define North America
IHDI <- read.csv2("output/Coefficient_of_human_inequality.csv", na.strings="")

# Subset the main dataset
IHDI_2017 <- subset(IHDI, IHDI$year == "X2017")

# Rename IHDI dataset column to match with map column 
colnames(IHDI_2017)[5] <- "Three_Letter_Country_Code"

# Add data to the map
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

mapData <- left_join(globeMap,IHDI_2017, by = c("Three_Letter_Country_Code","Three_Letter_Country_Code"))

# Create ggplot object and save it in an object. The group parameter is very important because it groups all the coordinates by country
myMap <- ggplot(mapData, aes(x=globeMap$long, y=globeMap$lat, group = as.factor(globeMap$group)))
# add geometry
myMap <- myMap + geom_polygon(aes(fill = value))
# add color palettes
myMap <- myMap + scale_fill_viridis_c(option="magma")
# add labels
myMap <- myMap + labs(title = "World Inequality-Adjusted Development Index (IHDI) for 2017", subtitle = "Average of education, health and income inequality indexes", caption = "Grey territories have no index. Source UN", x = "Longitude", y= "Latitude")
# add margins
myMap <- myMap + theme_bw()
# plot the map
myMap