15  Merge Shapefiles With External Data

15.1 Introduction

A common task in mapping is that we have a shapefile (Chapter 8) or sf object (Chapter 7) of map data, but we want to merge in some external data from another source so that we can map that external data.

Often we want to use different colors to map that external data (Chapter 11).

Here, I use an sf object (Chapter 7) of countries of the world (Chapter 12), and merge that data with data from the World Bank World Development Indicators (Chapter 13).

This tutorial builds upon another tutorial on Mapping with ggplot(Chapter 14)

15.2 Call Libraries

library(rnaturalearth) # natural earth data

library(sf) # simple (spatial) features

library(ggplot2) # beautiful plots

library(dplyr) # data wrangling and joins

15.3 Get Map Data on Countries of the World

I am using the rnaturalearth package to get map data on countries of the world. I read this data into an object called world.

mapdata <- ne_countries(scale = "medium", # medium scale
                        returnclass = "sf") # as sf object

15.4 Make a Map Without Data

I map the data with ggplot, and the special geom, geom_sf.

ggplot(mapdata) + 
  geom_sf() +
  labs(title = "Demonstration Map With No Data")

15.5 Get External Data

Here I load the World Bank Data (Chapter 13).

load("WorldBankData.Rdata")

head(WorldBankData) # replay data set

                      country iso2c iso3c year status lastupdated Gini      GDP
1                 Afghanistan    AF   AFG 2023         2024-10-24   NA       NA
2 Africa Eastern and Southern    ZH   AFE 2023         2024-10-24   NA 1672.506
3  Africa Western and Central    ZI   AFW 2023         2024-10-24   NA 1584.333
4                     Albania    AL   ALB 2023         2024-10-24   NA 8367.776
5                     Algeria    DZ   DZA 2023         2024-10-24   NA 5260.206
6              American Samoa    AS   ASM 2023         2024-10-24   NA       NA
  adult_literacy life_expectancy population undernourishment
1             NA              NA   42239854               NA
2       73.27511              NA  739108306               NA
3       60.50555              NA  502789511               NA
4             NA              NA    2745972               NA
5             NA              NA   45606480               NA
6             NA              NA      43914               NA
                      region   capital longitude latitude              income
1                 South Asia     Kabul   69.1761  34.5228          Low income
2                 Aggregates                                       Aggregates
3                 Aggregates                                       Aggregates
4      Europe & Central Asia    Tirane   19.8172  41.3317 Upper middle income
5 Middle East & North Africa   Algiers   3.05097  36.7397 Lower middle income
6        East Asia & Pacific Pago Pago  -170.691 -14.2846 Upper middle income
         lending
1            IDA
2     Aggregates
3     Aggregates
4           IBRD
5           IBRD
6 Not classified

15.6 Join Data to Shapefile

I use left_join from the dplyr package to merge the spatial data in world with externaldata.

left_join is a function that keeps all observations in the data on the left (the shapefile), and only those matching observations in the data on the right (the external data), which is usually what I want in mapping.

I need a unique identifier for my rows of data, so here I use iso_a3, a unique 3 letter identifier for countries of the world.

First I need to make a copy of a variable in WorldBankData with a new name so that the identifiers will match exactly.

WorldBankData$iso_a3 <- WorldBankData$iso3c 

Then I merge the data using left_join.

newdata <- left_join(mapdata, # map data
                     WorldBankData, # table of indicators
                     by = "iso_a3") # join by

15.7 Make a Map With The Data

Once I have the merged data, it is easy to map it with ggplot and geom_sf. Note that I need to specify an aesthetic for geom_sf. Here GDP is the fill color for countries on the map.

Data could also be mapped with another package like leaflet (Section 19.1).

ggplot(newdata) +
  geom_sf(aes(fill = GDP)) + # adding a fill aesthetic
  scale_fill_viridis_c(na.value = "grey97", # value for NA
                       option = "viridis") + # viridis colors
  labs(title = "Demonstration Map With Merged Data") +
  theme_minimal() # better theme