In geocompr/geocompkg: Geocomputation with R Metapackage

This vignette provides some further detail on the Vector attribute joining section (see https://geocompr.robinlovelace.net/attr.html#vector-attribute-joining ) of the Geocomputation with R book.

This vignette requires the following packages to be installed and attached:

library(sf)
library(spData)
library(dplyr)

We will use an sf object north_america with country codes (iso_a2), names and geometries, as well as a data.frame object wb_north_america containing information about urban population and unemployment for three countries. Note that north_america contains data about Canada, Greenland and the United States but the World Bank dataset (wb_north_america) contains information about Canada, Mexico and the United States:

north_america = world %>%
  filter(subregion == "Northern America") %>%
  dplyr::select(iso_a2, name_long)
north_america

wb_north_america = worldbank_df %>% 
  filter(name %in% c("Canada", "Mexico", "United States")) %>%
  dplyr::select(name, iso_a2, urban_pop, unemploy = unemployment)
wb_north_america

We will use a left join to combine the two datasets. Left joins are the most commonly used operation for adding attributes to spatial data, as they return all observations from the left object (north_america) and the matched observations from the right object (wb_north_america) in new columns. Rows in the left object without matches in the right (Greenland in this case) result in NA values.

To join two objects we need to specify a key. This is a variable (or a set of variables) that uniquely identifies each observation (row). The by argument of dplyr's join functions lets you identify the key variable. In simple cases, a single, unique variable exist in both objects like the iso_a2 column in our example (you may need to rename columns with identifying information for this to work):

left_join1 = north_america %>% 
  left_join(wb_north_america, by = "iso_a2")
left_join1

This has created a spatial dataset with the new variables added. The utility of this is shown in the figure below, which shows the unemployment rate (a World Bank variable) across the countries of North America.

# tmap::qtm(left_join1, "unemploy", fill.breaks = c(6, 6.5, 7), fill.title="Unemployment rate: ",
#           projection = "+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")
library(tmap)
tm_shape(left_join1) + 
  tm_polygons("unemploy", breaks = c(6, 6.5, 7), title = "Unemployment rate: ") +
  tm_layout(legend.position = c("right", "bottom"))

It is also possible to join objects by different variables. Both of the datasets have variables with names of countries, but they are named differently. The north_america has a name_long column and the wb_north_america has a name column. In these cases a named vector, such as c("name_long" = "name"), can specify the connection:

left_join2 = north_america %>% 
  left_join(wb_north_america, by = c("name_long" = "name"))
left_join2

Note that the result contains two duplicated variables - iso_a2.x and iso_a2.y because both x and y objects have the column iso_a2. This can be solved by specifying all the keys:

left_join3 = north_america %>% 
  left_join(wb_north_america, by = c("iso_a2", "name_long" = "name"))
left_join3

Joins also work when a data frame is the first argument. However, for them to work we need to drop the sf class.

left_join4 = wb_north_america %>%
  left_join(st_drop_geometry(north_america), by = c("iso_a2"))
left_join4

class(left_join4)

In contrast to left_join(), inner_join() keeps only observations from the left object (north_america) where there are matching observations in the right object (wb_north_america). All columns from the left and right object are still kept:

inner_join1 = north_america %>% 
  inner_join(wb_north_america, by = c("iso_a2", "name_long" = "name"))
inner_join1

geocompr/geocompkg documentation built on July 5, 2025, 2:35 a.m.