Plotting maps with ggplot2

# Loading useful packages
library(tidyverse)
library(gganimate)
library(maps)
library(knitr)
library(kableExtra)

# Loading data from GitHub
raw_data <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-01-22/prison_population.csv")

Let’s plot a map of the contiguous United States:

map_data("usa") %>% 
  tbl_df() %>% 
  ggplot(aes(x = long, y = lat, group = group)) + 
  geom_polygon() +
  coord_map()

What map_data essentially does is that it returns a dataframe consisting of a series of points (longitudes and latitudes) which are grouped together. The grouping tells the geom_polygon layer which dots to connect in order to get an outline of the US. To include the states, we simply pass "state" as an argument…

map_data("state") %>% 
  tbl_df() %>% 
  ggplot(aes(x = long, y = lat, group = group)) + 
  geom_polygon() +
  coord_map()

… and likewise with counties:

map_data("county") %>% 
  tbl_df() %>% 
  ggplot(aes(x = long, y = lat, group = group)) + 
  geom_polygon() +
  coord_map()

There are of course other maps available (see the documentation for more info). Also, if you’re interested in some more details about how map_data works, please check out this excellent guide.

Visualising missing data

While I was (unsuccessfully) experimenting with tmap, it became clear that plotting maps wasn’t my only challenge this week. The data has a lot of missing values:

# Checking which columns have missing values
raw_data %>% 
  summarise_all(funs(sum(is.na(.)))) %>% 
  select_if(function(x) x > 0) %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("striped", "condensed"))

In fact, over half the rows have missing values for prison populations. This is clearly a problem, and may require a significant amount of time to deal with (David briefly describes techniques for dealing with missing data that I found quite useful).

Instead of dealing with the intricacies associated with addressing the missing values problem, I decided to turn the problem on its head and visualise them as a function of time. First I calculated the percentage of missing values for each state in every year:

state_missing_data <- raw_data %>% 
  filter(pop_category == "Total") %>% 
  group_by(year, state) %>%
  summarise(missing_data = round(100*mean(is.na(prison_population)), 2)) %>% 
  ungroup()

We can take a closer look at e.g. Texas to get an idea of what the results of the previous chunk are:

state_missing_data %>% 
  filter(state == "TX") %>% 
  kable() %>% 
  kable_styling(bootstrap_options = c("striped", "condensed"))

There are no values for the prison population in Texas until 1983. Following this there is a relatively stable period with much more data, until 1996. From then on there is a dramatic increase in the amount of missing data, which lasts until 2003. Then things look quite good for a while, until we reach the last year of the data set for which there are no recorded values for the prison population.

Next, I used gganimate to visualise the percentage of missing values for each state by year:

state_missing_data %>% 
  mutate(region = str_to_lower(state.name[match(state, state.abb)])) %>% # Cool trick!
  right_join(map_data("state"), by = "region") %>% 
  ggplot(aes(x = long, y = lat, group = group, fill = missing_data)) +
  geom_polygon() +
  ggthemes::theme_map() +
  coord_map() +
  scale_fill_viridis_c(guide = guide_legend(title = "Percent")) + 
  transition_manual(year) +
  labs(title = "Percentage of counties with missing data (per state)", 
       subtitle = "Year: {current_frame}") +
  theme(legend.position = "right",
        plot.title = element_text(hjust = 0.5, face = "bold"),
        plot.subtitle = element_text(hjust = 0.5))

Note the cool little trick I picked up (once again) from David’s screencast: In order to join our prison data with the map data, David makes clever use of the function match and the base R dataset state. Try it out for yourself to see how it works!

That’s it for this week’s’ #tidytuesday. Thanks for reading!