2 Fancy stuff / Eye catchers

In this chapter, we’re going to learn how to add fancy elements like plots, icon and images to {gt} tables. We’re going to start this chapter by using a selection of the gapminder data set from {gapminder}.

library(tidyverse)
library(gt)
gapminder_data <- gapminder::gapminder |> 
  janitor::clean_names() |> 
  select(continent, country, year, life_exp) |> 
  mutate(
    year = as.character(year),
    # Year is really categorical with numeric labels
    country = as.character(country) 
  ) 
gapminder_data
## # A tibble: 1,704 × 4
##    continent country     year  life_exp
##    <fct>     <chr>       <chr>    <dbl>
##  1 Asia      Afghanistan 1952      28.8
##  2 Asia      Afghanistan 1957      30.3
##  3 Asia      Afghanistan 1962      32.0
##  4 Asia      Afghanistan 1967      34.0
##  5 Asia      Afghanistan 1972      36.1
##  6 Asia      Afghanistan 1977      38.4
##  7 Asia      Afghanistan 1982      39.9
##  8 Asia      Afghanistan 1987      40.8
##  9 Asia      Afghanistan 1992      41.7
## 10 Asia      Afghanistan 1997      41.8
## # ℹ 1,694 more rows

Let’s bring this into a table using some fancy elements. Many such elements can be added relatively easily with {gtExtras}. For example, here’s a summary table of our data set.

library(gtExtras)
gt_plt_summary(gapminder_data) 
## Warning in geom_point(data = NULL, aes(x = rng_vals[1], y = 1), color = "transparent", : All aesthetics have length 1, but the data has 1704 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
##   a single row.
## Warning in geom_point(data = NULL, aes(x = rng_vals[2], y = 1), color = "transparent", : All aesthetics have length 1, but the data has 1704 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
##   a single row.

	Column	Missing	Mean	Median	SD
gapminder_data
1704 rows x 4 cols
	continent	0.0%	—	—	—
	country	0.0%	—	—	—
	year	0.0%	—	—	—
	life_exp	0.0%	59.5	60.7	12.9

As you can see, this table includes icons in the first column (categorical or continuous variables) and a plot overview in the third column. Automatic tables like this can give you a feeling for the data at a glance. For example, we can see that there are 12 years and 142 countries present in the data set. Also, no values are missing.

Since we have quite a lot of info on many countries and years, let us make our data set a bit smaller. We don’t want to create huge tables (yet). Just like in the last chapter, we will have to reorder our data a bit so that it’s already in a good table format.

selected_countries <- gapminder_data  |> 
# Filter to use only six years (those that end in 7)
  filter(str_ends(year, "7")) |>
# sample two countries per continent
  group_by(continent, country) |> 
  nest() |> 
  group_by(continent) |> 
  slice_sample(n = 2) |> 
  ungroup() |> 
  unnest(data) |> 
# Rearrange the data into table format
  pivot_wider(names_from = year, names_prefix = 'year', values_from = life_exp)
selected_countries
## # A tibble: 10 × 8
##    continent country       year1957 year1967 year1977 year1987 year1997 year2007
##    <fct>     <chr>            <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
##  1 Africa    Egypt             44.4     49.3     53.3     59.8     67.2     71.3
##  2 Africa    Sierra Leone      31.6     34.1     36.8     40.0     39.9     42.6
##  3 Americas  Nicaragua         45.4     51.9     57.5     62.0     68.4     72.9
##  4 Americas  Jamaica           62.6     67.5     70.1     71.8     72.3     72.6
##  5 Asia      Syria             48.3     53.7     61.2     67.0     71.5     74.1
##  6 Asia      Singapore         63.2     67.9     70.8     73.6     77.2     80.0
##  7 Europe    Netherlands       73.0     73.8     75.2     76.8     78.0     79.8
##  8 Europe    United Kingd…     70.4     71.4     72.8     75.0     77.2     79.4
##  9 Oceania   New Zealand       70.3     71.5     72.2     74.3     77.6     80.2
## 10 Oceania   Australia         70.3     71.1     73.5     76.3     78.8     81.2

From this we can create a {gt} table just like we learned in the last chapter. And with {gtExtras} we can apply a cool FiveThirtyEight theme to our table.

# New column names
new_colnames <- colnames(selected_countries) |> str_remove('(country|year)')
names(new_colnames) <- colnames(selected_countries)

selected_countries |> 
  gt(groupname_col = 'continent') |> 
  tab_header(
    title = 'Life Expectancies over time',
    subtitle = 'Data is courtesy of the Gapminder foundation'
  ) |> 
  cols_label(.list = new_colnames) |> 
  fmt_number(columns = where(is.numeric), decimals = 2) |> 
  gt_theme_538()