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()

	1957	1967	1977	1987	1997	2007
Life Expectancies over time
Data is courtesy of the Gapminder foundation
Africa
Egypt	44.44	49.29	53.32	59.80	67.22	71.34
Sierra Leone	31.57	34.11	36.79	40.01	39.90	42.57
Americas
Nicaragua	45.43	51.88	57.47	62.01	68.43	72.90
Jamaica	62.61	67.51	70.11	71.77	72.26	72.57
Asia
Syria	48.28	53.66	61.20	66.97	71.53	74.14
Singapore	63.18	67.95	70.80	73.56	77.16	79.97
Europe
Netherlands	72.99	73.82	75.24	76.83	78.03	79.76
United Kingdom	70.42	71.36	72.76	75.01	77.22	79.42
Oceania
New Zealand	70.26	71.52	72.22	74.32	77.55	80.20
Australia	70.33	71.10	73.49	76.32	78.83	81.23

2.1 Transform columns into heatmaps

In this table, we can see that Sierra Leone had by far the lowest life expectancy in 2007 (among the depicted countries). We can figure this out by comparing the numbers in the most recent column one-by-one.

But that takes quite a lot of effort. Instead, let us make that easier to see by transforming that column into a heat map. To do so, just pass our table to gt_color_rows()¹. What you’ll need to specify, is

the targeted columns
the range of the values that are supposed to be colored
two colors that are used in a linear gradient

# Two colors from the Okabe Ito color palette
color_palette <- c("#CC79A7", "#009E73")

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() |> 
  gt_color_rows(
    columns = year2007, 
    domain = c(30, 85),
    palette = color_palette
  )
## Warning: Since gt v0.9.0, the `colors` argument has been deprecated.
## • Please use the `fn` argument instead.
## This warning is displayed once every 8 hours.

	1957	1967	1977	1987	1997	2007
Life Expectancies over time
Data is courtesy of the Gapminder foundation
Africa
Egypt	44.44	49.29	53.32	59.80	67.22	71.34
Sierra Leone	31.57	34.11	36.79	40.01	39.90	42.57
Americas
Nicaragua	45.43	51.88	57.47	62.01	68.43	72.90
Jamaica	62.61	67.51	70.11	71.77	72.26	72.57
Asia
Syria	48.28	53.66	61.20	66.97	71.53	74.14
Singapore	63.18	67.95	70.80	73.56	77.16	79.97
Europe
Netherlands	72.99	73.82	75.24	76.83	78.03	79.76
United Kingdom	70.42	71.36	72.76	75.01	77.22	79.42
Oceania
New Zealand	70.26	71.52	72.22	74.32	77.55	80.20
Australia	70.33	71.10	73.49	76.32	78.83	81.23

We could also do this for more columns. For example, we could also do the same with the 1957 column.

# Two colors from the Okabe Ito color palette
color_palette <- c("#CC79A7", "#009E73")

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() |> 
  gt_color_rows(
    columns = c(year1957, year2007), 
    domain = c(30, 85),
    palette = color_palette
  )