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
## # … with 1,694 more rowsLet’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) | gapminder_data | ||||||
| 1704 rows x 4 cols | ||||||
| Column | Plot Overview | Missing | Mean | Median | SD | |
|---|---|---|---|---|---|---|
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 year2…¹
## <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 Kingdom 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
## # … with abbreviated variable name ¹year2007From 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()| Life Expectancies over time | ||||||
| Data is courtesy of the Gapminder foundation | ||||||
| Africa | ||||||
|---|---|---|---|---|---|---|
| Americas | ||||||
| Asia | ||||||
| Europe | ||||||
| Oceania | ||||||
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
)| Life Expectancies over time | ||||||
| Data is courtesy of the Gapminder foundation | ||||||
| Africa | ||||||
|---|---|---|---|---|---|---|
| Americas | ||||||
| Asia | ||||||
| Europe | ||||||
| Oceania | ||||||
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
)| Life Expectancies over time | ||||||
| Data is courtesy of the Gapminder foundation | ||||||
| Africa | ||||||
|---|---|---|---|---|---|---|
| Americas | ||||||
| Asia | ||||||
| Europe | ||||||
| Oceania | ||||||
You could even do that with all columns. But I am not sure whether that’s a good idea here. After all, we may not want to overload our table with colors.
2.2 Add sparklines
It is quite hard to figure out that each depicted country increased its life expectancy in each year. Sure, you may have an idea that this is the case. But to be sure for real, you will have to compare each cell of each row.
Why don’t we make that a little bit easier? Let us add small line charts. This kind of chart is known as a sparkline. It’s main advantage is that it can make patterns really obvious. Have a look for yourself.
| Life Expectancies over time | |||||||
| Data is courtesy of the Gapminder foundation | |||||||
| Africa | |||||||
|---|---|---|---|---|---|---|---|
| Americas | |||||||
| Asia | |||||||
| Europe | |||||||
| Oceania | |||||||
To create such a table we first need a tibble that has a column Timeline that contains the values from 1957 to 2007. A column that contains more than one value per cell, you say? Yes, you heard that correctly. What we need is a tibble with list-like columns. Sounds fancy if you’ve never heard it before but it is not actually that hard to create one. Here’s what we’re going to do.
- Take the original data set
gapminder_dataand filter it such that it contains the same years and countries as our data setselected_countries - Group the filtered data set by
countryand runsummarise(Timeline = list(c(life_exp))).
The trick here is to wrap the combine function c() into list(). This way, the new list will become one object that will be saved into a tibble’s cell.
gapminder_data |>
filter(
str_ends(year, "7"),
country %in% selected_countries$country
)
## # A tibble: 60 × 4
## continent country year life_exp
## <fct> <chr> <chr> <dbl>
## 1 Oceania Australia 1957 70.3
## 2 Oceania Australia 1967 71.1
## 3 Oceania Australia 1977 73.5
## 4 Oceania Australia 1987 76.3
## 5 Oceania Australia 1997 78.8
## 6 Oceania Australia 2007 81.2
## 7 Africa Egypt 1957 44.4
## 8 Africa Egypt 1967 49.3
## 9 Africa Egypt 1977 53.3
## 10 Africa Egypt 1987 59.8
## # … with 50 more rowslife_exps_timeline <- gapminder_data |>
filter(
str_ends(year, "7"),
country %in% selected_countries$country
) |>
group_by(country) |>
summarise(Timeline = list(c(life_exp)))
life_exps_timeline
## # A tibble: 10 × 2
## country Timeline
## <chr> <list>
## 1 Australia <dbl [6]>
## 2 Egypt <dbl [6]>
## 3 Jamaica <dbl [6]>
## 4 Netherlands <dbl [6]>
## 5 New Zealand <dbl [6]>
## 6 Nicaragua <dbl [6]>
## 7 Sierra Leone <dbl [6]>
## 8 Singapore <dbl [6]>
## 9 Syria <dbl [6]>
## 10 United Kingdom <dbl [6]>Now we can run a quick left_join() to, well, join our two data sets. Then it’s gt()-time. This will list all values of the Timeline column in the {gt} table. Have a look.
selected_countries |>
left_join(life_exps_timeline, by = 'country') |>
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
)| Life Expectancies over time | |||||||
| Data is courtesy of the Gapminder foundation | |||||||
| Africa | |||||||
|---|---|---|---|---|---|---|---|
| Americas | |||||||
| Asia | |||||||
| Europe | |||||||
| Oceania | |||||||
Finally, the last ingredient is to target the Timeline column with the gt_plt_sparkline() layer. In that layer, we can adjust the colors and the dimensions of our sparkline too.
## Join First
selected_countries |>
left_join(life_exps_timeline, by = 'country') |>
## Do table as before
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
) |>
## Target Timeline column
gt_plt_sparkline(
column = Timeline,
palette = c("grey40", "grey40", "grey40", "dodgerblue1", "grey40"),
fig_dim = c(5, 28)
)| Life Expectancies over time | |||||||
| Data is courtesy of the Gapminder foundation | |||||||
| Africa | |||||||
|---|---|---|---|---|---|---|---|
| Americas | |||||||
| Asia | |||||||
| Europe | |||||||
| Oceania | |||||||