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< section id="motivation" class="level1">Motivation
Can we easily access a colour palette that is not viridis? Yes, we can! I will show you how to use the tintin package to create colorful plots.
Here are the palette options:
Install tintin
You can install the development version of tintin like so:
remotes::install_github("pachadotdev/tintin")
Examples
How many types of injury we find in Tintin comic books? Let’s use dplyr to find out:
library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
library(tintin) tintin_head_trauma %>% group_by(cause_of_injury) %>% count() %>% arrange(desc(n)) %>% print(n = 29)
# A tibble: 29 × 2 # Groups: cause_of_injury [29] cause_of_injury n <fct> <int> 1 Blow from a club 8 2 Explosion 4 3 Bullet injury 3 4 Car accident 3 5 Chloroform poisoning 3 6 Blow from a stick 2 7 Dehydration 2 8 Hit by a brick 2 9 Hitting a tree trunk 2 10 Hypoxemia 2 11 Avalanche 1 12 Blow from a billy club 1 13 Falling down stairs 1 14 Falling on ice 1 15 Fight with a lion 1 16 G-force 1 17 Gas poisoning 1 18 Hit by a board 1 19 Hit by a camel femur 1 20 Hit by a camera 1 21 Hit by a rake 1 22 Hit by a tree trunk 1 23 Hit by a whisky bottle 1 24 Punch 1 25 Struck by a giant apple 1 26 Struck by a sword 1 27 Struck by an oar 1 28 Struck by Snowy 1 29 Train accident 1
How about we condense the data a bit? Let’s use forcats::fct_lump to group the injuries and obtain the five most common causes of injury:
library(forcats) tintin_head_trauma %>% mutate(cause_of_injury = fct_lump(cause_of_injury, 5)) %>% filter(cause_of_injury != "Other") %>% group_by(cause_of_injury) %>% count() %>% arrange(desc(n))
# A tibble: 5 × 2 # Groups: cause_of_injury [5] cause_of_injury n <fct> <int> 1 Blow from a club 8 2 Explosion 4 3 Bullet injury 3 4 Car accident 3 5 Chloroform poisoning 3
Now, let’s plot the number of injuries per year using ggplot2 but creating intervals for the loss of consciousness variable:
library(ggplot2)
dplot <- tintin_head_trauma %>%
mutate(
locs_interval = cut(
loss_of_consciousness_severity,
breaks = 5
)
) %>%
group_by(year, locs_interval) %>%
count()
g <- ggplot(dplot) +
geom_col(aes(x = as.factor(year), y = n, fill = locs_interval)) +
theme_minimal(base_size = 13) +
theme(axis.text.x = element_text(angle = 30)) +
labs(x = "Year",
y = "Number of injuries",
fill = "Severity",
title = "Tintin's Head Trauma")
g
Now let’s use the tintin package to add colours:
# Top 3 books by count of injuries tintin_head_trauma %>% group_by(book_title) %>% count() %>% arrange(desc(n)) %>% print(n = 3)
# A tibble: 16 × 2 # Groups: book_title [16] book_title n <fct> <int> 1 Land of Black Gold 6 2 The Black Island 5 3 Tintin in America 5 # ℹ 13 more rows
g + scale_fill_tintin_d(option = "land_of_black_gold")
g + scale_fill_tintin_d(option = "the_black_island")
g + scale_fill_tintin_d(option = "tintin_in_america")
More examples
Check the package’s repository for more examples.
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