README.md

In sharlagelfand/ggcute: Cute Things for ggplot2

ggcute

A collection of cute things to use with ggplot2.

Installation

You can install ggcute from GitHub with:

# install.packages("remotes")
remotes::install_github("sharlagelfand/ggcute")

fairyfloss

theme_fairyfloss() is a theme based off of sailorhg’s fairyfloss text editor theme.

You can use it like any other ggplot2 theme:

library(ggplot2)
library(ggcute)

ggplot(nintendo_sales, aes(x = sales_million, y = console)) +
  geom_col() +
  facet_wrap(~sales_type) +
  theme_fairyfloss()

It’s probably (definitely) not a complete theme but at least it’s cute ☁️

There is a colour palette built in:

library(scales)

show_col(ggcute:::fairyfloss_colours)

You can use it in plots via scale_fill_fairyfloss():

ggplot(nintendo_sales, aes(x = sales_million, y = console, fill = sales_type)) +
  geom_col(position = position_dodge2(width = 0.1), alpha = 0.75) +
  labs(
    x = "Sales (million)", y = "Console", title = "Nintendo sales by console",
    subtitle = "As of December 31, 2019"
  ) +
  scale_fill_fairyfloss() +
  theme_fairyfloss()

or scale_colour_fairyfloss() (“color” instead of colour works too):

ggplot(nintendo_sales, aes(x = sales_type, y = sales_million, colour = console)) +
  geom_jitter(size = 5, alpha = 0.5) +
  scale_colour_fairyfloss() +
  theme_fairyfloss(base_size = 12) +
  theme(
    legend.position = "bottom",
    legend.title = element_blank()
  )

Of course, you can use the palette without the theme and it’s still extremely cute:

ggplot(head(diamonds, 1000), aes(x = cut, y = carat, colour = carat)) +
  geom_jitter() +
  scale_colour_fairyfloss(discrete = FALSE) +
  theme_minimal()

sugarpilll

theme_sugarpill() is a theme based off the Sugarpill Fun Size eyeshadow palette. Similar to fairyfloss, there are also scale_*_sugarpill() functions.

library(dplyr)
library(forcats)

nintendo_sales %>%
  mutate(console = fct_lump_n(console, n = 8, w = sales_million, other_level = "Other Consoles")) %>%
  group_by(console, sales_type) %>%
  summarise(sales_million = sum(sales_million)) %>%
  ungroup() %>%
  ggplot(aes(x = sales_type, y = sales_million, fill = console)) +
  geom_col(position = position_dodge2()) +
  facet_wrap(vars(console)) +
  labs(
    x = "", y = "",
    title = "Nintendo units sold (millions) by console",
    subtitle = "Data as of December 31, 2019"
  ) +
  scale_fill_sugarpill() +
  theme_sugarpill() +
  theme(legend.position = "none")

Data

ggcute comes with one data set built in, Nintendo sales as of December 31, 2019:

nintendo_sales
#> # A tibble: 22 x 3
#>    console         sales_type sales_million
#>    <chr>           <chr>              <dbl>
#>  1 Nintendo Switch Hardware            52.5
#>  2 Nintendo Switch Software           311. 
#>  3 Nintendo 3DS    Hardware            75.7
#>  4 Nintendo 3DS    Software           382. 
#>  5 Wii U           Hardware            13.6
#>  6 Wii U           Software           103. 
#>  7 Wii             Hardware           102. 
#>  8 Wii             Software           921. 
#>  9 Nintendo DS     Hardware           154. 
#> 10 Nintendo DS     Software           949. 
#> # … with 12 more rows

sharlagelfand/ggcute documentation built on March 31, 2020, 12:52 a.m.