hueR lets you create colour palettes based on two variables: variable
a defining the hue, and variable b defining the shades of that hue.
This is intended for grouped categorical data, where values of b
represent subcategories of the values of a.
You can install the latest development version of hueR with:
devtools::install_github("david-barnett/hueR")library(hueR)
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(ggplot2)# sort countries, within continents, by average population
sortedSummary <- gapminder::gapminder %>%
group_by(continent, country) %>%
summarise(AvPop = mean(pop, na.rm = TRUE), .groups = "keep") %>%
group_by(continent) %>%
arrange(.by_group = TRUE, desc(AvPop))
# create palettes
countryPal7 <- sortedSummary %>%
hueGroupPal(group = "continent", shade = "country", maxShades = 7)
# plot population per year
gapminder::gapminder %>%
ggplot(aes(
x = factor(year), y = pop,
# setting as factor with levels in correct order ensures ordering of bars
fill = factor(country, levels = names(countryPal7))
)) +
geom_col() +
guides(fill = "none") +
# setting manual scale of course sets correct colours
scale_fill_manual(values = countryPal7) +
ggfittext::geom_fit_text(
aes(ymin = 0, ymax = pop, label = country),
position = "stack", colour = "white"
) +
theme_classic() +
coord_cartesian(expand = FALSE)
# plot population per year as share of world total that year
gapminder::gapminder %>%
group_by(year) %>%
mutate(popPerc = pop/sum(pop, na.rm = TRUE)) %>%
ggplot(aes(
x = factor(year), y = popPerc,
# setting as factor with levels in correct order ensures ordering of bars
fill = factor(country, levels = names(countryPal7))
)) +
geom_col() +
guides(fill = "none") +
# setting manual scale of course sets correct colours
scale_fill_manual(values = countryPal7) +
ggfittext::geom_fit_text(
aes(ymin = 0, ymax = popPerc, label = country),
position = "stack", colour = "white"
) +
theme_classic() +
coord_cartesian(expand = FALSE)
# plot with modified palette
countryPal7alt <- sortedSummary %>%
hueGroupPal(group = "continent", shade = "country", maxShades = 7,
hues = hueSet(start = 0))
gapminder::gapminder %>%
group_by(year) %>%
mutate(popPerc = pop/sum(pop, na.rm = TRUE)) %>%
ggplot(aes(
x = factor(year), y = popPerc,
# setting as factor with levels in correct order ensures ordering of bars
fill = factor(country, levels = names(countryPal7alt))
)) +
geom_col() +
guides(fill = "none") +
# setting manual scale of course sets correct colours
scale_fill_manual(values = countryPal7alt) +
ggfittext::geom_fit_text(grow = TRUE,
aes(ymin = 0, ymax = popPerc, label = country),
position = "stack", colour = "white"
) +
theme_classic() +
coord_cartesian(expand = FALSE)
devtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 4.1.1 (2021-08-10)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz Europe/Berlin
#> date 2021-09-27
#>
#> - Packages -------------------------------------------------------------------
#> package * version date lib source
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