Make Wes Anderson themed graphs with wesanderson package in R

Well this is just delightful! This package was created by Karthik Ram.

install.packages("wesanderson")
library(wesanderson)
library(hrbrthemes) # for plot themes
library(gapminder) # for data
library(ggbump) # for the bump plot

After you install the wesanderson package, you can

1. create a ggplot2 graph object
2. choose the Wes Anderson color scheme you want to use and create a palette object
3. add the graph object and and the palette object and behold your beautiful data

wes_palette(name, n, type = c("discrete", "continuous"))

To generate a vector of colors, the wes_palette() function requires:

• name: Name of desired palette
• n: Number of colors desired (i.e. how many categories so n = 4).

The names of all the palettes you can enter into the wes_anderson() function

We can use data from the gapminder package. We will look at the scatterplot between life expectancy and GDP per capita.

We feed the wes_palette() function into the scale_color_manual() with the values = wes_palette() argument.

We indicate that the colours would be the different geographic regions.

If we indicate fill in the geom_point() arguments, we would change the last line to scale_fill_manual()

We can log the gapminder variables with the mutate(across(where(is.numeric), log)). Alternatively, we could scale the axes when we are at the ggplot section of the code with the scale_*_continuous(trans='log10')

gapminder %>% 
  filter(continent != "Oceania") %>% 
  mutate(across(where(is.numeric), log)) %>% 
  ggplot(aes(x = lifeExp, y = gdpPercap)) + 
  geom_point(aes(color = continent), size = 3, alpha = 0.8) +
  # facet_wrap(~factor(continent)) +
  hrbrthemes::theme_ft_rc() + 
  ggtitle("GDP per capita and life expectancy") + 
  theme(legend.title = element_blank(),
        legend.text = element_text(size = 20),
        plot.title = element_text(size = 30)) +
  scale_color_manual(values= wes_palette("FantasticFox1", n = 4)) +

Next looking at bump plot of OECD data with the Royal Tenanbaum’s colour palette.

Click here to read more about the OECD dataset.

trust %>% 
  filter(country_name == "Ireland" | country_name == "Sweden" | country_name == "Germany" | country_name == "Spain" | country_name == "Belgium") %>% 
  group_by(year) %>%
  mutate(rank_budget = rank(-trust, ties.method = "min"),
         rank_budget = as.factor(rank_budget)) %>%
  ungroup()  %>% 
  ggplot(aes(x = year, y  = reorder(rank_budget, desc(rank_budget)), 
             group = country_name,
             color = country_name, fill = country_name)) +
  geom_bump(aes(), 
            smooth = 7,
            size = 5, alpha = 0.9) + 
  geom_point(aes(color = country_name), fill = "white", 
             shape = 21, size = 5, stroke = 5) +
  labs(title = "Level of trust in government",
       subtitle = "Data Source: OECD") + 
  theme(panel.border = element_blank(),
        legend.position = "bottom",
        plot.title = element_text(size = 30),
        legend.title = element_blank(),
        legend.text = element_text(size = 20, color = "white"),
        axis.text.y = element_text(size = 20), 
        axis.text.x = element_text(size = 20),
        legend.background = element_rect(fill = "#5e6472"),
        axis.title = element_blank(),
        axis.text = element_text(color = "white", size = 10),
        text= element_text(size = 15, color = "white"),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.key = element_rect(fill = "#5e6472"),
        plot.background = element_rect(fill = "#5e6472"),
        panel.background = element_rect(fill = "#5e6472")) +
  guides(colour = guide_legend(override.aes = list(size=8))) +
  scale_color_manual(values= wes_palette("Royal2", n = 5)) +
  scale_x_continuous(n.breaks = 20)

Click here to read more about the bump plot from the ggbump package.

Last, we can look at Darjeeling colour palette.

trust %>% 
  filter(country_name == "Ireland" | country_name == "Germany" | country_name == "Sweden"| country_name == "Spain" | country_name == "Belgium") %>% 
  ggplot(aes(x = year,
             y = trust, group = country_name)) +
  geom_line(aes(color = country_name), size = 3) +
  geom_point(aes(color = country_name), fill = "white", shape = 21, size = 5, stroke = 5) +
  labs(title = "Level of trust in government",
       subtitle = "Data Source: OECD") + 
  theme(panel.border = element_blank(),
        legend.position = "bottom",
        plot.title = element_text(size = 30),
        legend.title = element_blank(),
        legend.text = element_text(size = 20, color = "white"),
        axis.text.y = element_text(size = 20), 
        axis.text.x = element_text(size = 20),
        legend.background = element_rect(fill = "#5e6472"),
        axis.title = element_blank(),
        axis.text = element_text(color = "white", size = 10),
        text= element_text(size = 15, color = "white"),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.key = element_rect(fill = "#5e6472"),
        plot.background = element_rect(fill = "#5e6472"),
        panel.background = element_rect(fill = "#5e6472")) +
  guides(colour = guide_legend(override.aes = list(size=8))) +
  scale_color_manual(values= wes_palette("Darjeeling1", n = 5)) +
  scale_x_continuous(n.breaks = 20) 

Last we can look at a bar chart counting different regime types in the eighteenth century.

eighteenth_century <- data_1880s %>%
filter(!is.na(regime)) %>%
filter(!is.na(appointment)) %>%
ggplot(aes(appointment)) + geom_bar(aes(fill = factor(regime)), position = position_stack(reverse = TRUE)) + theme(legend.position = "top", text = element_text(size=15), axis.text.x = element_text(angle = -30, vjust = 1, hjust = 0))

Both the regime variable and the appointment variable are discrete categories so we can use the geom_bar() function. When adding the palette to the barplot object, we can use the scale_fill_manual() function.

eighteenth_century + scale_fill_manual(values = wes_palette("Darjeeling1", n = 4)

Now to compare the breakdown with countries in the 21st century (2000 to present)