Full working solution:
library(dplyr)
library(ggplot2)
library(gridExtra)
library(RColorBrewer)
library(tidyr)
cyl <- sort(unique(mpg$cyl))
ncat <- length(cyl) # 4 types of cylinders
dd <- mpg %>%
group_by(manufacturer, cyl) %>%
summarise(n = n()) %>%
ungroup()
# create palettes
purples <- tibble(cyl, colr = brewer.pal(ncat, "Purples"))
reds <- tibble(manufacturer = "audi", cyl, colr = brewer.pal(ncat, "Reds"))
blues <- tibble(manufacturer = "ford", cyl, colr = brewer.pal(ncat, "Blues"))
# merge them with the data
dd_p <- dd %>% filter(!(manufacturer %in% c("audi", "ford"))) %>% left_join(purples)
dd_r <- dd %>% filter(manufacturer == "audi") %>% left_join(reds)
dd_b <- dd %>% filter(manufacturer == "ford") %>% left_join(blues)
mm <- dd %>%
group_by(manufacturer) %>%
summarise(mcyl = weighted.mean(cyl, n)) %>%
arrange(mcyl) %>%
ungroup()
gg_dd <- rbind(dd_p, dd_r, dd_b) %>%
left_join(mm)
main_plot <- gg_dd %>%
ggplot(mapping = aes(x = reorder(manufacturer, mcyl), y = n, fill = colr)) +
geom_bar(stat = "identity", position = "fill") +
coord_flip() +
scale_fill_identity()
combined_legend<- purples %>%
full_join(reds) %>%
full_join(blues) %>%
mutate(manufacturer = ifelse(is.na(manufacturer), "generic", manufacturer)) %>% ggplot(mapping = aes(y = manufacturer, x = factor(cyl), fill = colr)) +
geom_tile() +
scale_fill_identity() +
coord_fixed()
grid.arrange(main_plot, combined_legend, heights=c(10,2), ncol=1)
