ggplot: How to show density instead of count in grouped bar plot with facet

The dataframe consists of two factor variables: cls with 3 leveles and subset with 2 levels. I want to compare how much of each class (cls) is there in both groups of subset. I want to show percentages on y-axis. They should be computed within certain subset group, not whole dataset.

library(tidyverse)
data = data.frame(
  x = rnorm(1000),
  cls = factor(c(rep("A", 200), rep("B", 300), rep("C", 500))),
  subset = factor(c(rep("train", 900), rep("test", 100)))
)

This was my attempt to show percentages, but it failed because they are computed within whole dataset instead of subset group:

ggplot(data, aes(x = cls, fill = cls))   geom_bar(aes(y = ..count.. / sum(..count..)))   facet_wrap(~subset)

How can I fix it?

Edit related to the accepted answer:

plot_train_vs_test = function(data, var, subset_colname){
  plot_data = data %>% 
    count(var, eval(subset_colname)) %>% 
    group_by(eval(subset_colname)) %>% 
    mutate(perc = n/sum(n))
  
  ggplot(plot_data, aes(x = var, y = perc, fill = var))  
    geom_col()  
    scale_y_continuous(labels = scales::label_percent())  
    facet_wrap(~eval(subset_colname))
}

plot_train_vs_test(data, "cls", "subset")

Results in errors.

CodePudding user response：

One option and easy fix would be to compute the percentages outside of ggplot and plot the summarized data:

library(ggplot2)
library(dplyr, warn = FALSE)

set.seed(123)

data <- data.frame(
  x = rnorm(1000),
  cls = factor(c(rep("A", 200), rep("B", 300), rep("C", 500))),
  subset = factor(c(rep("train", 900), rep("test", 100)))
)

data_sum <- data %>%
  count(cls, subset) %>%
  group_by(subset) %>%
  mutate(pct = n / sum(n))

ggplot(data_sum, aes(x = cls, y = pct, fill = cls))  
  geom_col()  
  scale_y_continuous(labels = scales::label_percent())  
  facet_wrap(~subset)

EDIT One approach to put the code in a function may look like so:

plot_train_vs_test <- function(.data, x, facet) {
  .data_sum <- .data %>%
    count({{ x }}, {{ facet }}) %>%
    group_by({{ facet }}) %>%
    mutate(pct = n / sum(n))

  ggplot(.data_sum, aes(x = {{ x }}, y = pct, fill = {{ x }}))  
    geom_col()  
    scale_y_continuous(labels = scales::label_percent())  
    facet_wrap(vars({{ facet }}))
}

plot_train_vs_test(data, cls, subset)

For more on the details and especially the {{ operator see Programming with dplyr, Programming with ggplot2 and Best practices for programming with ggplot2