ggplot2: stacked barplot over different columns

Question

I have the following sample data with three different cost-types and a year-column:

library(tidyverse)

# Sample data
costsA <- sample(100:200,30, replace=T)
costsB <- sample(100:140,30, replace=T)
costsC <- sample(20:20,30, replace=T)
year <- sample(c("2000", "2010", "2030"), 30, replace=T)
df <- data.frame(costsA, costsB, costsC, year)

My goal is to plot these costs in a stacked barplot, so that I can compare the mean-costs between the three year-categories. In order to do so I aggregated the values:

df %>% group_by(year) %>%
  summarise(n=n(),
            meanA = mean(costsA),
            meanB = mean(costsB),
            meanC = mean(costsC)) %>%
ggplot( ... ) + geom_bar()

But how can I plot the graph now? In the x-axis there should be the years and in the y-axis the stacked costs.

example

What you want to do is not quite clear to me but something like this?? df %>% group_by(year) %>% summarise(n=n(), meanA = mean(costsA), meanB = mean(costsB), meanC = mean(costsC)) %>% gather("key", "value", - c(year, n)) %>% ggplot(aes(x = year, y = value, group = key, fill = key)) + geom_bar(stat = "identity") — amatsuo_net

amatsuo_net amatsuo_net · Accepted Answer · 2019-02-25T16:25:12

You have to make the summarise data into a tidy(-ish) format to generate a plot like the one you posted. In a tidy-verse, you'd do that with gather function where you convert multiple columns into two-columns of key-value pairs. For instance, the following code generates the figure below.

df %>% group_by(year) %>%
  summarise(n=n(),
            meanA = mean(costsA),
            meanB = mean(costsB),
            meanC = mean(costsC)) %>% 
  gather("key", "value", - c(year, n)) %>%
  ggplot(aes(x = year, y = value, group = key, fill = key)) + geom_col()

With gather("key", "value", - c(year, n)), three columns (costsA, costsB, costsC) are changed to the key-value pairs.

ggplot2: stacked barplot over different columns

1 Answers