Calculating data for visualization on stacked 100% bar
This article is originally published at https://tomaztsql.wordpress.com
Calculating cumulative percentage or percentage per group for each time can sometimes be a task with a slight twist. Let’s check this with ggplot2 and tidyverse.
library(ggplot2)
library(tidyverse)
data <- data.frame(
sector = rep(1:20, each = 5),
item = rep(1:5, times = 20),
value = rpois(100, 10)
)
Three (out of many more) ways to show how this can be achieved.
using simple calculation
# using simple calculation
ggplot(data, aes(x = factor(sector), y = value / sum(value) * 100, fill = factor(item))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
labs(x = "Sector", y = "Percentage", title = "Stacked 100% Bar Plot by Sector") + coord_flip()
replacing percent with tapply
# replacing percent with tapply
ggplot(data, aes(x = factor(sector), y = value / tapply(value, sector, sum)[as.character(sector)] * 100, fill = factor(item))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
labs(x = "Sector", y = "Percentage", title = "Stacked 100% Bar Plot by Sector") + coord_flip()
without any complications
ggplot(data, aes(x = factor(sector), y = value, fill = factor(item))) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format(), name = "Percentage") +
labs(x = "Sector", title = "Stacked 100% Bar Plot by Sector") + coord_flip()
And by all means, the diagram is in all cases the same. Just the examples can be slightly more over-engineered 
Happy R-coding and stay healthy!
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This article is originally published at https://tomaztsql.wordpress.com
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