Understanding Stacked Bar Charts in R Studio
Introduction
Stacked bar charts are a powerful visualization tool used to compare categorical data across different groups. In this post, we’ll explore how to create a stacked bar chart in R Studio using the ggplot2 package and address a common issue where the bars appear to be “stacking” on top of each other.
The Problem
The user provided a code snippet that produces a close-to-desired plot but has an unexpected behavior. When creating a stacked bar chart, R Studio is instead adding any value from one group to another, making it seem like they are stacking on top of each other. This happens because the default behavior of geom_bar is to stack the groups on top of each other.
library(ggplot2)
library(reshape2)
elections <- data.frame(Country = c("England", "NI", "Scotland", "Wales"),
Con = c(291, 0, 13, 8), Lab = c(200, 0, 7, 28))
elections_long <- melt(elections, id = "Country")
ggplot(elections_long, aes(x = Country, y = value)) +
geom_bar(stat="identity", aes(fill = variable)) +
ggtitle("Seats Won By Labour and Conservative In the United Kingdom") +
labs(y= "Amount of Seats Won", x = "Country") +
scale_fill_manual(values = c("#53a0ed", "#f71d1d")) +
labs(fill = "Party")
This code produces a plot where the value for Labour seems to be part of the Conservative bar, which is not what the user wants.
The Solution
To create a non-stacked bar chart where each group has its own separate bars, we can use the position=dodge() function. This will position the groups beside each other, rather than stacking them on top of each other.
library(ggplot2)
library(reshape2)
elections <- data.frame(Country = c("England", "NI", "Scotland", "Wales"),
Con = c(291, 0, 13, 8), Lab = c(200, 0, 7, 28))
elections_long <- melt(elections, id = "Country")
names(elections_long) <- c("Country", "Party", "Votes")
ggplot(elections_long, aes(x = Country, y = Votes, fill = Party)) +
geom_bar(stat="identity", position = position_dodge()) +
ggtitle("Seats Won By Labour and Conservative In the United Kingdom") +
labs(y= "Amount of Seats Won", x = "Country") +
scale_fill_manual(values = c("#53a0ed", "#f71d1d")) +
labs(fill = "Party")
By using position=dodge(), we can create a non-stacked bar chart where each group has its own separate bars. This is the desired behavior, and it’s easy to achieve with ggplot2.
Rotating Bars
If you want to rotate the bars so that the Labour party is “behind” the Conservative party, you can use the reversescale() function in combination with scale_fill_manual(). However, this will only work if you have two groups, as it reverses the order of the fill colors.
To achieve a similar effect for more than two groups, you’ll need to create multiple bars for each group and position them accordingly. Here’s an example:
library(ggplot2)
library(reshape2)
elections <- data.frame(Country = c("England", "NI", "Scotland", "Wales"),
Con = c(291, 0, 13, 8), Lab = c(200, 0, 7, 28))
elections_long <- melt(elections, id = "Country")
names(elections_long) <- c("Country", "Party", "Votes")
ggplot(elections_long, aes(x = Country, y = Votes, fill = Party)) +
geom_bar(stat="identity", position = position_dodge()) +
ggtitle("Seats Won By Labour and Conservative In the United Kingdom") +
labs(y= "Amount of Seats Won", x = "Country") +
scale_fill_manual(values = c("#53a0ed", "#f71d1d")) +
labs(fill = "Party")
In this code, we’re using position=dodge() to create non-stacked bars. To achieve the desired effect, you’ll need to adjust the position of each bar manually.
Conclusion
Creating a stacked bar chart in R Studio is a powerful way to compare categorical data across different groups. However, it’s easy to get caught up in unexpected behavior where the bars appear to be stacking on top of each other. By using position=dodge(), you can create non-stacked bars and achieve the desired visualization.
Remember, the key to creating effective visualizations is to understand your data and what you’re trying to communicate with your chart. With practice and experience, you’ll become proficient in using R Studio and ggplot2 to create stunning visualizations that help tell your story.
Last modified on 2024-06-02