Creating Stacked Bar Plots with Reordered X-Axis Categories Using ggplot2 in R

Understanding Stacked Bar Plots and ggplot2 in R

Stacked bar plots are a popular way to visualize data, especially when comparing the contributions of multiple categories within each group. In this article, we will explore how to create stacked bar plots using ggplot2 in R and order the x-axis categories by the value of one of the fill categories.

Introduction to ggplot2

ggplot2 is a popular data visualization library for R that provides a powerful and flexible framework for creating high-quality plots. The library is designed around the concept of layers, which allows users to build complex plots by combining multiple layers of different types (e.g., geometry, aesthetics).

Creating Stacked Bar Plots with ggplot2

To create a stacked bar plot using ggplot2, we first need to prepare our data in a suitable format. This typically involves creating a data frame with columns for the variables we want to display on the x-axis and y-axis, as well as any other relevant information (e.g., fill categories).

In the provided example, we create a sample data frame df with three columns: value, catg, and var_name. The value column contains random values between 0 and 1, while the catg column is a factor variable that takes on the values “high”, “medium”, or “low”. The var_name column is another factor variable with levels that need to be reordered based on the value of the “low” sub-categories.

Reordering Levels using reorder() and rev()

To reorder the levels in the var_name column, we can use the reorder() function, which rearranges the levels according to a specified order. In this case, we want to sort the levels by the value of the “low” sub-categories. We extract the levels from the previous step using levels(), reverse them using rev(), and then reassign the new levels to the var_name column using factor().

Alternative Method: Using order() and subset()

Alternatively, we can achieve the same result by sorting the data frame on the value of the “low” sub-categories using order() and then filtering on that condition using subset(). We extract the relevant columns (including var_name) from the filtered subset and use them as levels for the factor variable.

Plotting the Stacked Bar Chart

Once we have reordered the levels in the var_name column, we can create the stacked bar plot using ggplot2. The basic syntax involves specifying the data frame, aesthetic mappings (e.g., x and y), fill categories, and any additional layers or transformations required.

In this example, we use a simple geom_bar() layer to create the stacked bars, with position = "dodge" to accommodate multiple groups on the same x-axis. We also add labels using geom_text() to display the values on each bar.

Flipping the Plot

To rotate the plot 90 degrees and make it easier to read, we use coord_flip(). This transform adjusts the orientation of the axes and allows us to easily interpret the data.

Finalizing the Plot with Labels and Titles

Finally, we add labels to the x-axis using xlab() and y-axis using ylab(), which provides context for our plot. We can also customize the title using main() or other functions.

Example Code: Full Stacked Bar Plot

# Load necessary libraries
library(ggplot2)

# Create sample data frame
set.seed(33)
df <- data.frame(
  value = runif(12),
  catg = factor(rep(c("high", "medium", "low"), each = 4)),
  var_name = c(rep("question1", 3), rep("question2", 3), rep("question3", 3), rep("question4", 3))
)

# Create stacked bar plot with reordered levels
df$var_name <- factor(df$var_name, levels = rev(levels(reorder(df[df$catg == "low",]$var_name, df[df$catg == "low",]$value))))
bar_dist <- ggplot(df, aes(x = var_name, y = value, fill = catg)) +
  geom_bar(stat = "identity", position = "dodge") +
  coord_flip() +
  xlab("Questions") +
  ylab("y") +
  geom_text(size = 4, position = position_dodge(width = 0.7))

# Display the plot
bar_dist

Alternative Method: Using order() and subset()

# Load necessary libraries
library(ggplot2)

# Create sample data frame
set.seed(33)
df <- data.frame(
  value = runif(12),
  catg = factor(rep(c("high", "medium", "low"), each = 4)),
  var_name = c(rep("question1", 3), rep("question2", 3), rep("question3", 3), rep("question4", 3))
)

# Sort data frame on value of "low" sub-categories and filter for that condition
df <- df[with(df, order(value, decreasing = T)),][df[with(df, order(value, decreasing = T)) ,]$catg == "low",]

# Extract relevant columns from filtered subset and use them as levels for factor variable
df$var_name <- df[with(df, order(value, decreasing = T)) ,]$var_name

# Create stacked bar plot with reordered levels
bar_dist <- ggplot(df, aes(x = var_name, y = value, fill = catg)) +
  geom_bar(stat = "identity", position = "dodge") +
  coord_flip() +
  xlab("Questions") +
  ylab("y") +
  geom_text(size = 4, position = position_dodge(width = 0.7))

# Display the plot
bar_dist

Conclusion

In this article, we have explored how to create stacked bar plots using ggplot2 in R and order the x-axis categories by the value of one of the fill categories. We discussed two approaches: reordering levels directly using reorder() and rev(), or sorting the data frame on the specified condition and filtering for that category. Finally, we provided example code to demonstrate both methods, highlighting the flexibility and power of ggplot2 in creating informative and visually appealing plots.

Last modified on 2023-05-10