Ordering x-axis categorical variable using another categorical variable

Introduction

In data visualization, particularly when working with categorical variables, it’s often desirable to order the values on one axis based on another. This can be particularly useful when dealing with ordinal or ranked data. In this article, we’ll explore how to achieve this ordering in R using ggplot2, focusing on a specific scenario involving an x-axis categorical variable.

Background

The example provided involves a dataframe data containing information about samples, including class, ID, stage, abundance, and substrate. We’re interested in plotting the abundance of each sample by its stacked abundance for each different variable within the class using ggplot2’s geom_bar. However, we want to order the x-axis (ID) based on another categorical variable, specifically “stage,” but still keep the ID as labels.

The Issue

The issue at hand is how to achieve the desired ordering of the x-axis without having the stage values as labels. We can do this by leveraging R’s built-in factor function and its level parameter. However, we need a more tailored approach since simply assigning a factor to “stage” doesn’t automatically apply the specific order we want for the ID.

A Potential Solution

The suggested solution involves creating a new variable by combining paste0(d$stage, d$ID) to use this as an additional sorting criterion and then filtering out d$stage from being displayed on the x-axis. However, let’s dive deeper into how we can apply these concepts more effectively.

Step 1: Creating a New Variable

First, we need to create a new variable that incorporates both stage and ID in a way that allows us to sort based on this unique identifier. We can achieve this by using paste0(d$stage, d$ID).

# Assuming 'd' is the dataframe containing our data
new_variable <- paste0(d$stage, d$ID)

Step 2: Ordering and Factoring

Now that we have a new variable, we can order it based on our desired criteria. We’ll use factor to convert this variable into a factor with specified levels.

# Order the 'new_variable' by 'stage'
ordered_new_variable <- factor(new_variable, levels = c("A-X0", "EGG", "EL", "LL","PP","P","A-X1"))

# Sort the dataframe based on 'ordered_new_variable'
d$sorted_new_variable <- ordered(ordered_new_variable)

Step 3: Applying Custom Ordering

To apply our custom ordering, we need to reorder the levels of our categorical variables within ggplot2 plots. We can do this by specifying a custom order when creating the aesthetics for our plot.

# Sample code using ggplot2's reorder function
graph <- ggplot(data, aes(x=reorder(ID, ordered_new_variable), y=Abundance, fill=Class)) +
  facet_grid(~substrat, scales="free_x") +
  geom_bar(aes(color=Class, fill=Class), stat="identity", position="stack")

Step 4: Removing Stage from the x-axis Labels

We want to remove the stage labels from the x-axis. One way to achieve this is by specifying order in the reorder function within ggplot2, which rearranges the categories based on a custom order without displaying them.

# Code snippet for removing stage labels
graph <- ggplot(data, aes(x=reorder(ID, ordered_new_variable), y=Abundance, fill=Class)) +
  facet_grid(~substrat, scales="free_x") +
  geom_bar(aes(color=Class, fill=Class), stat="identity", position="stack")

Example Use Case

Let’s put it all together in a complete example.

# Sample code for the final plot
data <- data.frame(
  ID = c("J8-1", "J21-2", "A-X1-3", "EGG-4"),
  Class = c("OTUA", "OTUB", "OTUA", "OTUB"),
  Stage = c("A-X0", "A-X0", "A-X1", "EGG"),
  Abundance = c(123, 234, [numerical values], [numerical values]),
  Substrat = c("G", "PC", "only two categorical values", "only two categorical values")
)

# Create the new variable
new_variable <- paste0(data$Stage, data$ID)
ordered_new_variable <- factor(new_variable, levels = c("A-X0", "EGG", "EL", "LL","PP","P","A-X1"))

# Sort the dataframe based on 'ordered_new_variable'
data$sorted_new_variable <- ordered(ordered_new_variable)

graph <- ggplot(data, aes(x=reorder(ID, sorted_new_variable), y=Abundance, fill=Class)) +
  facet_grid(~substrat, scales="free_x") +
  geom_bar(aes(color=Class, fill=Class), stat="identity", position="stack")

Conclusion

In conclusion, by creating a new variable that incorporates both the stage and ID of each sample, ordering it based on this new variable, and then specifying a custom order within ggplot2’s plot aesthetics, we can achieve our goal of displaying samples in the desired order on the x-axis while keeping their IDs as labels.

Last modified on 2023-10-14