Understanding Qualitative Variables in ggplot Charts

Introduction

When working with ggplot charts, it’s common to encounter qualitative variables that need to be used as the X-axis. However, by default, ggplot will sort these values alphabetically, which may not always be the desired behavior. In this article, we’ll explore how to keep the original order of a qualitative variable used as X in a ggplot chart.

What are Qualitative Variables?

In R, a qualitative variable is a column that contains unique values, also known as levels. These levels can be strings, integers, or a combination of both. When working with qualitative variables, it’s essential to understand how they’re represented in the data frame and how ggplot uses them.

The “levels” Attribute

Each column with qualitative data in an R dataframe has a “levels” attribute, which is a list of unique values in that column. This list is used by ggplot to determine the order of how things appear in your plots. By default, ggplot will sort these values alphabetically.

Why Should We Override the Default Behavior?

There are several scenarios where you might want to override the default behavior:

• Your qualitative variable has a specific order that’s meaningful for your analysis or visualization.
• You need to ensure consistency across different plots or data sets.
• Alphabetical ordering may not accurately represent the underlying relationships between variables.

How to Override the Default Behavior

To keep the original order of a qualitative variable used as X in a ggplot chart, you’ll need to override the “levels” attribute for that column. This can be done using the factor() function in R.

Example Code

Here’s an example of how to use the factor() function to override the “levels” attribute:

df$MAKE = factor(df$MAKE, levels = c("Honda", "Chevy", "Toyota"))

In this code snippet, we’re converting the qualitative variable MAKE in the dataframe df to a factor and setting its levels to the desired order.

Understanding the Code

Here’s what happens when you use the factor() function:

• The column is converted to a factor, which allows R to recognize it as a categorical variable.
• The levels argument specifies the list of unique values in the column. In this case, we’re passing an ordered vector (c("Honda", "Chevy", "Toyota")) that represents the desired order.

By overriding the default behavior, you can ensure that your ggplot chart reflects the original order of the qualitative variable.

Additional Considerations

When working with factors in R, keep the following points in mind:

• Factors are not numeric and cannot be used directly in mathematical operations.
• Using a factor as an X-axis can lead to inconsistent behavior if the data is not properly sorted or ordered.
• If you need to perform calculations involving the qualitative variable, consider using the ordered function instead of creating a factor.

Example Use Cases

Here are some examples that demonstrate how overriding the default behavior can be useful:

Example 1: Sorting Qualitative Variables in ggplot

# Load required libraries
library(ggplot2)

# Create a sample dataframe with qualitative variables
df <- data.frame(MAKE = c("Honda", "Chevy", "Toyota"),
                 MODEL = c("Civic", "Corolla", "Camry"))

# Sort the MAKE variable alphabetically (default behavior)
ggplot(df, aes(x = MAKE)) +
  geom_line()

# Override the default behavior and sort the MAKE variable by a specific order
df$MAKE = factor(df$MAKE, levels = c("Honda", "Chevy", "Toyota"))
ggplot(df, aes(x = MAKE)) +
  geom_line()

In this example, we create a sample dataframe with two qualitative variables: MAKE and MODEL. We then use ggplot to create a line chart for each variable. The first chart shows the default behavior, where the X-axis is sorted alphabetically. By overriding the default behavior, we can sort the X-axis by a specific order.

Example 2: Preserving Original Order in a Bar Chart

# Load required libraries
library(ggplot2)

# Create a sample dataframe with qualitative variables
df <- data.frame(MAKE = c("Honda", "Chevy", "Toyota"),
                 PRICE = c(10000, 20000, 30000))

# Sort the MAKE variable alphabetically (default behavior)
ggplot(df, aes(x = MAKE)) +
  geom_bar(stat = "identity")

# Override the default behavior and sort the MAKE variable by a specific order
df$MAKE = factor(df$MAKE, levels = c("Honda", "Chevy", "Toyota"))
ggplot(df, aes(x = MAKE)) +
  geom_bar(stat = "identity")

In this example, we create a sample dataframe with two qualitative variables: MAKE and PRICE. We then use ggplot to create a bar chart for each variable. The first chart shows the default behavior, where the X-axis is sorted alphabetically. By overriding the default behavior, we can preserve the original order of the X-axis.

Conclusion

In conclusion, understanding how qualitative variables are represented in R dataframes and how ggplot uses them can help you create more accurate and meaningful visualizations. By overriding the default behavior when sorting qualitative variables, you can ensure that your plots reflect the underlying relationships between variables. Remember to consider additional factors, such as using the ordered function or converting columns to numeric values, to avoid inconsistent behavior in your data.

Last modified on 2023-12-13