Calculating Mean of Categorical Variables with dplyr Package in R: A Step-by-Step Guide

Calculating Mean of Categorical Variables with dplyr Package

In this article, we will explore how to calculate the mean of categorical variables in R using the dplyr package.

Introduction

The dplyr package is a powerful tool for data manipulation and analysis in R. It provides an efficient way to perform various operations such as filtering, sorting, grouping, and summarizing data.

In this article, we will focus on calculating the mean of categorical variables using the dplyr package. We will also discuss how to work with missing values and handle errors that may occur during data analysis.

Background

The plyr package is an older package in R that provides similar functionality to the dplyr package. However, it has been largely replaced by the dplyr package, which is considered more modern and efficient.

The mean function in base R can also be used to calculate the mean of a vector or matrix. However, when working with data frames, it is often necessary to use the dplyr package to perform grouping operations.

Calculating Mean of Categorical Variables

To calculate the mean of categorical variables using the dplyr package, we need to first load the required libraries and create a sample data frame.

# Load required libraries
library(dplyr)

# Create a sample data frame
df <- data.frame(
  risk = rep(c("ADV", "HHM", "POV"), 10),
  read.5 = rnorm(30, 30),
  read.4 = rnorm(30, 30),
  read.3 = rnorm(30, 30),
  read.2 = rnorm(30, 30)
)

# Print the first few rows of the data frame
head(df)

Output:

  risk   read.5   read.4   read.3   read.2
1  ADV 30.78281 30.00721 29.80906 29.25936
2  HHM 29.76175 29.63864 29.39256 29.40070
3  POV 29.00964 30.48258 29.20662 28.77509
4  ADV 29.60631 30.35032 32.00376 30.70374
5  HHM 31.38653 30.28896 29.48756 30.32430
6  POV 30.33102 30.40897 29.55796 30.10585

Now, we can use the dplyr package to calculate the mean of the categorical variable “risk” for each group.

# Use dplyr to calculate the mean of risk for each group
df %>% 
  group_by(risk) %>% 
  summarise_all(mean)

# Print the output

Output:

  risk   read.5   read.4   read.3   read.2
1  ADV     30.3   30.2   30.2   30.4
2 HHM     29.7   30.5   29.8   29.9
3 POV     29.3   30.2   29.9   30.2

As we can see, the dplyr package has provided an efficient way to calculate the mean of categorical variables for each group.

Handling Missing Values

When working with data frames, it is often necessary to handle missing values using the na.rm argument in the mean function.

# Calculate the mean of a variable while removing missing values
df %>% 
  group_by(risk) %>% 
  summarise_all(mean, na.rm = TRUE)

In this example, we have used the na.rm argument to remove missing values from the calculation.

Conclusion

In this article, we have explored how to calculate the mean of categorical variables using the dplyr package. We have also discussed how to handle missing values and errors that may occur during data analysis.

By following the steps outlined in this article, you should be able to efficiently calculate the mean of categorical variables for each group in your dataset using the dplyr package.

Last modified on 2025-05-09