Calculating Mean and Standard Deviation in R with dplyr: A Step-by-Step Guide

Introduction to Calculating Mean and Standard Deviation in R

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In this article, we will explore how to calculate the mean and standard deviation of a variable from two different groups in R. We will use the dplyr package to achieve this easily.

What is the dplyr Package?

The dplyr package is a popular data manipulation library for R. It provides a grammar of data manipulation that allows you to specify what you want to do with your data in a more declarative way. The main functions used in this article are group_by(), summarise(), and mutate().

Calculating Mean and Standard Deviation

To calculate the mean and standard deviation of a variable from two different groups, we can use the following R code:

# Load the necessary libraries
library(dplyr)

# Create a data frame with sample data
data <- data.frame(
  code = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
  rank = c(4, 11, 27, 53, 4, 22, 16, 21, 25, 18),
  iq = c(40, 65, 80, 80, 40, 70, 20, 55, 50, 40),
  score = c(86.298, 88.716, 70.178, 61.312, 89.522, 60.506, 81.462, 75.820, 69.372, 82.268),
  gender = c("Male", "Female", "Male", "Male", "Male", "Female", "Female", "Female", "Female", "Female")
)

# Group the data by gender and calculate mean and standard deviation of score
data %>% 
  group_by(gender) %>% 
  summarise(avg_score = mean(score),
            sd_score = sd(score))

Understanding the Code

Let’s break down what each line of code does:

library(dplyr) loads the dplyr package, which provides the functions used in this article.
We create a data frame data with sample data using c(), which is a function that returns a vector. Each column in the data frame is created using data.frame().
The main operations performed by this code are:
- group_by(gender): Groups the data by the gender variable.
- summarise(avg_score = mean(score), sd_score = sd(score)): Calculates the mean and standard deviation of the score variable for each group.

Results

When we run this code, it produces the following output:

# A tibble: 2 × 3
  gender avg_score sd_score
   <chr>     <dbl>    <dbl>
1 Female     76.35733 10.13981
2   Male      76.82750 13.36397

Alternative Methods

There are other ways to achieve this in R, but using dplyr is often the most efficient and readable way.

Using aggregate()

One alternative method is to use the aggregate() function from base R:

# Use aggregate() to calculate mean and standard deviation of score
mean_score <- aggregate(score ~ gender, data = data, FUN = mean)
sd_score <- apply(data[data$gender == "Male",], 2, sd)

# Print the results
print(mean_score)
print(sd_score)

This method is less readable than using dplyr, as it requires more manual manipulation of the data.

Using tapply()

Another alternative method is to use the tapply() function from base R:

# Use tapply() to calculate mean and standard deviation of score
mean_score <- tapply(data$score, data$gender, mean)
sd_score <- tapply(data$score, data$gender, sd)

# Print the results
print(mean_score)
print(sd_score)

This method is also less readable than using dplyr, as it requires more manual manipulation of the data.

Conclusion

In this article, we have explored how to calculate the mean and standard deviation of a variable from two different groups in R. We used the dplyr package to achieve this easily. We also provided alternative methods using base R functions such as aggregate() and tapply(). Understanding these concepts is essential for data analysis and manipulation in R.