Function that calculates mean, variance, and skewness simultaneously in a dataframe in R

In statistical analysis, understanding the central tendency (mean), dispersion (variance), and asymmetry (skewness) of data is essential for gaining insights into its distribution and characteristics. This article explores how to compute these three statistical measures simultaneously across multiple variables in a data frame using R Programming Language.

Understanding Mean, Variance, and Skewness

1. Mean: Represents the average value of a set of numbers. It measures the central tendency of the data.
2. Variance: Indicates the spread or dispersion of data points around the mean. A higher variance implies a greater spread.
3. Skewness: Measures the asymmetry of the probability distribution of a real-valued random variable about its mean. Positive skewness indicates a longer right tail, while negative skewness indicates a longer left tail.

Approach to Calculate Mean, Variance, and Skewness

To calculate mean, variance, and skewness simultaneously across variables in a data frame in R, we can use the following approach:

1. Load Required Libraries: We'll use the dplyr package for data manipulation and the moments package for calculating skewness.
2. Define a Function: Create a function that computes mean, variance, and skewness for each variable in a DataFrame.
3. Apply the Function: Apply the function to each numeric variable in the DataFrame to obtain the desired statistics.

Let's walk through an example where we calculate mean, variance, and skewness for each numeric variable in a DataFrame.

Step 1: Load Required Libraries

First we will install and load the Required Libraries.

# Install packages if not already installed
install.packages("dplyr")
install.packages("moments")

# Load libraries
library(dplyr)
library(moments)

Step 2: Define a Function

Create a function calc_stats_df that computes mean, variance, and skewness for each numeric variable in a DataFrame.

calc_stats_df <- function(df) {
  # Select numeric variables
  numeric_vars <- sapply(df, is.numeric)
  df_numeric <- df[, numeric_vars]
  
  # Calculate mean, variance, and skewness
  stats <- sapply(df_numeric, function(x) {
    c(mean = mean(x, na.rm = TRUE),
      variance = var(x, na.rm = TRUE),
      skewness = skewness(x, na.rm = TRUE))
  })
  
  # Convert to DataFrame and transpose
  stats_df <- as.data.frame(stats)
  stats_df <- t(stats_df)
  colnames(stats_df) <- c("Mean", "Variance", "Skewness")
  
  # Add variable names as row names
  rownames(stats_df) <- names(stats)
  
  return(stats_df)
}

Step 3: Apply the Function

Apply calc_stats_df to a sample DataFrame to calculate mean, variance, and skewness for each numeric variable.

# Sample DataFrame
set.seed(123)
df <- data.frame(
  var1 = rnorm(100),
  var2 = rnorm(100, mean = 2),
  var3 = rnorm(100, sd = 2)
)

# Calculate statistics
statistics <- calc_stats_df(df)
statistics

Output:

           Mean  Variance   Skewness
[1,] 0.09040591 0.8332328 0.06049948
[2,] 1.89245320 0.9350631 0.63879379
[3,] 0.24093022 3.6090802 0.32581993

The statistics DataFrame will contain the mean, variance, and skewness for each numeric variable (var1, var2, var3) in the original DataFrame df.

Conclusion

Calculating mean, variance, and skewness simultaneously across variables in a DataFrame provides valuable insights into the distribution and characteristics of data. By using the dplyr and moments packages in R, we can efficiently compute these statistics and gain a deeper understanding of our data's central tendency, dispersion, and asymmetry. This approach facilitates exploratory data analysis and supports informed decision-making in various fields such as finance, healthcare, and social sciences where understanding data distributions is crucial.

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Article Tags :

• R Language
• R Functions