Skewness is a measure of the asymmetry of a probability distribution. In R, you can calculate the skewness of a dataset using the skewness() function from the e1071 package.

You can install the e1071 package in R using the install.packages() function. Here’s an example:

# Install the e1071 package
install.packages("e1071")

Here’s an example of how to calculate skewness using skewness()

 

# Load the e1071 package
library(e1071)

# Create a vector of data
x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9)

# Calculate the skewness of the data
skewness(x)

Skewness using the psych package in R

You can use various statistical methods and visualizations to identify whether a distribution is skewed or not in R. Here are some steps that you can follow:

Install and Load the necessary R packages:

install.packages("ggplot2")
install.packages("psych")

library(ggplot2) # for creating visualizations
library(psych) # for computing skewness

2. Generate a sample data set with a known distribution that you suspect is skewed. For example, we can create a sample data set from a normal distribution:

set.seed(123) # to ensure reproducibility
x <- rnorm(1000, mean = 0, sd = 1)

3. Compute the skewness of the data using the skewness function from the psych package:

skewness(x)

4. Visualize the distribution using a histogram and a density plot using ggplot2 package:

ggplot(data.frame(x), aes(x=x)) +
geom_histogram(bins=30, fill="lightblue", col="black") +
geom_density(fill="red", alpha=0.3) +
theme_classic()

 

Skewness using  the moments package

# Load the 'moments' package
library(moments)

# Generate some sample data
x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

# Calculate the skewness of the data
skewness(x)