The post Descriptive Statistics in R appeared first on Data Science Tutorials

Discover the future: Dive deep into the world of data science today! Data Science Tutorials.

Descriptive Statistics in R: Often it is necessary to create a table containing descriptive statistics for variables in a data frame.

One of the best ways to do this is to use the stat.desc() function of the pastecs package in R.

This function allows you to perform various statistical analyses, including calculating descriptive statistics for variables in a data frame.

The syntax of the stat.desc() function

The syntax for the stat.desc() function is as follows:

stat.desc(x, basic=TRUE, desc=TRUE, norm=FALSE, p=0.95)

Where:

• x: The name of the data frame.
• basic: A Boolean value indicating whether basic statistics should be returned or not.
• desc: A Boolean value indicating whether more advanced statistics should be returned.
• norm: A Boolean value indicating whether normal distribution statistics should be returned.
• p: The p-value to use when calculating confidence interval values.

Example: Using the stat.desc() function in R

Suppose we have a data frame in R containing information about several basketball players, including their team name, total number of points scored, and total number of assists.

We can the stat.desc() Function to calculate descriptive statistics for each of the columns in the data frame.

Here is an example of how you can stat.desc() function:

# Load the pastecs package
library(pastecs)

# Create a data frame
df 



When we run this code, we get a table of descriptive statistics for each of the columns in the data frame. 



Convert a continuous variable to a categorical in R » Data Science Tutorials



This table includes information such as the number of values, null values, and NA values for each column, as well as the minimum and maximum values for each column.



Interpreting the Output



The output of the stat.desc() function is a table that includes a variety of statistical measures. Here’s how to interpret each of these measures:

• nbr.val: The number of values ​​in the column.
• nbr.null: The number of null values ​​in the column.
• nbr.na: The number of NA values ​​in the column.
• min: The minimum value in the column.
• max: The maximum value in the column.
• range: The range (max – min) of the values ​​in the column.
• sum: The sum of the values ​​in the column.
• median: The median value in the column.
• mean: The average value in the column.
• SE.mean: The standard error of the mean value.
• CI.mean .95: The 95% confidence interval for the mean value.
• var: The variance of the values ​​in the column.
• std.dev: The standard deviation of the values ​​in the column.
• coef.var: The coefficient of variation of the values ​​in the column.

Using the stat.desc() function with multiple columns

To calculate descriptive statistics for multiple columns in a data frame, you can use the following syntax:

# Calculate descriptive statistics for points and assists columns
stat_desc(df(c('points', 'assists')))

This calculates descriptive statistics for only the points and assist columns in the data frame.

Conclusion

The stat.desc() function is a powerful tool that allows you to calculate descriptive statistics for variables in a data frame.

This feature allows you to easily create tables with various statistical values. These can be useful for analyzing and visualizing your data.

The post Descriptive Statistics in R appeared first on Data Science Tutorials

Discover your inner data genius: explore, learn and transform with our Data Science Haven! Data Science Tutorials.

Related