# Descriptive statistics in R

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Descriptive statistics in R, it is often necessary to create a table that contains descriptive statistics for variables in a data frame.

One of the best ways to do this is by using the `stat.desc()`

function from the `pastecs`

package in R.

This function can be used to perform a variety of 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 to return basic statistics or not.`desc`

: A boolean value indicating whether to return more advanced statistics or not.`norm`

: A boolean value indicating whether to return normal distribution statistics or not.`p`

: The p-value to use when calculating confidence interval values.

**Example: Using the stat.desc() Function in R**

Suppose that we have a data frame in R that contains information about various basketball players, including their team name, total points scored, and total assists.

We can use the `stat.desc()`

function to calculate descriptive statistics for each of the columns in the data frame.

Here is an example of how to use the `stat.desc()`

function:

# Load the pastecs package library(pastecs) # Create a data frame df <- data.frame(team=c('P1', 'P1', 'P1', 'P2', 'P2', 'P2', 'P3', 'P3'), points=c(220, 309, 124, 218, 125, 110, 128, 123), assists=c(13, 18, 18, 12, 15, 12, 18, 12)) # View the data frame df # Calculate descriptive statistics for each column in the data frame stat_desc(df)

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

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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 values in the column.`sum`

: The sum of values in the column.`median`

: The median value in the column.`mean`

: The mean 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 values in the column.`std.dev`

: The standard deviation of values in the column.`coef.var`

: The coefficient of variation of values in the column.

**Using the stat.desc() Function with Multiple Columns**

If you want 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 will calculate descriptive statistics for only the points and assists columns in the data frame.

**Conclusion**

The `stat.desc()`

function is a powerful tool that can be used to calculate descriptive statistics for variables in a data frame.

By using this function, you can easily create tables that contain a variety of statistical measures, which can be useful for analyzing and visualizing your data.

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