# How to Use the tapply() Function in R (With Examples)

The tapply() function in R can be used to apply some function to a vector, grouped by another vector.

This function uses the following basic syntax:

tapply(X, INDEX, FUN, ..)

where:

• X: A vector to apply a function to
• INDEX: A vector to group by
• FUN: The function to apply

The following examples show how to use this function in practice with the following data frame in R:

```#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
position=c('G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'),
points=c(14, 19, 13, 8, 15, 15, 17, 19),
assists=c(4, 3, 3, 5, 9, 14, 15, 12))

#view data frame
df

team position points assists
1    A        G     14       4
2    A        G     19       3
3    A        F     13       3
4    A        F      8       5
5    B        G     15       9
6    B        G     15      14
7    B        F     17      15
8    B        F     19      12```

## Example 1: Apply Function to One Variable, Grouped by One Variable

The following code shows how to use the tapply() function to calculate the mean value of points, grouped by team:

```#calculate mean of points, grouped by team
tapply(df\$points, df\$team, mean)

A    B
13.5 16.5```

From the output we can see:

• The mean value of points for team A is 13.5.
• The mean value of points for team B is 16.5.

Note that you can also include additional arguments after the function, such as na.rm, to indicate that you wish to calculate the mean while ignoring NA values in the data frame:

```#calculate mean of points, grouped by team
tapply(df\$points, df\$team, mean, na.rm=TRUE)

A    B
13.5 16.5```

## Example 2: Apply Function to One Variable, Grouped by Multiple Variables

The following code shows how to use the tapply() function to calculate the mean value of points, grouped by team and position:

```#calculate mean of points, grouped by team and position
tapply(df\$points, list(df\$team, df\$position), mean, na.rm=TRUE)

F    G
A 10.5 16.5
B 18.0 15.0
```

From the output we can see:

• The mean value of points for team A and position F is 10.5.
• The mean value of points for team A and position G is 16.5.
• The mean value of points for team B and position F is 18.0.
• The mean value of points for team B and position G is 15.0.

Note: In this example we grouped by two variables, but we can include as many variables as we’d like in the list() function to group by even more variables.