# dplyr: How to Summarise Data But Keep All Columns

When using the summarise() function in dplyr, all variables not included in the summarise() or group_by() functions will automatically be dropped.

However, you can use the mutate() function to summarize data while keeping all of the columns in the data frame.

The following example shows how to use this function in practice.

## Example: Summarise Data But Keep All Columns Using dplyr

Suppose we have the following data frame that contains information about various basketball players:

```#create data frame
df <- data.frame(team=rep(c('A', 'B', 'C'), each=3),
points=c(4, 9, 8, 12, 15, 14, 29, 30, 22),
assists=c(3, 3, 2, 5, 8, 10, 4, 5, 12))

#view data frame
df

team points assists
1    A      4       3
2    A      9       3
3    A      8       2
4    B     12       5
5    B     15       8
6    B     14      10
7    C     29       4
8    C     30       5
9    C     22      12```

We can use the following syntax to summarize the mean points scored by team:

```library(dplyr)

#summarize mean points values by team
df %>%
group_by(team) %>%
summarise(mean_pts = mean(points))

# A tibble: 3 x 2
team  mean_pts

1 A          7
2 B         13.7
3 C         27
```

The column called mean_pts displays the mean points scored by each team.

From the output we can see:

• The mean points scored by players on team A is 7.
• The mean points scored by players on team B is 13.7.
• The mean points scored by players on team C is 27.

However, suppose we would like to keep all other columns from the original data frame.

We can use the following syntax with the mutate() function to do so:

```library(dplyr)

#summarize mean points values by team and keep all columns
df %>%
group_by(team) %>%
mutate(mean_pts = mean(points)) %>%
ungroup()

# A tibble: 9 x 4
team  points assists mean_pts

1 A          4       3      7
2 A          9       3      7
3 A          8       2      7
4 B         12       5     13.7
5 B         15       8     13.7
6 B         14      10     13.7
7 C         29       4     27
8 C         30       5     27
9 C         22      12     27
```

By using the mutate() function, we’re able to create a new column called mean_pts that summarizes the mean points scored by team while also keeping all other columns from the original data frame.