You can use the following basic syntax to add a ‘count’ column to a data frame in R:

df %>%
  group_by(var1) %>%
  mutate(var1_count = n())

This particular syntax adds a column called var1_count to the data frame that contains the count of values in the column called var1.

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

Example: Add Count Column in R

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

#define data frama
df <- data.frame(team=c('A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'),
                 position=c('G', 'F', 'F', 'F', 'G', 'G', 'F', 'F'),
                 points=c(18, 22, 19, 14, 14, 11, 20, 28))

#view data frame
df

  team position points
1    A        G     18
2    A        F     22
3    A        F     19
4    B        F     14
5    B        G     14
6    B        G     11
7    B        F     20
8    B        F     28

We can use the following code to add a column called team_count that contains the count of each team:

library(dplyr)

#add column that shows total count of each team
df %>%
  group_by(team) %>%
  mutate(team_count = n())

# A tibble: 8 x 4
# Groups:   team [2]
  team  position points team_count
              
1 A     G            18          3
2 A     F            22          3
3 A     F            19          3
4 B     F            14          5
5 B     G            14          5
6 B     G            11          5
7 B     F            20          5
8 B     F            28          5

There are 3 rows with a team value of A and 5 rows with a team value of B.

Thus:

• For each row where the team is equal to A, the value in the team_count column is 3.
• For each row where the team is equal to B, the value in the team_count column is 5.

You can also add a ‘count’ column that groups by multiple variables.

For example, the following code shows how to add a ‘count’ column that groups by the team and position variables:

library(dplyr)

#add column that shows total count of each team and position
df %>%
  group_by(team, position) %>%
  mutate(team_pos_count = n())

# A tibble: 8 x 4
# Groups:   team, position [4]
  team  position points team_pos_count
                  
1 A     G            18              1
2 A     F            22              2
3 A     F            19              2
4 B     F            14              3
5 B     G            14              2
6 B     G            11              2
7 B     F            20              3
8 B     F            28              3

From the output we can see:

• There is 1 row that contains A in the team column and G in the position column.
• There are 2 rows that contain A in the team column and F in the position column.
• There are 3 rows that contain B in the team column and F in the position column.
• There are 2 rows that contain B in the team column and F in the position column.