How to Calculate Relative Frequencies Using dplyr

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Often you may want to calculate the relative frequencies/proportions of values in one or more columns of a data frame in R.

Fortunately this is easy to do using functions from the dplyr package. This tutorial demonstrates how to use these functions to calculate relative frequencies on the following data frame:

#create data frame
df #view data frame
df

  team position points
1    A        G     12
2    A        F     15
3    A        F     19
4    B        G     22
5    B        G     32
6    B        G     34
7    B        F     39

Example 1: Relative Frequency of One Variable

The following code shows how to calculate the relative frequency of each team in the data frame:

library(dplyr)

df %>%
  group_by(team) %>%
  summarise(n = n()) %>%
  mutate(freq = n / sum(n))

# A tibble: 2 x 3
  team       n  freq
    
1 A          3 0.429
2 B          4 0.571

This tells us that team A accounts for 42.9% of all rows in the data frame while team B accounts for the remaining 57.1% of rows. Notice that together they add up to 100%.

Related: The Complete Guide: How to Group & Summarize Data in R

Example 2: Relative Frequency of Multiple Variables

The following code shows how to calculate the relative frequency of positions by team:

library(dplyr)

df %>%
  group_by(team, position) %>%
  summarise(n = n()) %>%
  mutate(freq = n / sum(n))

# A tibble: 4 x 4
# Groups:   team [2]
  team   position     n  freq
       
1 A      F            2 0.667
2 A      G            1 0.333
3 B      F            1 0.250
4 B      G            3 0.750

This tells us that:

• 66.7% of players on team A are in position F.
• 33.3% of players on team A are in position G.
• 25.0% of players on team A are in position F.
• 75.0% of players on team B are in position G.

Related: How to Use Mutate to Create New Variables in R

Example 3: Display Relative Frequencies as Percentages

The following code shows how to calculate the relative frequency of positions by team and how to display these relative frequencies as percentages:

library(dplyr)

df %>%
  group_by(team, position) %>%
  summarise(n = n()) %>%
  mutate(freq = paste0(round(100 * n/sum(n), 0), '%'))

# A tibble: 4 x 4
# Groups:   team [2]
  team   position     n freq 
       
1 A      F            2 67%  
2 A      G            1 33%  
3 B      F            1 25%  
4 B      G            3 75%

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