How do you create a relative frequency table in R?

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A relative frequency table tells you how often certain values in a dataset occur relative to the total number of values in the dataset.

You can use the following basic syntax to create a frequency table in R:

table(data)/length(data)

The table() function calculates the frequency of each individual data value and the length() function calculates the total number of values in the dataset.

Thus, dividing each individual frequency by the length of the dataset gives us the relative frequency of each value in the dataset.

The following examples show how to use this syntax in practice.

Example 1: Relative Frequency Table for One Vector

The following code shows how to create a relative frequency table for a single vector in R:

#define data
data <- c('A', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C')

#create relative frequency table
table(data)/length(data)

  A   B   C 
0.2 0.3 0.5 

Here’s how to interpret the table:

• 20% of all values in the dataset are the letter A
• 30% of all values in the dataset are the letter B
• 50% of all values in the dataset are the letter C

Example 2: Relative Frequency Table for One Data Frame Column

The following code shows how to create a relative frequency table for one column of a data frame in R:

#define data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'A', 'B', 'B', 'C'),
                 wins=c(2, 9, 11, 12, 15, 17, 18, 19),
                 points=c(1, 2, 2, 2, 3, 3, 3, 3))

#view first few rows of data frame
head(df)

  team wins points
1    A    2      1
2    A    9      2
3    A   11      2
4    A   12      2
5    A   15      3
6    B   17      3

#calculate relative frequency table for 'team' column
table(df$team)/length(df$team)
 
    A      B      C 
0.625  0.250  0.125

Example 3: Relative Frequency Table for All Data Frame Columns

The following code shows how to create a relative frequency table for every column of a data frame in R:

#define data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'A', 'B', 'B', 'C'),
                 wins=c(2, 9, 11, 12, 15, 17, 18, 19),
                 points=c(1, 2, 2, 2, 3, 3, 3, 3))

#calculate relative frequency table for each column
sapply(df, function(x) table(x)/nrow(df))

$team
x
    A     B     C 
0.625 0.250 0.125 

$wins
x
    2     9    11    12    15    17    18    19 
0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125 

$points
x
    1     2     3 
0.125 0.375 0.500