In statistics, quantiles are values that divide a ranked dataset into equal groups.

The quantile() function in R can be used to calculate sample quantiles of a dataset.

This function uses the following basic syntax:

quantile(x, probs = seq(0, 1, 0.25), na.rm = FALSE)

where:

• x: Name of vector
• probs: Numeric vector of probabilities
• na.rm: Whether to remove NA values

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

Example 1: Calculate Quantiles of a Vector

The following code shows how to calculate quantiles of a vector in R:

#define vector of data 
data = c(1, 3, 3, 4, 5, 7, 8, 9, 12, 13, 13, 15, 18, 20, 22, 23, 24, 28)

#calculate quartiles
quantile(data, probs = seq(0, 1, 1/4))

 0%  25%  50%  75% 100% 
1.0  5.5 12.5 19.5 28.0 

#calculate quintiles
quantile(data, probs = seq(0, 1, 1/5))

 0%  20%  40%  60%  80% 100% 
1.0  4.4  8.8 13.4 21.2 28.0 

#calculate deciles
quantile(data, probs = seq(0, 1, 1/10))

 0%  10%  20%  30%  40%  50%  60%  70%  80%  90% 100% 
1.0  3.0  4.4  7.1  8.8 12.5 13.4 17.7 21.2 23.3 28.0 

#calculate random quantiles of interest
quantile(data, probs = c(.2, .5, .9))

20%  50%  90% 
4.4 12.5 23.3

Example 2: Calculate Quantiles of Columns in Data Frame

The following code shows how to calculate the quantiles of a specific column in a data frame:

#create data frame
df <- data.frame(var1=c(1, 3, 3, 4, 5, 7, 7, 8, 12, 14, 18),
                 var2=c(7, 7, 8, 3, 2, 6, 8, 9, 11, 11, 16),
                 var3=c(3, 3, 6, 6, 8, 4, 4, 7, 10, 10, 11))

#calculate quartiles of column 'var2'
quantile(df$var2, probs = seq(0, 1, 1/4))

  0%  25%  50%  75% 100% 
 2.0  6.5  8.0 10.0 16.0 

We can also use the sapply() function to calculate the quantiles of multiple columns at once:

#calculate quartiles of every column
sapply(df, function(x) quantile(x, probs = seq(0, 1, 1/4)))

     var1 var2 var3
0%    1.0  2.0    3
25%   3.5  6.5    4
50%   7.0  8.0    6
75%  10.0 10.0    9
100% 18.0 16.0   11

Example 3: Calculate Quantiles by Group

The following code shows how to use functions from the package to calculate quantiles by a grouping variable:

library(dplyr)

#define data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C'),
                 points=c(1, 3, 3, 4, 5, 7, 7, 8, 12, 14, 18))

#define quantiles of interest
q = c(.25, .5, .75)

#calculate quantiles by grouping variable
df %>%
  group_by(team) %>%
  summarize(quant25 = quantile(points, probs = q[1]), 
            quant50 = quantile(points, probs = q[2]),
            quant75 = quantile(points, probs = q[3]))

# A tibble: 3 x 4
  team  quant25 quant50 quant75
           
1 A         2.5       3    3.25
2 B         6.5       7    7.25
3 C          13      14      16   

The following tutorials show how to use the quantile() function to calculate other common quantile values: