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In statistics,Â **quantiles**Â 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 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 dplyr package to calculate quantiles by a grouping variable:

library(dplyr) #define data frame df 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

**Additional Resources**

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

How to Calculate Percentiles in R

How to Calculate Deciles in R

How to Calculate Quartiles in R