In this article, we will discuss how to calculate quartiles in the R programming language.
Quartiles are just special percentiles that occur after a certain percent of data has been covered.
- First quartile: Refers to 25th percentile of the data. This depicts that 25% percent of data is under the produced value.
- Second quartile: Refers to 50th percentile of the data. This depicts that 50% percent of data is under the produced value. This is also the median of the data.
- Third quartile: Refers to the 75th percentile of the data. This predicts that 75% percent of the data is under the produced value.
To obtain the required quartiles, the quantile() function is used.
Syntax: quantile( data, probs)
Parameter:
- data: data whose percentiles are to be calculated
- probs: percentile value
Example 1: Calculate quartile in vector
R
x = c(2,13,5,36,12,50)
res<-quantile(x, probs = c(0,0.25,0.5,0.75,1))
res |
Output:
0% 25% 50% 75% 100% 2.00 6.75 12.50 30.25 50.00
Example 2: Calculate quartile in dataframe
R
df<-data.frame(x = c(2,13,5,36,12,50),
y = c('a','b','c','c','c','b'))
res<-quantile(df$x, probs = c(0,0.25,0.5,0.75,1))
res |
Output:
0% 25% 50% 75% 100% 2.00 6.75 12.50 30.25 50.00
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