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Create a quartile column for each value in an R data frame column.
Any numerical data can be divided into four parts by using three quartiles, first quartile at 25%, second quartile at 50% and third quartile at 75% hence there will be four quarters to represent first 25%, second 25%, third 25% and the last 25% in a set of data.
If we want to create a quartile (1 to 4) column for each value in an R data frame column then we can use the quantile function and cut function as shown in the below Examples.
Example 1
Following snippet creates a sample data frame −
x<-sample(1:50,20) df1<-data.frame(x) df1
The following dataframe is created
x 1 4 2 44 3 1 4 14 5 5 6 18 7 3 8 16 9 41 10 10 11 35 12 48 13 36 14 23 15 17 16 19 17 11 18 43 19 28 20 29
To create a quartile column for column x in df1 on the above created data frame, add the following code to the above snippet −
x<-sample(1:50,20) df1<-data.frame(x) df1$Quartile<-cut(df1$x,quantile(df1$x),include.lowest=TRUE,labels=FALSE) df1
Output
If you execute all the above given snippets as a single program, it generates the following Output −
x Quartile 1 4 1 2 44 4 3 1 1 4 14 2 5 5 1 6 18 2 7 3 1 8 16 2 9 41 4 10 10 1 11 35 3 12 48 4 13 36 4 14 23 3 15 17 2 16 19 3 17 11 2 18 43 4 19 28 3 20 29 3
Example 2
Following snippet creates a sample data frame −
y<-rnorm(20) df2<-data.frame(y) df2
The following dataframe is created
y 1 -0.08949509 2 -0.12711363 3 -0.52805367 4 0.08087408 5 -1.35069115 6 0.13678392 7 -0.41386292 8 -0.80830050 9 -1.13387570 10 -1.56282579 11 -1.27191819 12 1.10834061 13 -1.53450425 14 0.83568645 15 -0.52896185 16 0.45211521 17 -1.45162982 18 -0.63935428 19 1.71258558 20 1.09091493
To create a quartile column for column y in df2 on the above created data frame, add the following code to the above snippet −
y<-rnorm(20) df2<-data.frame(y) df2$Quartile<-cut(df2$y,quantile(df2$y),include.lowest=TRUE,labels=FALSE) df2
Output
If you execute all the above given snippets as a single program, it generates the following Output −
y Quartile 1 -0.08949509 3 2 -0.12711363 3 3 -0.52805367 2 4 0.08087408 3 5 -1.35069115 1 6 0.13678392 3 7 -0.41386292 3 8 -0.80830050 2 9 -1.13387570 2 10 -1.56282579 1 11 -1.27191819 1 12 1.10834061 4 13 -1.53450425 1 14 0.83568645 4 15 -0.52896185 2 16 0.45211521 4 17 -1.45162982 1 18 -0.63935428 2 19 1.71258558 4 20 1.09091493 4
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