How to create a rank variable using mutate function of dplyr package in R?

A rank variable is created to convert a numerical variable into ordinal variable. This is useful for non-parametric analysis because if the distribution of the numerical variable is not normal or there are assumptions of parametric analysis that cannot be followed by the numerical variable then the raw variable values are not analyzed directly. To create a rank variable using mutate function, we can use dense_rank argument.

Example

Consider the below data frame −

set.seed(7) x1 <-sample(1:5,20,replace=TRUE) x2 <-rep(c("Winter","Spring","Summer","Rainy"),times=5) df <-data.frame(x1,x2) df

Output

x1 x2 1 2 Winter 2 3 Spring 3 4 Summer 4 2 Rainy 5 2 Winter 6 3 Spring 7 3 Summer 8 2 Rainy 9 4 Winter 10 3 Spring 11 4 Summer 12 2 Rainy 13 3 Winter 14 5 Spring 15 4 Summer 16 3 Rainy 17 2 Winter 18 2 Spring 19 4 Summer 20 3 Rainy library(dplyr)

Creating a rank variable for x1 variable −

Example

df%>%mutate(Rank_x1=dense_rank(desc(-x1)))

Output

x1 x2 Rank_x1 1 2 Winter 1 2 3 Spring 2 3 4 Summer 3 4 2 Rainy 1 5 2 Winter 1 6 3 Spring 2 7 3 Summer 2 8 2 Rainy 1 9 4 Winter 3 10 3 Spring 2 11 4 Summer 3 12 2 Rainy 1 13 3 Winter 2 14 5 Spring 4 15 4 Summer 3 16 3 Rainy 2 17 2 Winter 1 18 2 Spring 1 19 4 Summer 3 20 3 Rainy 2

Let’s have a look at another example −

Example

grp <-rep(c(28,29,31,45,37),times=4) Percentage <-rep(c(28,29,31,45,37),times=4) ID <-1:20 df_new <-data.frame(ID,Percentage) df_new

Output

ID Percentage 1 1 28 2 2 29 3 3 31 4 4 45 5 5 37 6 6 28 7 7 29 8 8 31 9 9 45 10 10 37 11 11 28 12 12 29 13 13 31 14 14 45 15 15 37 16 16 28 17 17 29 18 18 31 19 19 45 20 20 37

Example

df%>%mutate(Rank_Percentage=dense_rank(desc(-Percentage)))

Output

x1 x2 Rank_Percentage 1 2 Winter 1 2 3 Spring 2 3 4 Summer 3 4 2 Rainy 5 5 2 Winter 4 6 3 Spring 1 7 3 Summer 2 8 2 Rainy 3 9 4 Winter 5 10 3 Spring 4 11 4 Summer 1 12 2 Rainy 2 13 3 Winter 3 14 5 Spring 5 15 4 Summer 4 16 3 Rainy 1 17 2 Winter 2 18 2 Spring 3 19 4 Summer 5 20 3 Rainy 4