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How to add columns with square of each column in R data frame?
To add columns with square of each column in R data frame, we can use setNames function and cbind function for squaring each value. This might be required when we want to use squared form of variables in the data analysis.
Check out the below given examples to understand how it can be done.
Example 1
Following snippet creates a sample data frame −
x1<-rpois(20,2) x2<-rpois(20,5) x3<-rpois(20,1) df1<-data.frame(x1,x2,x3) df1
Output
The following dataframe is created −
x1 x2 x3 1 2 7 1 2 3 6 1 3 4 5 2 4 1 2 0 5 0 6 0 6 2 3 1 7 1 4 2 8 2 4 1 9 1 3 0 10 2 3 1 11 1 5 2 12 3 3 1 13 3 4 0 14 1 7 1 15 3 3 3 16 2 5 0 17 2 8 2 18 0 5 3 19 6 1 0 20 4 4 3
To add squared columns in df1, add the following code to the above snippet −
x1<-rpois(20,2) x2<-rpois(20,5) x3<-rpois(20,1) df1<-data.frame(x1,x2,x3) setNames(as.data.frame(cbind(df1,df1^2)), c(names(df1),paste0(names(df1),'_2')))
Output
If you execute all the above given codes as a single program, it generates the following output −
x1 x2 x3 x1_2 x2_2 x3_2 1 2 7 1 4 49 1 2 3 6 1 9 36 1 3 4 5 2 16 25 4 4 1 2 0 1 4 0 5 0 6 0 0 36 0 6 2 3 1 4 9 1 7 1 4 2 1 16 4 8 2 4 1 4 16 1 9 1 3 0 1 9 0 10 2 3 1 4 9 1 11 1 5 2 1 25 4 12 3 3 1 9 9 1 13 3 4 0 9 16 0 14 1 7 1 1 49 1 15 3 3 3 9 9 9 16 2 5 0 4 25 0 17 2 8 2 4 64 4 18 0 5 3 0 25 9 19 6 1 0 36 1 0 20 4 4 3 16 16 9
Example 2
Following snippet creates a sample data frame −
y1<-round(rnorm(20),2) y2<-round(rnorm(20),2) df2<-data.frame(y1,y2) df2
Output
The following dataframe is created −
y1 y2 1 -0.05 0.98 2 -1.54 -1.98 3 0.66 0.61 4 -0.54 1.22 5 0.34 -0.70 6 -0.26 0.84 7 0.19 -0.20 8 1.39 1.11 9 -1.11 -0.61 10 0.88 -0.10 11 -0.74 -0.03 12 -1.35 -0.94 13 0.99 -1.93 14 1.56 -2.36 15 -0.36 0.95 16 0.38 0.69 17 -0.07 -0.95 18 -0.28 0.68 19 -1.10 0.82 20 0.41 -0.27
To add squared columns in df2, add the following code to the above snippet −
y1<-round(rnorm(20),2) y2<-round(rnorm(20),2) df2<-data.frame(y1,y2) setNames(as.data.frame(cbind(df2,df2^2)), c(names(df2),paste0(names(df2),'_2')))
Output
If you execute all the above given codes as a single program, it generates the following output −
y1 y2 y1_2 y2_2 1 -0.05 0.98 0.0025 0.9604 2 -1.54 -1.98 2.3716 3.9204 3 0.66 0.61 0.4356 0.3721 4 -0.54 1.22 0.2916 1.4884 5 0.34 -0.70 0.1156 0.4900 6 -0.26 0.84 0.0676 0.7056 7 0.19 -0.20 0.0361 0.0400 8 1.39 1.11 1.9321 1.2321 9 -1.11 -0.61 1.2321 0.3721 10 0.88 -0.10 0.7744 0.0100 11 -0.74 -0.03 0.5476 0.0009 12 -1.35 -0.94 1.8225 0.8836 13 0.99 -1.93 0.9801 3.7249 14 1.56 -2.36 2.4336 5.5696 15 -0.36 0.95 0.1296 0.9025 16 0.38 0.69 0.1444 0.4761 17 -0.07 -0.95 0.0049 0.9025 18 -0.28 0.68 0.0784 0.4624 19 -1.10 0.82 1.2100 0.6724 20 0.41 -0.27 0.1681 0.0729
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