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How to find the square root of each value in columns if some columns are categorical in R data frame?
To find the square root of each value in columns if some columns are categorical in R data frame, we can follow the below steps −
First of all, create a data frame.
Then, use numcolwise function from plyr package to find the square root of each value in columns if some columns are categorical.
Example
Create the data frame
Let’s create a data frame as shown below −
Level<-sample(c("low","medium","high"),25,replace=TRUE) Group<-sample(c("first","second"),25,replace=TRUE) DV1<-sample(c(1,4,9,16,25,36,49,64,81,100),25,replace=TRUE) DV2<-sample(c(1,4,9,16,25,36,49,64,81,100),25,replace=TRUE) df<-data.frame(Level,Group,DV1,DV2) df
Output
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
Level Group DV1 DV2 1 high second 49 64 2 medium second 64 64 3 high second 64 4 4 medium second 81 49 5 medium second 9 64 6 low second 64 4 7 low second 49 64 8 high first 100 81 9 high second 49 9 10 high second 81 1 11 medium second 64 64 12 low second 64 4 13 high first 4 81 14 medium second 100 1 15 low first 16 16 16 high second 64 36 17 high first 81 16 18 high first 16 9 19 high first 81 1 20 high second 100 16 21 low second 25 9 22 medium first 36 16 23 high first 100 64 24 high first 25 36 25 high second 1 49
Find the square root of each value in columns if some columns are categorical
Using numcolwise function from plyr package to find the square root of each value in columns if some columns are categorical in the data frame df −
Level<-sample(c("low","medium","high"),25,replace=TRUE) Group<-sample(c("first","second"),25,replace=TRUE) DV1<-sample(c(1,4,9,16,25,36,49,64,81,100),25,replace=TRUE) DV2<-sample(c(1,4,9,16,25,36,49,64,81,100),25,replace=TRUE) df<-data.frame(Level,Group,DV1,DV2) library(plyr) numcolwise(sqrt)(df)
Output
DV1 DV2 1 7 8 2 8 8 3 8 2 4 9 7 5 3 8 6 8 2 7 7 8 8 10 9 9 7 3 10 9 1 11 8 8 12 8 2 13 2 9 14 10 1 15 4 4 16 8 6 17 9 4 18 4 3 19 9 1 20 10 4 21 5 3 22 6 4 23 10 8 24 5 6 25 1 7
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