How to find the range of columns if some columns are categorical in R data frame?

To find the range of 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 range of 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<-rnorm(25) DV2<-rnorm(25) 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 low first 0.4545623484 1.22457875 2 medium first 2.4889402150 1.32313305 3 high second -0.3256057111 -0.16534155 4 high first 0.2666232706 0.39905496 5 medium second 0.2505499602 0.16705061 6 medium second 1.4470741152 -0.95407901 7 medium second -0.6963398452 -1.63102625 8 low second -0.3689070319 0.89160781 9 low first 0.0205268260 -0.19421709 10 high second -0.2771581159 -0.58141607 11 medium first 0.3217334614 -2.01628121 12 low first -1.0149160270 -1.58027196 13 high second 0.2073933789 0.39786787 14 high second -0.7404056228 -0.36160167 15 high second 0.5608725981 0.13807999 16 medium second 0.1696982325 -2.17273629 17 medium first 0.3139221353 0.87757664 18 high first 0.2133404248 0.73458995 19 medium first -0.0001034651 0.32130203 20 medium second 1.2085469946 1.55984002 21 high first 0.0703214269 0.04091434 22 low first 0.2014678620 -0.96800566 23 low first -0.8416639293 0.96249702 24 low first -0.3355991743 1.63965559 25 low first 0.2084838973 -2.09570685

Find the range if some columns are categorical

Using numcolwise function from plyr package to find the range of numerical 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<-rnorm(25) DV2<-rnorm(25) df<-data.frame(Level,Group,DV1,DV2) library(plyr) numcolwise(range)(df)

Output

 DV1 DV2 1 -1.014916 -2.172736 2 2.488940 1.639656