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How to find the position of maximum of each numerical column if some columns are categorical in R data frame?
To find the position of maximum of each numerical column 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 maximum of each numerical column if some columns are categorical.
Example 1
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<-rpois(25,5)
DV2<-rpois(25,10)
df1<-data.frame(Level,Group,DV1,DV2)
df1Output
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 8 7 2 low first 6 11 3 high first 2 14 4 medium second 3 11 5 low second 4 10 6 medium second 7 7 7 high second 4 15 8 low second 3 8 9 high second 5 6 10 medium second 3 13 11 medium second 1 13 12 low first 3 10 13 high first 6 10 14 high first 5 14 15 medium first 10 11 16 low first 6 7 17 medium second 7 10 18 high second 5 11 19 medium second 4 11 20 low first 5 13 21 medium first 2 9 22 medium first 6 12 23 low second 5 8 24 low second 6 10 25 low second 2 6
Find the maximum of each column if some columns are categorical
Using numcolwise function from plyr package to find the maximum of each numerical column if some columns are categorical in data frame df1 −
Level<-sample(c("low","medium","high"),25,replace=TRUE)
Group<-sample(c("first","second"),25,replace=TRUE)
DV1<-rpois(25,5)
DV2<-rpois(25,10)
df1<-data.frame(Level,Group,DV1,DV2)
library(plyr)
numcolwise(which.max)(df1)Output
DV1 DV2 1 24 15
Example 2
Create the data frame
Let’s create a data frame as shown below −
factor1<-sample(c("Super","Lower","Medium"),25,replace=TRUE)
factor2<-sample(c("I","II","III"),25,replace=TRUE)
v1<-rnorm(25)
v2<-rnorm(25)
df2<-data.frame(factor1,factor2,v1,v2)
df2Output
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
factor1 factor2 v1 v2 1 Lower II -0.88708231 0.30097842 2 Super I -1.15358512 -0.50595244 3 Lower II -0.07962128 -0.74934137 4 Super I -1.48634012 0.19566058 5 Lower III 1.14577383 -1.09185066 6 Super II 0.88951251 -0.02418110 7 Lower III 0.13711621 -1.02686656 8 Super I 0.27011965 1.26320650 9 Medium III 0.16775174 -1.92041942 10 Medium III -0.15766279 1.26627694 11 Medium I -1.23267080 -0.93831033 12 Medium II 0.38065869 2.09701663 13 Medium I -1.45391083 -0.08486117 14 Lower III 0.80940837 -1.06338634 15 Medium II 0.20411080 -0.29534513 16 Lower III 0.59453629 2.64966638 17 Medium III 0.31227512 1.68916757 18 Lower I 2.89731076 0.96783335 19 Super III -0.06000641 0.58903660 20 Lower III 0.92520811 -1.03121594 21 Medium III 1.85323653 -1.33632487 22 Medium II 1.13713484 -1.27496569 23 Super I 0.52744948 0.28164512 24 Lower I 0.17266053 0.57324301 25 Lower II 2.67321967 -1.80427360
Find the maximum of each column if some columns are categorical
Using numcolwise function from plyr package to find the maximum of each numerical column if some columns are categorical in data frame df2 −
factor1<-sample(c("Super","Lower","Medium"),25,replace=TRUE)
factor2<-sample(c("I","II","III"),25,replace=TRUE)
v1<-rnorm(25)
v2<-rnorm(25)
df2<-data.frame(factor1,factor2,v1,v2)
library(plyr)
numcolwise(which.max)(df2)Output
v1 v2 1 18 16
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