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How to find the cosine of each value in columns if some columns are categorical in R data frame?
To find the cosine 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 cosine 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(1:5,25,replace=TRUE) DV2<-sample(1:5,25,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 medium first 3 3 2 medium second 5 3 3 low first 5 4 4 low second 4 3 5 medium first 1 3 6 medium first 2 1 7 medium second 3 5 8 low second 4 1 9 low second 2 1 10 medium first 1 5 11 high first 2 4 12 medium second 5 1 13 medium second 5 1 14 high first 5 3 15 low first 2 5 16 high first 1 1 17 low second 4 2 18 high second 1 5 19 medium second 3 2 20 medium second 1 2 21 high second 5 2 22 medium second 1 4 23 low second 3 1 24 medium first 5 5 25 low first 5 1
Find the cosine of each value in columns if some columns are categorical
Using numcolwise function from plyr package to find the cosine 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(1:5,25,replace=TRUE) DV2<-sample(1:5,25,replace=TRUE) df<-data.frame(Level,Group,DV1,DV2) library(plyr) numcolwise(cos)(df)
Output
DV1 DV2 1 0.5403023 -0.4161468 2 -0.4161468 0.2836622 3 0.2836622 -0.4161468 4 -0.6536436 -0.9899925 5 0.5403023 0.5403023 6 -0.9899925 -0.9899925 7 -0.6536436 0.5403023 8 0.2836622 -0.4161468 9 0.2836622 0.5403023 10 -0.4161468 0.5403023 11 -0.9899925 -0.6536436 12 0.5403023 -0.9899925 13 -0.4161468 -0.4161468 14 0.2836622 0.2836622 15 0.2836622 -0.4161468 16 0.2836622 -0.9899925 17 -0.9899925 -0.4161468 18 -0.9899925 0.2836622 19 0.2836622 0.2836622 20 -0.6536436 -0.9899925 21 -0.6536436 0.5403023 22 -0.9899925 0.2836622 23 -0.4161468 -0.9899925 24 0.2836622 0.2836622 25 -0.6536436 -0.4161468
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