How to loop through all rows and specific columns to compute a function?-CodePudding

I am not sure why I am getting the error " longer object length is not a multiple of shorter object length"? I have run a similar code with different dataframes and it seemed to work.

 Data: 
   feature   mean   sample1  sample2 sample3
     a          3        4       8       7  
     b          4        3.4     3       6
     c          5        2       5       7

Here is the code I am working with:

      datalist6 = matrix( nrow = length(unique(Data$feature)), ncol = length(sub), 
                dimnames = list(unique(Data$feature), unique(sub)))

                sub <- colnames(Data[,c(3:46)])
                for(i in Data$feature) {
                        for (j in sub){
                        subset <- filter(Data, feature==i & sub==j)
                         func <- j/subset$mean
              datalist6[i,j] <- func
                     }
               }

This code will then throw that error. Essentially I am trying to compute coefficient of variance with mean in column subset$mean and variance of samples in columns 3:46 (sub). I am trying to run through nested for loops to determine the coefficient of variance for each row (features) for each column.

TIA!

CodePudding user response：

No for loop required.

With the assumption that you are using dplyr,

library(dplyr)
summarize(Data, feature, across(sample1:sample3, ~ . / mean))
#   feature  sample1  sample2  sample3
# 1       a 1.333333 2.666667 2.333333
# 2       b 0.850000 0.750000 1.500000
# 3       c 0.400000 1.000000 1.400000

If you instead need a base R solution,

cbind(Data["feature"], subset(Data, select = sample1:sample3) / Data$mean)
#   feature  sample1  sample2  sample3
# 1       a 1.333333 2.666667 2.333333
# 2       b 0.850000 0.750000 1.500000
# 3       c 0.400000 1.000000 1.400000