Replace empty columns with NA's in nested lists within lists

Question

I have produced 10 nested lists within lists using two functions (below) - produced from dummy data called normalised_scores. Each nested list contains two empty columns called Predicted and Actual (code below). I would like to replace these empty columns with NA's. I tried to achieve this with a function (Shuffle100) but got error messages (below). After looking online for a long time, I cannot find an efficient method apart from using long-winded code (below). If anyone can help, then thank you.

Dummy data

   Family=rep(c("G8", "V4"), each=40)
   x <- matrix(rnorm(960), ncol=12)
   normalised_scores <- cbind(Family, x)
   colnames(my_data)<-c("Family",
                        "Swimming", 
                        "Not.Swimming",
                        "Running", 
                        "Not.Running",
                        "Fighting",
                        "Not.Fighting",
                        "Resting",
                        "Not.Resting",
                        "Hunting",
                        "Not.Hunting",
                        "Grooming",
                        "Not.Grooming")

I wrote this function below to produce these 10 nested lists from classification tree models using rpart and caret packages. I tried to produce two columns (Predicted and Actual) filled with NA'susing this function but without success.

 library(caret)
 library(e1071)
 library(rpart)

shuffle100 <-lapply(seq(10), function(n){ #Select the production of 10 dataframes
subset <- normalised_scores[sample(nrow(normalised_scores), 80),] #Shuffle  rows
subset_idx <- sample(1:nrow(subset), replace = FALSE)
subset <- subset[subset_idx, ] #training subset
subset1<-subset[-subset_idx, ] #test subset
subset_resampled_idx <- createDataPartition(subset_idx, times = 1, p = 0.7, list = FALSE) #70 % training set    
subset_resampled <- subset[subset_resampled_idx, ]
ct_mod<-rpart(Family~., data=subset_resampled, method="class",  control=rpart.control(cp=0.005)) #10 ct
ct_pred<-predict(ct_mod, newdata=subset[, 2:13]) 
ct_dataframe=as.data.frame(ct_pred) #create new data frame
ct_dataframe$Predicted=NA
ct_dataframe$Actual=NA
})

Produce two empty columns called Predicted and Actual

my_list <- lapply(shuffle100, function(df){#Create two new columns Predicted and Actual
           if (nrow(df) > 0)
           cbind(df, Predicted = c(""), Actual = c(""))
           else
           cbind(df, Predicted = character(), Actual = c(""))
})

Error messages from the function Shuffle100 and much of the same (lists 1-10):

               [[1]]
                [1] NA

               [[2]]
                [1] NA

               [[3]]
                [1] NA
          

Is there a more efficient way of doing this?

          #Insert NA's

          my_list[[1]]$Predicted<-NA
          my_list[[1]]$Actual<-NA

          my_list[[2]]$Predicted<-NA
          my_list[[2]]$Actual<-NA

          my_list[[3]]$Predicted<-NA
          my_list[[3]]$Actual<-NA

          my_list[[4]]$Predicted<-NA
          my_list[[4]]$Actual<-NA

          my_list[[5]]$Predicted<-NA
          my_list[[5]]$Actual<-NA

          my_list[[6]]$Predicted<-NA
          my_list[[6]]$Actual<-NA

          my_list[[7]]$Predicted<-NA
          my_list[[7]]$Actual<-NA

          my_list[[8]]$Predicted<-NA
          my_list[[8]]$Actual<-NA

          my_list[[9]]$Predicted<-NA
          my_list[[9]]$Actual<-NA

          my_list[[10]]$Predicted<-NA
          my_list[[10]]$Actual<-NA

        

Answer 1

We can use transform

lapply(my_list, transform, Predicted=NA, Actual=NA)

Answer 2

Using dplyr's mutate function, try

    library(dplyr)
    lapply(my_list, function(x) mutate(x, Predicted = NA, Actual = NA)