R: "Sabotaging" a Loop-CodePudding

I am working with the R programming language. Suppose I had infinite time and didn't care if this WHILE LOOP takes forever to run:

library(dplyr)
list_results <- list()
for (i in 1:100){
    
    c1_i = c2_i = c3_i = ctotal_i =  0
    
    while(c1_i   c2_i    c3_i < 15 && nrow_i > 5 ) {
        
        
        num_1_i = sample_n(iris, 30)
        
        
        
        num_2_i = sample_n(iris, 30)
        
        
        num_3_i = sample_n(iris, 30)
        
        
        c1_i = mean(num_1_i$Sepal.Length)
        c2_i = mean(num_2_i$Sepal.Length)
        c3_i = mean(num_3_i$Sepal.Length)
        ctotal_i = c1_i   c2_i    c3_i
        
        combined_i = rbind(num_1_i, num_2_i, num_3_i)
        nrow_i = nrow(unique(combined_i[duplicated(combined_i), ]))
        
    }
    
    inter_results_i <- data.frame(i, c1_i, c2_i, c3_i, ctotal_i, nrow_i)
    list_results[[i]] <- inter_results_i
}

In this case, I would want to this LOOP to ONLY continue to the next iteration if the current iteration produces a result where "c1_i c2_i c3_i > 15" AND "nrow_i <5". I thought I had correctly specified these conditions in this LOOP.

However, every time I run the above LOOP and check the results, nrow_i is never less than 5:

[[100]]
    i     c1_i     c2_i     c3_i ctotal_i nrow_i
1 100 5.796667 6.116667 5.863333 17.77667     19

In my opinion, this LOOP should have never completed running until both of these conditions have met.

My Question: How can I purposefully "sabotage" my LOOP and ensure that "nrow_i" is always less than 5, even if it takes forever to run? How should I write the code for such a scenario - can someone please suggest what to do?

Thanks!

CodePudding user response：

Hope that I have understood the problem correctly and assuming nrow_i = 0, I think this is what you are trying to do

library(dplyr)

list_results <- list()
nrow_i <- 0

for (i in 1:100) {
  
    c1_i <- c2_i <- c3_i <- ctotal_i <- 0
    
    while (TRUE) {
      
        num_1_i <- sample_n(iris, 30)
        num_2_i <- sample_n(iris, 30)
        num_3_i <- sample_n(iris, 30)

        c1_i <- mean(num_1_i$Sepal.Length)
        c2_i <- mean(num_2_i$Sepal.Length)
        c3_i <- mean(num_3_i$Sepal.Length)

        ctotal_i <- c1_i   c2_i   c3_i

        combined_i <- rbind(num_1_i, num_2_i, num_3_i)
        nrow_i <- nrow(unique(combined_i[duplicated(combined_i), ]))

        
        if (ctotal_i > 15 && nrow_i < 5) break
    }
  
    # if (ctotal_i <= 15 && nrow_i >= 5) break
    
    inter_results_i <- data.frame(i, c1_i, c2_i, c3_i, ctotal_i, nrow_i)
    list_results[[i]] <- inter_results_i
}

Here while loop will run till we have ctotal_i > 15 and nrow_i < 5. (using ctotal_i since ctotal_i = c1_i c2_i c3_i) and store the values in list_results and move to next iteration.