nested loop doesn't work

Question

I have a df that have three columns and several rows. I am trying to create a list using the below loop but it is failing at the step where i need to populate the list with more than one result. Can somebody point to me where i am going wrong?

Here is my subset of df

       Gene      kaks chr
1 Bra011025 0.5909820 A01
2 Bra011027 0.3684600 A01
3 Bra011028 0.2126320 A01
4 Bra011030 0.0910217 A01
5 Bra011033 0.2412330 A01
6 Bra011034 0.1092790 A01 

And here is my loop

results <- list()
chro <- c("A01", "A02", "A03", "A04", "A05", "A06", "A07", "A08", "A09", "A10")

for(i in 1:10) {
  for(j in c("A01", "A02", "A03", "A04", "A05", "A06", "A07", "A08", "A09", "A10")) {
      simulated.index <- sample(1:nrow(allData),sum(allData$chr==j))
      simulated.kaks <- allData$kaks[simulated.index]
      simulatedNot.kaks <- allData$kaks[-simulated.index]
      results[[j]] <- mean(simulated.kaks)-mean(simulatedNot.kaks)
  } 
}

The output contains only one value..

> head(results)
$A01
[1] 0.003432181

$A02
[1] -0.03501376

$A03
[1] -0.0003581717

$A04
[1] -0.01792963

$A05
[1] -0.01241799

$A06
[1] 0.002551261

Answer 1

You can do the simulation for each index j, using the lapply function (and an example with more than one chr value...)

allData = read.table(text="       Gene      kaks chr
1 Bra011025 0.5909820 A01
2 Bra011027 0.3684600 A01
3 Bra011028 0.2126320 A01
4 Bra011030 0.0910217 A02
5 Bra011033 0.2412330 A02
6 Bra011034 0.1092790 A02", header=T)
setNames(lapply(unique(allData$chr), function(j) {
  sapply(1:10, function(i) {
      simulated.index <- sample(1:nrow(allData),sum(allData$chr==j))
      simulated.kaks <- allData$kaks[simulated.index]
      simulatedNot.kaks <- allData$kaks[-simulated.index]
      mean(simulated.kaks)-mean(simulatedNot.kaks)
  })
}), unique(allData$chr))
# $A01
#  [1]  0.15869543 -0.16243990 -0.08979343  0.15869543 -0.07762190 -0.07072610
#  [7] -0.05855457 -0.26258077 -0.15869543 -0.07072610
# 
# $A02
#  [1]  0.15869543 -0.07762190  0.01034743  0.17461143 -0.24351343  0.24351343
#  [7]  0.24351343 -0.17461143  0.05855457  0.17461143

Answer 2

I think you have to make two lists.

results_A <- list()
results_B <- list()
for(i in 1:5) {
  for(j in 1:5) {
    results_A[[j]] <- i*j
  }
  results_B[[i]] <- results_A
}

results_B