Trying to generate a data frame in a for loop - r

Question

Currently I'm working on the following code:
data <- rep(1:3, times = c(10,4,6))
for(i in 1:5) {
samp <- sample(data, 4)
data <- exclude(data, samp)
print(samp)
for(i in 1:3) {
prsamp <- sum(samp == i)/4
print(prsamp)
}
if (length(data) == 0) {
break
}
}
This currently prints out five vectors of length four, with the corresponding probabilities of each number occurring in each vector.
> source("buffoon.R")
> buffoon(20, 4, 3, c(10,4,6))
[1] 1 1 2 3
[1] 0.5
[1] 0.25
[1] 0.25
[1] 1 3 3 2
[1] 0.25
[1] 0.25
[1] 0.5
[1] 2 1 1 1
[1] 0.75
[1] 0.25
[1] 0
[1] 3 1 2 3
[1] 0.25
[1] 0.25
[1] 0.5
[1] 1 3 1 1
[1] 0.75
[1] 0
[1] 0.25
So, for instance, the first vector 1123 gives us a 0.5 prob of 1, 0.25 of 2, and 0.25 of 3. I would like to turn the output into a nice data frame which lists in column 1 each row vector, and in column 2 another row vector corresponding to the respective elemental probability occurrences, but I'm running into many errors. I've been researching this issue for a few hours now, but no success. Any help is appreciated.
My ideal data frame would look like this:
Sample Probability Dist
1 1123 0.5 0.25 0.25
2 1332 0.25 0.25 0.5
and so on, down to row 5.

Answer 1

The first thing you will have to do is create an empty dataframe. Secondly, you will want your for loop to write in this dataframe instead of simply printing out the results directly. Also, you don't want to use a for loop containing i as variable in a for loop already using i. I suggest you try the following:
data <- rep(1:3, times = c(10,4,6))
datafr <- data.frame(Sample=rep(NA,5),Probability.Dist=rep(NA,5))
for(i in 1:5) {
samp <- sample(data, 4)
data <- exclude(data, samp)
datafr$Sample[i] <- samp[1]*1000+samp[2]*100+samp[3]*10+samp[4] #easy way of getting your wanted sample layout
prsamp <- rep(0,3)
for(j in 1:3) {
prsamp[j] <- sum(samp == j)/4
}
datafr$Probability.Dist[i] <- toString(prsamp)
if (length(data) == 0) {
break
}
}
datafr
# Sample Probability.Dist
#1 1231 0.5, 0.25, 0.25
#2 2132 0.25, 0.5, 0.25
#3 1313 0.5, 0, 0.5
#4 2111 0.75, 0.25, 0
#5 3131 0.5, 0, 0.5
I also have to advise you against using 3 values in a single column of a dataframe. For further analysis and even readability, it would be much preferred to give each value it's own column.