How can I add and calculate a column's value in R - r

Question

This is the first part of my code:
BSum=0.0
mydata = NULL
while(BSum < 5)
{
A=(rpois (1, lambda=1))
y=runif(A,0,1)
B1 = length(which(y<=0.5))
BSum = BSum + B1
C = A - B1
mydata=rbind(mydata,c("A"=A,"B"=B1,"C"=C))
}
I need 3 more columns here. For column D(row x) I would generate as many random nos. (between 0 and 1) as is the value in Column B(row x). Then I see how many of those random nos. are less than or equal to 0.1. I put the total count of these in Column D. The remainder (B-D) becomes column F. I generate another column E that will get populated the same same way D was generated from B. The remainder again gets added to what had accumulated in Column F.

Answer 1

:= is from the data.table package. As you don't have this loaded, either your object isn't a data.table object or it is but you don't say and the package was not loaded.
If the former (your object is a data frame, not a data.table) then you want cbind(). As in:
set.seed(1)
df <- data.frame(A = runif(10))
cbind(df, list(B = runif(10), C = letters[1:10]))
> cbind(df, list(B = runif(10), C = letters[1:10]))
A B C
1 0.26550866 0.2059746 a
2 0.37212390 0.1765568 b
3 0.57285336 0.6870228 c
4 0.90820779 0.3841037 d
5 0.20168193 0.7698414 e
6 0.89838968 0.4976992 f
7 0.94467527 0.7176185 g
8 0.66079779 0.9919061 h
9 0.62911404 0.3800352 i
10 0.06178627 0.7774452 j
For your particular problem, try:
myfun <- function(z) {
ret1 <- apply(z, 1, function(x) sum(runif(x) <= 0.1))
ret2 <- z[,1] - ret1
cbind(z, B = ret1, C = ret2)
}
set.seed(1)
df <- data.frame(A = rpois(10, 2))
myfun(df)
> myfun(df)
A B C
1 1 0 1
2 1 0 1
3 2 0 2
4 4 0 4
5 1 0 1
6 4 0 4
7 4 1 3
8 2 0 2
9 2 0 2
10 0 0 0
You could make this more efficient, say by not doing each row individually, but it'd involve more coding.
Updated
If I understand your update (and I might not as I already showed you how to do those steps, though not in the same configuration as you now want), then I think this is what you wanted. Note that how you create E is a little ambiguous. I took you literally and just did exactly the same as for D.
set.seed(2)
BSum <- 0.0
mydata <- NULL
while(BSum < 5) {
A <- rpois(1, lambda = 1)
B1 <- sum(runif(A, 0, 1) <= 0.5)
BSum <- BSum + B1
C <- A - B1
D <- sum(runif(B1) <= 0.1)
F <- B1 - D
E <- sum(runif(B1) <= 0.1)
F <- F + (D - E)
mydata <- rbind(mydata, c(A = A, B = B1, C = C, D = D, E = E, F = F))
}
With that seed I get
R> mydata
A B C D E F
[1,] 0 0 0 0 0 0
[2,] 1 0 1 0 0 0
[3,] 0 0 0 0 0 0
[4,] 3 1 2 0 0 1
[5,] 1 1 0 0 0 1
[6,] 1 0 1 0 0 0
[7,] 3 3 0 0 0 3