[R] Trying to speed up an if/else statement in simulations

[nqf]

Dear R-help,

I am trying to write a function to simulate datasets of size n which contain
two time-to-event outcome variables with associated 'Event'/'Censored'
indicator variables (flag1 and flag2 respectively). One of these indicator
variables needs to be dependent on the other, so I am creating the first and
trying to use this to create the second using an if/else statement. 

My data structure needs to follow this algorithm (for each row of the data):
If flag1=1 then flag2 should be 1 with probability 0.95 and zero otherwise
Else if flag1=0 then flag2 should be 1 with probability 0.5 and zero
otherwise

I can set up this example quite simply using if else statements, but this is
incredibly inefficient when running thousands of datasets:
data<-as.data.frame(rbinom(10,1,0.5))
colnames(data)<-'flag1'
for (i in 1:n) {
  if (data$flag1[i]==1) {data$flag2[i]<-rbinom(1,1,0.95)} else
{data$flag2[i]<-rbinom(1,1,0.5)}  
 }


I think to speed up the simulations I would be better changing to
vectorisation and using something like:
ifelse(data$flag1==1,rbinom(1,1,0.95),rbinom(1,1,0.5)) 
but the rbinom statements here generate one value and repeat this draw for
every element of flag2 that matches the 'if' statement on flag1.

Is there a way to assign flag2 to a new bernoulli draw for each subject in
the data frame with flag1=1?

I hope my question is clear, and thank you in advance for your help. 

Thanks,
Natalie
PhD student, Reading University

P.S. I am using R 2.12.1 on Windows 7.


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