Hi
In advance, thank you!
I'm pretty new to R so please don't judge too much 
I've written a loop that is similar to a MonteCarlo simulation, my problem is that it takes 6 minutes for 100.000 loops, and 4 hours for 500.000 loops. but i don't understand why it is so ineffective.
Is there a better way to do this and obtain the same result?
#Event 1
E1LB<-200000
E1UB<-150000000
E1Chance<-0.10
#Event 2
E2LB<-50000
E2UB<-7000000
E2Chance<-0.10
#Event 3
E3LB<-20000
E3UB<-8500000
E3Chance<-0.15
#Event 4
E4LB<-2000
E4UB<-5000000
E4Chance<-0.25
#Event 1 mean and sd
LnE1LB<-log(E1LB)
LnE1UB<-log(E1UB)
meanE1<-(LnE1LB+LnE1UB)/2
sdE1<-(LnE1UB-LnE1LB)/3.29
#Event 2 mean and sd
LnE2LB<-log(E2LB)
LnE2UB<-log(E2UB)
meanE2<-(LnE2LB+LnE2UB)/2
sdE2<-(LnE2UB-LnE2LB)/3.29
#Event 3 mean and sd
LnE3LB<-log(E3LB)
LnE3UB<-log(E3UB)
meanE3<-(LnE3LB+LnE3UB)/2
sdE3<-(LnE3UB-LnE3LB)/3.29
#Event 4 mean and sd
LnE4LB<-log(E4LB)
LnE4UB<-log(E4UB)
meanE4<-(LnE4LB+LnE4UB)/2
sdE4<-(LnE4UB-LnE4LB)/3.29
#Simulation with 100.000 koops
for (k in 1:100000) {
Event1<-ifelse (runif(1)<E1Chance, qlnorm(runif(1),meanE1,sdE1), 0)
Event2<-ifelse (runif(1)<E2Chance, qlnorm(runif(1),meanE2,sdE2), 0)
Event3<-ifelse(runif(1)<E3Chance, qlnorm(runif(1),meanE3,sdE3), 0)
Event4<-ifelse(runif(1)<E4Chance, qlnorm(runif(1),meanE4,sdE4), 0)
results<- rbind(results,data.frame(Event1, Event2, Event3, Event4))
}