Performing simulations and combining the data into one data frame - r

Question

For each reported study, I want to do 1000 simulations of a parameter X using normal or log-normal distribution (based on a flag) and then combine all the simulations in one data frame. I am looking for an automated way of doing this.
What I have is a data frame with the following columns:
SOURCE NSUB MEAN SD DIST
Study1 10 1.5 0.3 0
Study2 5 2.5 0.4 1
Study1 4 3.5 0.3 0
when DIST==0 then it is normal distribution, if DIST==1 then it is log-normal.
I am able to do the simulations and combine them using hard coding: for example:
#for Study1:
set.seed <-1
NSUB <- 10
MEAN <- 1.5
SD <- 0.3
DIST <- 0 #Normal distribution
df1 <- data.frame("SOURCE"="Study1","NSUB"=NSUB,"DIST"=DIST, "VALUE" = rnorm(1000, mean=MEAN, sd=SD))
#For study2
set.seed <-2
NSUB <- 5
MEAN <- 2.5
SD <- 0.4
DIST <- 1 #log-normal distribution
df2 <- data.frame("SOURCE"="Study2","NSUB"=NSUB,"DIST"=DIST, "VALUE" = rlnorm(1000, meanlog=log(MEAN), sdlog=SD))
#Combine all
dfall <- rbind(df1,df2)
However, this would be tedious to me I have alot of reported means and SD for the parameter. I need help in how to make this automated so it does 1000 simulation for each row (using MEAN and SD) and then combine all simulated data in one data frame.

Answer 1

In the interest of implementing readable and general code, you should do two things here:
Write a function that takes each row of your simulation configuration dataset and returns the simulated values as a data_frame (doSim below). This makes it easier to test your simulation code separately from your iteration over simulation configurations.
Use dplyr to pass each row of the function to this function, and collect up the results as a data_frame.
Here is some sample code:
library(dplyr)
# read in the simultation configuration dataset
dfX = read.table(textConnection("
SOURCE NSUB MEAN SD DIST
Study1 10 1.5 0.3 0
Study2 5 2.5 0.4 1
Study1 4 3.5 0.3 0"),
header = TRUE, stringsAsFactors = FALSE)
# write a function that takes each row of the configuration
# data.frame and returns the simulations
doSim = function(simConfig, seed = 12345) {
set.seed(seed)
dist = if(simConfig[["DIST"]] == 0) rnorm else rlnorm
mean = if(simConfig[["DIST"]] == 0) simConfig[["MEAN"]] else log(simConfig[["MEAN"]])
return(
data_frame(
source = simConfig[["SOURCE"]],
nsub = simConfig[["NSUB"]],
value = dist(1000, mean = mean, sd = simConfig[["SD"]])
)
)
}
# test the function
doSim(dfX[1, ])
# apply over dfX
dfX %>%
rowwise() %>%
do(doSim(.))