Creating a function that prints out multiple data-frames in r - r

Question

I have a function that I have written to create a simulation that demonstrates the central limit theorem. I'm not sure if its possible or if I am better off just making separate functions but currently it only stores that data frame containing the mean values of all the trials.
# create function to perform CLT simulation
# where n = sample size, t = number of trials, pop = which population is being used, popmean = population mean,
cltsim <- function(n, t, pop, popmean, popsd, poptitle){
popsim <- data.frame()
# Run the simulation
for(i in n) { # for each value of n
col <- c()
for(j in t) { #loop through each value of t
trial <- 1:j
counter <- j #set up an egg timer based on whichever t value we're on
value <- c()
while(counter > 0) { # and extract n samples from the population
bucket <- sample(pop, i, replace = TRUE)
xbar <- mean(bucket) #calculate the sample mean
value <- c(value, xbar) # and add it to a vector
counter <- counter - 1 #egg timer counts down and loops back until it hits 0
}
sbar <- sd(value) #calculate the sample standard deviation
col <- cbind(trial, value, sbar, i, j) #merge all info together
popsim <- rbind(popsim, col) # attach it to empty dataframe
}
}
#clean up so just the finished data frame is left
rm(col, bucket, value, counter, i, j, n, sbar, t, xbar, trial)
#tidy up data frame in order to graph it
names(popsim) <- c("trial#", "value", "sdev", "samples", "trials")
#view the rows of data in popsim data table
popsim
}
when I try to add any more code that requires creating datatables it doesnt store them, below are the blocks of code I wish to add to the function
g1 <- ggplot(popsim, aes(x = value)) + geom_density(fill = "#09AB30") +
facet_grid(samples ~ trials, labeller = label_both) +
ggtitle(paste("Demonstrating The Central Limit Theorem with Simulation using", poptitle)) +
geom_vline(xintercept = popmean, linetype = "dashed")
g1
and
#create data frame of simulated sample standard deviations \
sdmatrix <- matrix(unique(popsim$sdev), nrow = 4, ncol = 4)
sdf <- as.data.frame(sdmatrix, row.names = c("t10", "t100", "t1000", "t10000"))
names(sdf) <- c("s1", "s10", "s30", "s50")
sdf <- t(sdf)
rm(sdmatrix)
sdf
exvals <- pop1sd/sqrt(c(1, 10, 30, 50))
dfex <- as.data.frame(exvals, row.names = c("s1", "s10", "s30", "s50"))
names(dfex) <- "Predicted Standard Deviations"
dfex
Ive had a look around and I cant find a solution anywhere, am I better off just writing different functions for them? Any advice or input on how to make this lot of code more effective/efficient would be greatly appreciated.
thanks in advance