Sampling from a normal distribution using a for loop

So I am trying to sample from a uniform distribution 1000 times each time computing the mean of 20 random samples from said uniform distribution.

Now let's loop through 1000 times, sampling 20 values from a uniform distribution and computing the mean of the sample, saving this mean to a variable called sampMean within a tibble called uniformSampleMeans.
{r 2c}

unif_sample_size = 20 # sample size
n_samples = 1000 # number of samples

# set up q data frame to contain the results
uniformSampleMeans <- tibble(sampMean = runif(n_samples, unif_sample_size))


# loop through all samples.  for each one, take a new random sample, 
# compute the mean, and store it in the data frame

for (i in 1:n_samples){
  uniformSampleMeans$sampMean[i] = summarize(uniformSampleMeans = mean(unif_sample_size))
}

I successfully generate a tibble, however the values are "NaN". Additionally when I get to my for loop I get an error.

Error in summarise_(.data, .dots = compat_as_lazy_dots(...)) : argument ".data" is missing, with no default

Any insight would be much appreciated!

Solution

Given that you've tagged this as a dplyr question, you can use summarise_all:

library(dplyr)

n_obs = 20 
n_samples = 1000 

samples <- data.frame(matrix(runif(n_obs * n_samples), nrow = 20))

summarise_all(samples, mean)

As others have noted, it's possible to do this in base R, too.

Update Per OP comment
Yes, possible to use a for loop, although not advisable. Here's one approach:

unif_sample_size = 20 
n_samples = 1000 
total_draws <- unif_sample_size * n_samples

uniformSampleMeans <- 
  tibble(draw_from_uniform = runif(n_samples * unif_sample_size))

sample_means <- vector(length = n_samples)

i <- 1
for (ix in seq(1, total_draws, by = unif_sample_size)) {
  start <- ix
  end <- ix + unif_sample_size - 1
  sample_means[i] <- mean(uniformSampleMeans$draw_from_uniform[start:end])
  i <- i + 1
}