How to loop multiple Normal distributions with Bartlett tests and ANOVAs?

I am trying to simulate data for a single factor ANOVA by simulating data from a Normal distribution assuming 3 groups in the ANOVA with n=10 observations at each group. The following means {5,5,5} and standard deviations {5,10,15} are used for the three groups. I then want to test the assumption of homoscedasticity using a Bartlett's test, followed by an F-test.

I want to repeat these steps 100,000 times in a for loop while also saving the results for the Bartlett's test and F-test in a vectors.

I've run the below code in R, however the normal distributions are only saving 1 observation per group instead of 10 observations per group. This is stopping the rest of the code from running properly.

v <- 100000 #create a vector for 100000 to be used in the for loop

#create empty vectors to store the repeated values from the for loop in
Storage <- c()
Group1 <- c() 
Group2 <- c()
Group3 <- c()
b.test2 <- c()
f.test2 <- c()
fit2 <- c()

# Loop through a normal distribution for 3 groups 100,000 times
for (i in 1:v) {
  # Generate random data from a normal distribution for each group
  Group1[i] <- rnorm(n = 10, mean = 5, sd = 5)
  Group2[i] <- rnorm(n = 10, mean = 5, sd = 10)
  Group3[i] <- rnorm(n = 10, mean = 5, sd = 15)

  # Combine the three groups into a single data frame
  data2 <- data.frame(Group1, Group2, Group3)

  # Pivot the simulated data into a long data set prior to running the ANOVA and Bartlett test
      data.long2 <- data2 %>%
        pivot_longer(cols = c("Group1":"Group3"), names_to = "group", values_to = "num" )

  # Check for homoscedasticity of the residuals using a Bartlett test and save the results into a     vector
  b.test2[i] <- bartlett.test(data2)

  # Create the first model fit for the simulated data
  fit2[i] <- lm(num~group, data = data2)

  # Save the p-value from the F-test in a vector
  f.test2[i] <- Anova(fit2, type=3)[1,4]

}

since you are trying to do these analyses on individual groups you need to store your data in lists (or subgroups)

here is a different approach that is dependent on using purrr and dplyr - it avoids the looping

first create a function for generating your data

group_fun <- function(n){
  data.frame(group = rep(c("group1", "group2", "group3"), each=n),
             num = c(rnorm(n,5,5), rnorm(n,5,10), rnorm(n,5,15)))
}

then replicate the data function the desired # of times (i'm using 20 for brevity)

n = 10

1:20 %>%                                     # number of iterations
  map(., ~ group_fun(n)) %>%                 # run the data function once for each iteration
  tibble::enframe(.) %>%                     # collapse for a single list for each iter
  mutate(fit  = map(value, ~lm(num~group, data = .)),
         anov = map(fit, anova),.            # run models
         bart = map(value, ~bartlett.test(num~group, data = .))) %>% 
  mutate(ftest = map(anov, broom::tidy),
         bart = map(bart, broom::tidy),      # process resulsts
         summary = map(fit, broom::tidy)) %>% 
  unnest(ftest)                              # pull out results

since some of the model outputs have the same names you could use names_repair if you want more than one output unnest(c(ftest, bart), names_repair = "universal")

there are some slightly more efficient ways to do this but this should get you started