Hi,

I am having some difficulty obtaining summary estimates across elements of a nested list. See below a very simplified example below. Essentially I am want to access the elements within a list and manipulate them easily (e.g. get means across the lists, etc...). Sometimes the elements are in the same position and sometimes and sometimes they are in different positions. I am pretty sure there is much easier and straightforward way to this in the tidyverse (or base R) but I can't seem to be able to find it. I would be grateful if you could help me solve this issue. thank you

```
library(reprex)
library(tidyverse)
#1) Creating a certain function that does some simulations
to_estimate <- function(){
mean_age_female <- rnorm(1, mean=27, sd=1)
mean_age_male <- rnorm(1, mean=30, sd=1)
return(list(sim=cbind(mean_age_female,mean_age_male)))
}
to_estimate()
#> $sim
#> mean_age_female mean_age_male
#> [1,] 26.35325 31.04636
#2) replicating these simulations and obtain estimates (mean, sd, percentile)
rep=4
res <- vector(mode = "list", length = rep)
set.seed(123)
for (i in 1:rep){
res[[i]] <- to_estimate()
}
res
#> [[1]]
#> [[1]]$sim
#> mean_age_female mean_age_male
#> [1,] 26.43952 29.76982
#>
#>
#> [[2]]
#> [[2]]$sim
#> mean_age_female mean_age_male
#> [1,] 28.55871 30.07051
#>
#>
#> [[3]]
#> [[3]]$sim
#> mean_age_female mean_age_male
#> [1,] 27.12929 31.71506
#>
#>
#> [[4]]
#> [[4]]$sim
#> mean_age_female mean_age_male
#> [1,] 27.46092 28.73494
#Summarizing the results
means_female <- mean(res[[1]]$sim[,"mean_age_female"],
res[[2]]$sim[,"mean_age_female"],
res[[3]]$sim[,"mean_age_female"],
res[[4]]$sim[,"mean_age_female"])
sd_female <-sd(c(res[[1]]$sim[,"mean_age_female"],
res[[2]]$sim[,"mean_age_female"],
res[[3]]$sim[,"mean_age_female"],
res[[4]]$sim[,"mean_age_female"]))
upper_female <-quantile(c(res[[1]]$sim[,"mean_age_female"],
res[[2]]$sim[,"mean_age_female"],
res[[3]]$sim[,"mean_age_female"],
res[[4]]$sim[,"mean_age_female"]), 0.975)
lower_female <-quantile(c(res[[1]]$sim[,"mean_age_female"],
res[[2]]$sim[,"mean_age_female"],
res[[3]]$sim[,"mean_age_female"],
res[[4]]$sim[,"mean_age_female"]), 0.025)
res_female <- cbind(means_female, sd_female, lower_female, upper_female)
rownames(res_female) <- NULL
res_female
#> means_female sd_female lower_female upper_female
#> [1,] 26.43952 0.8835687 26.49126 28.47637
means_male <- mean(res[[1]]$sim[,"mean_age_male"],
res[[2]]$sim[,"mean_age_male"],
res[[3]]$sim[,"mean_age_male"],
res[[4]]$sim[,"mean_age_male"])
sd_male <-sd(c(res[[1]]$sim[,"mean_age_male"],
res[[2]]$sim[,"mean_age_male"],
res[[3]]$sim[,"mean_age_male"],
res[[4]]$sim[,"mean_age_male"]))
upper_male <-quantile(c(res[[1]]$sim[,"mean_age_male"],
res[[2]]$sim[,"mean_age_male"],
res[[3]]$sim[,"mean_age_male"],
res[[4]]$sim[,"mean_age_male"]), 0.975)
lower_male <-quantile(c(res[[1]]$sim[,"mean_age_male"],
res[[2]]$sim[,"mean_age_male"],
res[[3]]$sim[,"mean_age_male"],
res[[4]]$sim[,"mean_age_male"]), 0.025)
res_male <- cbind(means_male, sd_male, lower_male, upper_male)
rownames(res_male) <- NULL
cbind(res_female, res_male) %>%
data.frame() %>%
pivot_longer(cols = everything(),
names_to = c(".value", "gender"),
names_sep = "_",
values_to = c("gender", "means", "sd", "lower", "upper"))
#> # A tibble: 2 x 5
#> gender means sd lower upper
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 female 26.4 0.884 26.5 28.5
#> 2 male 29.8 1.24 28.8 31.6
#is there a function in tidyverse that can do this automatically such as
# in purrr package with map or modify?
```