This might not be exactly what you are looking for, but based on what you are trying to achieve, it might be a good approach to look into:
# Load Libraries ----------------------------------------------------------
library("tidyverse")
library("broom")
# Create Example Data -----------------------------------------------------
map(1:6, \(i) write_csv(x = tibble(x = rnorm(10), y = rnorm(10)),
file = str_c("~/my_csv_file_", i, ".csv")) )
# Load Data ---------------------------------------------------------------
my_csv_files <- list.files(path = "~", full.names = TRUE, pattern = "csv$")
my_data <- my_csv_files %>%
map(read_csv)
# Run tests ---------------------------------------------------------------
my_test_results <- my_data %>%
bind_rows(.id = "file_number") %>%
group_by(file_number) %>%
nest %>%
ungroup %>%
mutate(test = map(data, ~t.test(pluck(.x, "x"),
pluck(.x, "y"), data = .x)),
tidy_test = map(test, tidy)) %>%
unnest(tidy_test) %>%
select(-data, -test) %>%
mutate(q.value = p.adjust(p.value))
...and hot tip: Want to understand what is going on? Ask chatGPT Please explain the following code in simple terms and then paste the above 