Is there a way to store all factors selected from running the same Stepwise regression on each of N datasets using lapply(csvs, FUN(i) { step() })? - r

Question

My file folder with the N datasets in the form of csv files is called sample_obs. The goal is to end up with two lists, one I have already figured out how to obtain, namely, a list of the names of each individual csv file that matches the format of their actual names in the folder all of them are in, not their file paths.
So, this is all the code I have written and the other list I need to create is a list of the factors/Independent Variables chosen by my Backward Elimination Stepwise Regression function only, no R-squared, Cp, AIC, BIC, or any other standard regression diagnostic tools; I don't want or need coefficient estimates either, just the regressors "chosen" for each dataset out of the 30 candidate regressors.
So far, in terms of my code that actually runs (except the last few lines):
# these 2 lines together create a simple character list of
# all the file names in the file folder of datasets you created
directory_path <- "~/DAEN_698/sample_obs"
file_list <- list.files(path = directory_path, full.names = TRUE, recursive = TRUE)
head(file_list, n = 2)
> head(file_list, n = 2)
[1] "C:/Users/Spencer/Documents/DAEN_698/sample_obs2/0-5-1-1.csv"
[2] "C:/Users/Spencer/Documents/DAEN_698/sample_obs2/0-5-1-2.csv"
# Create another list with the just the "n-n-n-n" part of the names of of each dataset
DS_name_list = stri_sub(file_list, 49, 55)
head(DS_name_list, n = 3)
> head(DS_name_list, n = 3)
[1] "0-5-1-1" "0-5-1-2" "0-5-1-3"
# This command reads all the data in each of the N csv files via their names
# stored in the 'file_list' list of characters.
csvs <- lapply(file_list, read.csv)
### Step 3: Run a Backward Elimination Stepwise Regression function on each of the N csvs.
# Assign the full models (meaning one with all 30 candidate regressors included in step 1)
# as the initial model that BE starts out with.
# This is crucial because if the initial model had less than the number of candidate factors # in the datasets, e.g. 25 (so, X1:X26), then it could miss 1 or more of the factors
# X26:X30 which ought to be 'chosen' in dataset j by Stepwise j.
full_model <- lapply(csvs, function(i) {
lm(formula = Y ~ ., data = i) })
Finally, this is the part where I get really tripped up. I have tried at least 6 different sets of arguments, different syntax, using different objects, etc. when running my BE Stepwise Regression on my N datasets, but I'll just include 2 of them below which take entirely different approaches but are both wrong:
# attempt 1
set.seed(50) # for reproducibility
BE_fits1 <- map(.x = full_model[-1], .f = function(i) { step(object = all_IVs_models2, direction = 'backward', scope = formula(full_model), trace = 0) })
# attempt 3
set.seed(50) # for reproducibility
BE_fits3 <- lapply(full_model, function(i) {
step(object = i[["coefficients"]], direction = 'backward',
scope = formula(full_model), trace = 0)
When I hit Ctrl+Enter on attempt 1, I get the following error message:
Error in x$terms %||% attr(x, "terms") %||% stop("no terms component nor attribute") :
no terms component nor attribute
And when I try to run my code for attempt #3, I get the following different error message:
Error in x$terms : $ operator is invalid for atomic vectors
I don't recognize either of these error messages.
p.s. If anyone looking over this question would like, I can re ask this question but including MUCH less minute details if you want me to.