Changing R object names and pathway in a loop over a function - r

Question

library('dplyr')
num_ens <- 10
I have a piece of code that takes num_ens files of data from a directory, reads them in, takes the average across them, and saves them as 1 object
A_tree <- lapply(1:num_ens, function(i) {
# importing data on each index i
r <- read.csv(
paste0("/Users/sethparker/vox_LA_max/top_down_individ_sd1/ens_",i,"_0_tree_from_data.txt"),
header = FALSE
)
# creating add columns
colnames(r) <- c("GPP","NPP","LA")
r$month <- seq.int(nrow(r))
r$run <- i
return(r)
})
A_tree <- bind_rows(A_tree)
A_tree <- A_tree %>% group_by(month) %>% summarize(across(c(GPP,NPP,LA), mean))
I would like to auto automate this same process to go across 7 directories:
/top_down_individ_sd1/ through /top_down_individ_sd7/
and to generate a series of objects:
A_tree through G_tree
I have unsuccessfully tried to achieve this with a few variations of the following for loop, which have yielded errors
letters <- LETTERS[seq(from = 1, to = 7)]
sd <- c("sd1","sd2","sd3","sd4","sd5","sd6","sd7")
for (j in 1:7) {
paste0(letters[j],"_tree") <- lapply(1:num_ens, function(i) {
# importing data on each index i
r <- read.csv(paste0(paste0("/Users/sethparker/vox_LA_max/top_down_individ_",sd[j]),"/ens_",i,"_0_tree_from_data.txt"),
header = FALSE)
# creating add columns
colnames(r) <- c("GPP","NPP","LA")
r$month <- seq.int(nrow(r))
r$run <- i
return(r)
})
paste0(letters[j],"_tree") <- bind_rows(paste0(letters[j],"_tree"))
paste0(letters[j],"_tree") <- paste0(letters[j],"_tree") %>% group_by(month) %>% summarize(across(c(GPP,NPP,LA), mean))
}
How can I achieve this goal without errors

Answer 1

tree <- list()
for (j in 1:7) {
tree[[j]] <- lapply(1:num_ens, function(i) {
# importing data on each index i
r <- read.csv(paste0(paste0("/Users/sethparker/vox_LA_max/top_down_individ_",sd[j]),"/ens_",i,"_0_tree_from_data.txt"),
header = FALSE)
# creating add columns
colnames(r) <- c("GPP","NPP","LA")
r$month <- seq.int(nrow(r))
r$run <- i
return(r)
})
tree[[j]] <- bind_rows(tree[[j]])
tree[[j]] <- tree[[j]] %>% group_by(month) %>% summarize(across(c(GPP,NPP,LA), mean))
}