making code (loop) more efficient

Hello,

I made a terribly inefficient code which runs forever but it does run.

library(dplyr)
library(splitstackshape)

datalist = list()
files <- list.files("/WEIGHTS1/Retina", pattern=".RDat", ignore.case=T)

for(i in files)
{
a<-get(load(i))
names <- rownames(a)
data <- as.data.frame(cbind(names,a))
rownames(data) <- NULL
dd=na.omit(concat.split.multiple(data = data, split.cols = c("names"),
seps = ":"))
dd=select(dd,names_1,blup,names_3,names_4)
colnames(dd)=c("rsid","weight","ref_allele","eff_allele")
dd$WGT<-i
datalist[[i]] <- dd # add it to your list
}

big_data = do.call(rbind, datalist)

There is 17345 RDat files this loop has to go through. And each file
has approximately 10,000 lines. All RDat files can be downloaded from
here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115828 and
they are compressed in this file: GSE115828_retina_TWAS_wgts.tar.gz .
And subset of 3 of those .RDat files is here:
https://github.com/montenegrina/sample

For one of those files, say i="retina.ENSG00000135776.wgt.RDat"
dd looks like this:

head(dd)

          rsid        weight ref_allele eff_allele
1:  rs72763981  9.376766e-09          C          G
2: rs144383755 -2.093346e-09          A          G
3:   rs1925717  1.511376e-08          T          C
4:  rs61827307 -1.625302e-08          C          A
5:  rs61827308 -1.625302e-08          G          C
6: rs199623136 -9.128354e-10         GC          G
                           WGT
1: retina.ENSG00000135776.wgt.RDat
2: retina.ENSG00000135776.wgt.RDat
3: retina.ENSG00000135776.wgt.RDat
4: retina.ENSG00000135776.wgt.RDat
5: retina.ENSG00000135776.wgt.RDat
6: retina.ENSG00000135776.wgt.RDat

so on attempt to parallelize this I did this:

library(parallel)
library(data.table)
library(foreach)
library(doSNOW)

n <-  parallel::detectCores()
cl <- parallel::makeCluster(n, type = "SOCK")
doSNOW::registerDoSNOW(cl)
files <- list.files("/WEIGHTS1/Retina", pattern=".RDat", ignore.case=T)

lst_out <- foreach::foreach(i = seq_along(files),
                  .packages = c("data.table") ) %dopar% {

   a <- get(load(files[i]))
   names <- rownames(a)
   data <- data.table(names, a["blup"])
   nm1 <- c("rsid", "ref_allele", "eff_allele")
   data[,  (nm1) := tstrsplit(names, ":")[-2]]
   return(data[, .(rsid, weight = blup, ref_allele, eff_allele)][,
               WGT := files[i]][])
 }
parallel::stopCluster(cl)

big_data <- rbindlist(lst_out)

I am getting this Error:

Error in { : task 7 failed - "object 'blup' not found"

parallel::stopCluster(cl)

Can you please advise,
Ana