Running 'xlsx' processes in parallel, using the 'parallel' R package - r

Question

I have a project where I need to process some data from an Excel file with R. I must use the 'xlsx' package because of some specific functions.
First, I wrote a script, which works as expected without errors.
options(java.parameters = "-Xmx4096m") #for extra memory
library(xlsx)
wb <- loadWorkbook(file = "my_excel.xlsx")
sheet1 <- getSheets(wb)[[1]]
rows <- getRows(sheet1)
make_df <- function (x) {
cells <- getCells(rows[x])
styles <- sapply(cells, getCellStyle)
cellColor <- function(style) {
fg <- style$getFillForegroundXSSFColor()
rgb <- tryCatch(fg$getRgb(), error = function(e) NULL)
rgb <- paste(rgb, collapse = "")
return(rgb)
}
colors <- sapply(styles, cellColor)
if (!any(colors == "ff0000")) {
df[nrow(df) + 1, ] <- sapply(cells, getCellValue) #I define this 'df' somewhere in the code; this part could be improved
}
}
df <- sapply(1 : length(rows), make_df)
In short, I am looking for the rows in Excel where there are no red-colored cells, like described here. The problem is that the Excel file is very big, and it takes a lot of time to process.
What I'd like to do is to run the row checking in parallel, to be more efficient, so I added:
cl = makeCluster(detectCores() - 1)
clusterEvalQ(cl=cl, c(library(xlsx))) #sharing the package with the workers
clusterExport(cl = cl, c('rows')) #sharing the 'row' variable with the workers
df <- parSapply(cl, 1 : length(rows), make_df)
And after running this, I get the following error:
Error in checkForRemoteErrors(val) :
7 nodes produced errors; first error: RcallMethod: attempt to call a method of a NULL object.
I tried the parallelization with another example, without using 'xlsx' functions, and it worked.
After some digging, I found this post which offered somewhat of an answer (more like a workaround), but I can't seem to be able to implement it.
Is there a clean way to do what I'm trying to do here?
If not, then what would be the best solution in this case?