Fast way to read xlsx files into R

Here is a small benchmark test. Results: readxl::read_xlsx on average about twice as fast as openxlsx::read.xlsx across different number of rows (n) and columns (p) using standard settings.

options(scipen=999)  # no scientific number formatnn <- c(1, 10, 100, 1000, 5000, 10000, 20000, 30000)pp <- c(1, 5, 10, 20, 30, 40, 50)# create some excel filesl <- list()  # save resultstmp_dir <- tempdir()for (n in nn) {  for (p in pp) {    name <-      cat("\n\tn:", n, "p:", p)    flush.console()    m <- matrix(rnorm(n*p), n, p)    file <- paste0(tmp_dir, "/n", n, "_p", p, ".xlsx")    # write    write.xlsx(m, file)    # read    elapsed <- system.time( x <- openxlsx::read.xlsx(file) )["elapsed"]    df <- data.frame(fun = "openxlsx::read.xlsx", n = n, p = p,                      elapsed = elapsed, stringsAsFactors = F, row.names = NULL)    l <- append(l, list(df))    elapsed <- system.time( x <- readxl::read_xlsx(file) )["elapsed"]    df <- data.frame(fun = "readxl::read_xlsx", n = n, p = p,                      elapsed = elapsed, stringsAsFactors = F, row.names = NULL)    l <- append(l, list(df))  }}# results d <- do.call(rbind, l)library(ggplot2)ggplot(d, aes(n, elapsed, color= fun)) +   geom_line() + geom_point() +    facet_wrap( ~ paste("columns:", p)) +  xlab("Number of rows") +  ylab("Seconds")

To write an excel file, readxl has a counterpart called writexl. As far as what is the best package to read an excel file, I think the benchmark provided above is pretty good.

The only reason I would use xlsx to write a package would be if I were to write many excel sheets in one .xlsx file.