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 format

nn <- c(1, 10, 100, 1000, 5000, 10000, 20000, 30000)
pp <- c(1, 5, 10, 20, 30, 40, 50)

# create some excel files
l <- list()  # save results
tmp_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.