readxl::read_xls returns "libxls error: Unable to open file"

Question

I have multiple .xls (~100MB) files from which I would like to load multiple sheets (from each) into R as a dataframe. I have tried various functions, such as xlsx::xlsx2 and XLConnect::readWorksheetFromFile, both of which always run for a very long time (>15 mins) and never finish and I have to force-quit RStudio to keep working.

I also tried gdata::read.xls, which does finish, but it takes more than 3 minutes per one sheet and it cannot extract multiple sheets at once (which would be very helpful to speed up my pipeline) like XLConnect::loadWorkbook can.

The time it takes these functions to execute (and I am not even sure the first two would ever finish if I let them go longer) is way too long for my pipeline, where I need to work with many files at once. Is there a way to get these to go/finish faster?

In several places, I have seen a recommendation to use the function readxl::read_xls, which seems to be widely recommended for this task and should be faster per sheet. This one, however, gives me an error:

> # Minimal reproducible example:
> setwd("/Users/USER/Desktop")
> library(readxl)
> data <- read_xls(path="test_file.xls")
Error: 
  filepath: /Users/USER/Desktop/test_file.xls
  libxls error: Unable to open file

I also did some elementary testing to make sure the file exists and is in the correct format:

> # Testing existence & format of the file
> file.exists("test_file.xls")
[1] TRUE
> format_from_ext("test_file.xls")
[1] "xls"
> format_from_signature("test_file.xls")
[1] "xls"

The test_file.xls used above is available here. Any advice would be appreciated in terms of making the first functions run faster or the read_xls run at all - thank you!

UPDATE:

It seems that some users are able to open the file above using the readxl::read_xls function, while others are not, both on Mac and Windows, using the most up to date versions of R, Rstudio, and readxl. The issue has been posted on the readxl GitHub and has not been resolved yet.

Answer 1

I downloaded your dataset and read each excel sheet in this way (for example, for sheets "Overall" and "Area"):

install.packages("readxl")
library(readxl)
library(data.table)

dt_overall <- as.data.table(read_excel("test_file.xls", sheet = "Overall"))
area_sheet <- as.data.table(read_excel("test_file.xls", sheet = "Area"))

Finally, I get dt like this (for example, only part of the dataset for the "Area" sheet):

Just as well, you can use the read_xls function instead read_excel.

I checked, it also works correctly and even a little faster, since read_excel is a wrapper over read_xls and read_xlsx functions from readxl package.

Also, you can use excel_sheets function from readxl package to read all sheets of your Excel file.

UPDATE

Benchmarking is done with microbenchmark package for the following packages/functions: gdata::read.xls, XLConnect::readWorksheetFromFile and readxl::read_excel.

But XLConnect it's a Java-based solution, so it requires a lot of RAM.