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 (12/14/2019):

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.

UPDATE (4/14/2023):

The above GitHub issue became a combination of various problems and yielded one possible solution, which was opening and closing the file in MS Excel first, which subsequently made it readable by readxl::read_xls. Understandably, though, this is not the best solution. The above issue was closed without a better resolution and instead, a related issue was opened in the libxls GitHub, so far without a resolution.

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.

Answer 2

I found that I was unable to open the file with read_xl immediately after downloading it, but if I opened the file in Excel, saved it, and closed it again, then read_xl was able to open it without issue.

My suggested workaround for handling hundreds of files is to build a little C# command line utility that opens, saves, and closes an Excel file. Source code is below, the utility can be compiled with visual studio community edition.

using System.IO;
using Excel = Microsoft.Office.Interop.Excel;

namespace resaver
{
  class Program
  {
    static void Main(string[] args)
    {
      string srcFile = Path.GetFullPath(args[0]);
      Excel.Application excelApplication = new Excel.Application();
      excelApplication.Application.DisplayAlerts = false;
      Excel.Workbook srcworkBook = excelApplication.Workbooks.Open(srcFile);
      srcworkBook.Save();
      srcworkBook.Close();
      excelApplication.Quit();
    }
  }
}

Once compiled, the utility can be called from R using e.g. system2().

Answer 3

On my system, i had to use path.expand.

R> file = "~/blah.xls"
R> read_xls(file)

Error: 
  filepath: ~/Dropbox/signal/aud/rba/balsheet/data/a03.xls
  libxls error: Unable to open file

R> read_xls(path.expand(file)) # fixed

Answer 4

I will propose a different workflow. If you happen to have LibreOffice installed, then you can convert your excel files to csv programatically. I have Linux, so I do it in bash, but I'm sure it can be possible in macOS.

So open a terminal and navigate to the folder with your excel files and run in terminal:

for i in *.xls
    do soffice --headless --convert-to csv "$i" 
done

Now in R you can use data.table::fread to read your files with a loop:

Scenario 1: the structure of files is different

If the structure of files is different, then you wouldn't want to rbind them together. You could run in R:

files <- dir("path/to/files", pattern = ".csv")
all_files <- list()
for (i in 1:length(files)){
  fileName <- gsub("(^.*/)(.*)(.csv$)", "\\2", files[i])
  all_files[[fileName]] <- fread(files[i])
}

If you want to extract your named elements within the list into the global environment, so that they can be converted into objects, you can use list2env:

list2env(all_files, envir = .GlobalEnv)

Please be aware of two things: First, in the gsub call, the direction of the slash. And second, list2env may overwrite objects in your Global Environment if they have the same name as the named elements within the list.

Scenario 2: the structure of files is the same

In that case it's likely you want to rbind them all together. You could run in R:

files <- dir("path/to/files", pattern = ".csv")
joined <- list()
for (i in 1:length(files)){
  joined <- rbindlist(joined, fread(files[i]), fill = TRUE)
}

Answer 5

I was seeing a similar error and wanted to share a short-term solution.

library(readxl)
download.file("https://mjwebster.github.io/DataJ/spreadsheets/MLBpayrolls.xls", "MLBPayrolls.xls")
MLBpayrolls <- read_excel("MLBpayrolls.xls", sheet = "MLB Payrolls", na = "n/a")

Yields (on some systems in my classroom but not others):

Error: filepath: MLBPayrolls.xls libxls error: Unable to open file

The temporary solution was to paste the URL of the xls file into Firefox and download it via the browser. Once this was done we could run the read_excel line without error.

This was happening today on Windows 10, with R 3.6.2 and R Studio 1.2.5033.

Answer 6

Resaving your file and you can solve your problem easily.

I also find this problem before but I get the answer from your discussion.

I used the read_excel() to open those files.

Answer 7

If you have downloaded the .xls data from the internet, even if you are opening it in Ms.Excel, it will open a prompt first asking to confirm if you trust the source, see below screenshot, I am guessing this is the reason R (read_xls) also can't open it, as it's considered unsafe. Save it as .xlsx file and then use read_xlsx() or read_excel().

Answer 8

Even thought this is not a code-based solution, I just changed the type file. For instance, instead of xls I saved as csv or xlsx. Then I opened it as regular one.
I worked it for me, because when I opened my xlsfile, I popped up the message: "The file format and extension of 'file.xls'' don't match. The file could be corrupted or unsafe..."