If the structure of files is different, then you wouldn't want to rbind them together. Now in R you can use data.table::fread to read your files with a loop: Scenario 1: the structure of files is different So open a terminal and navigate to the folder with your excel files and run in terminal: for i in *.xlsĭo soffice -headless -convert-to csv "$i" I have Linux, so I do it in bash, but I'm sure it can be possible in macOS. If you happen to have LibreOffice installed, then you can convert your excel files to csv programatically. The issue has been posted on the readxl GitHub and has not been resolved yet.
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 test_file.xls used above is available here.Īny advice would be appreciated in terms of making the first functions run faster or the read_xls run at all - thank you! UPDATE: > data # Testing existence & format of the file This one, however, gives me an error: > # Minimal reproducible example: 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. Is there a way to get these to go/finish faster? 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. 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. 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. xls (~100MB) files from which I would like to load multiple sheets (from each) into R as a dataframe. Post memes/jokes in /r/chemistrymemes and /r/chemistryjokes.I have multiple. Any such posts will be deleted.Īsk education and jobs questions in the current weekly topic. If you're looking for a more concentrated, advanced discussion of chemistry topics among professionals and grad students, check out /r/Chempros.īefore asking "What chemical is this?" see this chart.
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