https://www.r-bloggers.com/2021/06/reading-data-from-excel-files-xlsxlsxcsv-into-r-quick-guide/
Excel can hold a great quantity of data. However, I find that it is slow and
often crashes when I try to use Excel at large scale. It also grinds my entire
system to a halt. At the kb and mb scales I typically have few problems. At gb
scales Excel will hold the data, but doing anything with it is problematic (for
me). I have used readxl and associated read_excel() in R and not noticed issues
at my small scales. I could read a file multiple times in different data frames
and then compare them but that too is slow and can exceed system resources.
I only deal with a few files, so I would use something like 7-zip to decompress
the files before having R read them. I would bet that there are existing
programs that would unzip large batches of files, but I have never had to do
this where the target files are scattered amongst other files that are not
needed. If I can use "select all" then that is simple enough.
Tim
-----Original Message-----
From: R-help <r-help-bounces at r-project.org> On Behalf Of DynV
Montrealer
Sent: Thursday, May 16, 2024 9:51 AM
To: r-help at r-project.org
Subject: [R] Least error-prone reading of Excel files?
[External Email]
I'm tasked to read a table from an excel file and it doesn't mention
which method to use. I went back some lessons ago and the 5 years old lesson
mentioned to pick a package using the highest score the way of the attached
(screenshot). Since there's no requirement of a method to read Excel files,
I'd rather use the least error-prone one; what would that be? eg will try
multiple decompression algorithm if there's a decompression error.
Thank you kindly