Question 1

How to install all optional formats for rio in R?

Accepted Answer

After installing rio, use the install_formats() function to install all suggested packages for extended format support. This adds dependencies but enables full functionality for over 50 file types.

Question 2

rio vs data.table for CSV import in R – which is faster?

Accepted Answer

data.table's fread() is generally faster for large CSV files due to optimization, while rio's import() uses data.table by default but adds a layer of automatic detection. Use rio for simplicity across formats, data.table for performance-critical CSV tasks.

Question 3

How to convert an Excel file to JSON using rio?

Accepted Answer

Use the convert() function: convert('file.xlsx', 'file.json'). This imports the Excel data and exports it as JSON in one step, though note that JSON support requires the optional jsonlite package.

Question 4

Does rio support Parquet files in R?

Accepted Answer

Yes, but it's a suggested format requiring the nanoparquet package. Once installed, you can use import() and export() for .parquet files, as listed in the supported formats table.

Question 5

How to handle multiple sheets in an Excel file with rio?

Accepted Answer

Use import_list() to import all sheets as a list of data frames, or export() with a list to write multiple data frames to separate sheets in a new Excel workbook.

Question 6

What happens if rio doesn't recognize a file format?

Accepted Answer

For unrecognized formats, rio yields a simple 'Unrecognized file format' error. For formats with known R implementations but not in rio's list, it provides an informative message pointing to the relevant package.

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