A Swiss-Army Knife for Data I/O

The aim of rio is to make data file input and output as easy as possible. export() and import() serve as a Swiss-army knife for painless data I/O for data from almost any file format by inferring the data structure from the file extension, natively reading web-based data sources, setting reasonable defaults for import and export, and relying on efficient data import and export packages. An additional convenience function, convert(), provides a simple method for converting between file types.

Note that some of rio's functionality is provided by ‘Suggests’ dependendencies, meaning they are not installed by default. Use install_formats() to make sure these packages are available for use.

References

datamods provides Shiny modules for importing data via rio.

GREA provides an RStudio add-in to import data using rio.

See also

import(), import_list(), export(), export_list(), convert(), install_formats()

Author

Maintainer: Chung-hong Chan chainsawtiney@gmail.com (ORCID)

Authors:

• Jason Becker jason@jbecker.co

• David Schoch david@schochastics.net (ORCID)

• Thomas J. Leeper thosjleeper@gmail.com (ORCID)

Other contributors:

• Geoffrey CH Chan gefchchan@gmail.com [contributor]

• Christopher Gandrud [contributor]

• Andrew MacDonald [contributor]

• Ista Zahn [contributor]

• Stanislaus Stadlmann [contributor]

• Ruaridh Williamson ruaridh.williamson@gmail.com [contributor]

• Patrick Kennedy [contributor]

• Ryan Price ryapric@gmail.com [contributor]

• Trevor L Davis trevor.l.davis@gmail.com [contributor]

• Nathan Day nathancday@gmail.com [contributor]

• Bill Denney wdenney@humanpredictions.com (ORCID) [contributor]

• Alex Bokov alex.bokov@gmail.com (ORCID) [contributor]

• Hugo Gruson (ORCID) [contributor]

Examples

# export
library("datasets")
export(mtcars, csv_file <- tempfile(fileext = ".csv")) # comma-separated values
export(mtcars, rds_file <- tempfile(fileext = ".rds")) # R serialized
export(mtcars, sav_file <- tempfile(fileext = ".sav")) # SPSS

# import
x <- import(csv_file)
y <- import(rds_file)
#> Warning: Missing `trust` will be set to FALSE by default for RDS in 2.0.0.
z <- import(sav_file)

# convert sav (SPSS) to dta (Stata)
convert(sav_file, dta_file <- tempfile(fileext = ".dta"))

# cleanup
unlink(c(csv_file, rds_file, sav_file, dta_file))