rio: A Swiss-Army Knife for Data I/O

The aim of rio is to make data file I/O in R as easy as possible by implementing three simple functions in Swiss-army knife style:

Supported file formats

rio supports a variety of different file formats for import and export.

Format Import Export
Tab-separated data (.tsv) Yes Yes
Comma-separated data (.csv) Yes Yes
CSVY (CSV + YAML metadata header) (.csvy) Yes Yes
Feather R/Python interchange format (.feather) Yes Yes
Pipe-separated data (.psv) Yes Yes
Fixed-width format data (.fwf) Yes Yes
Serialized R objects (.rds) Yes Yes
Saved R objects (.RData) Yes Yes
JSON (.json) Yes Yes
YAML (.yml) Yes Yes
Stata (.dta) Yes Yes
SPSS (.sav) Yes Yes
SPSS Portable (.por) Yes
"XBASE" database files (.dbf) Yes Yes
Excel (.xls) Yes
Excel (.xlsx) Yes Yes
Weka Attribute-Relation File Format (.arff) Yes Yes
R syntax (.R) Yes Yes
Shallow XML documents (.xml) Yes Yes
HTML Tables (.html) Yes Yes
SAS (.sas7bdat) Yes Yes
SAS XPORT (.xpt) Yes
Minitab (.mtp) Yes
Epiinfo (.rec) Yes
Systat (.syd) Yes
Data Interchange Format (.dif) Yes
OpenDocument Spreadsheet (.ods) Yes
Fortran data (no recognized extension) Yes
Google Sheets Yes
Clipboard (default is tsv) Yes (Mac and Windows) Yes (Mac and Windows)

Additionally, any format that is not supported by rio but that has a known R implementation will produce an informative error message pointing to a package and import or export function. Unrecognized formats will yield a simple "Unrecognized file format" error.


Because rio is meant to streamline data I/O, the package is extremely easy to use. Here are some examples of reading, writing, and converting data files.


Exporting data is handled with one function, export():


export(mtcars, "mtcars.csv") # comma-separated values
export(mtcars, "mtcars.rds") # R serialized
export(mtcars, "mtcars.sav") # SPSS


Importing data is handled with one function, import():

x <- import("mtcars.csv")
y <- import("mtcars.rds")
z <- import("mtcars.sav")

# confirm data match
all.equal(x, y, check.attributes = FALSE)
## [1] TRUE
all.equal(x, z, check.attributes = FALSE)
## [1] TRUE

Note: Because of inconsistencies across underlying packages, the data.frame returned by import might vary slightly (in variable classes and attributes) depending on file type.


The convert() function links import() and export() by constructing a dataframe from the imported file and immediately writing it back to disk. convert() invisibly returns the file name of the exported file, so that it can be used to programmatically access the new file.

convert("mtcars.sav", "mtcars.dta")

It is also possible to use rio on the command-line by calling Rscript with the -e (expression) argument. For example, to convert a file from Stata (.dta) to comma-separated values (.csv), simply do the following:

Rscript -e "rio::convert('iris.dta', 'iris.csv')"

Package Philosophy

The core advantage of rio is that it makes assumptions that the user is probably willing to make. Eight of these are important:

  1. rio uses the file extension of a file name to determine what kind of file it is. This is the same logic used by Windows OS, for example, in determining what application is associated with a given file type. By taking away the need to manually match a file type (which a beginner may not recognize) to a particular import or export function, rio allows almost all common data formats to be read with the same function. Other packages do this as well, but rio aims to be more complete and more consistent than each:
  1. rio uses data.table::fread() for text-delimited files to automatically determine the file format regardless of the extension. So, a CSV that is actually tab-separated will still be correctly imported. It's also crazy fast.

  2. rio, wherever possible, does not import character strings as factors.

  3. rio stores metadata from rich file formats (SPSS, Stata, etc.) in variable-level attributes in a consistent form regardless of file type or underlying import function. The gather_attrs() function makes it easy to move variable-level attributes to the data frame level.

  4. rio supports web-based imports natively, including from SSL (HTTPS) URLs, from shortened URLs, from URLs that lack proper extensions, and from (public) Google Documents Spreadsheets.

  5. rio imports from from single-file .zip and .tar archives automatically, without the need to explicitly decompress them. Export to compressed directories is also supported.

  6. rio imports and exports files based on an internal S3 class infrastructure. This means that other packages can contain extensions to rio by registering S3 methods. These methods should take the form .import.rio_X() and .export.rio_X(), where X is the file extension of a file type. An example is provided in the rio.db package.

  7. rio wraps a variety of faster, more stream-lined I/O packages than those provided by base R or the foreign package. It uses haven for reading and writing SAS, Stata, and SPSS files, the fread function from data.table for delimited formats, smarter and faster fixed-width file import and export routines, and readxl for reading from Excel workbooks.

Package Installation

The package is available on CRAN and can be installed directly in R using:


The latest development version on GitHub can be installed using devtools:


CRAN Version Downloads Travis-CI Build Status Appveyor Build status