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:

The core advantage of rio is that it makes assumptions that the user is probably willing to make. Five 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. The reader package does something similar for reading certain text formats and R binary files and io offers a set of methods for reading and writing to file formats defined by that package, but rio supports a much broader set of commonly used file types for import and export.
  2. For text-delimited file formats, the package uses data.table::fread to automatically determine the file format regardless of the extension. So, a CSV that is actually tab-separated will still be correctly read in.
  3. When importing tabular data (CSV, TSV, etc.), rio does not convert strings to factors.
  4. The data import functions in base R only support import of local files or web-based data from websites serving HTTP, not SSL (HTTPS). For example, data stored on GitHub as publicly visible files cannot be read directly into R without either manually downloading them or reading them in via RCurl or httr. import supports HTTPS automatically.
  5. import reads from single-file .zip and .tar archives automatically, without the need to explicitly decompress them first.

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

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
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
Stata (.dta) Yes Yes
SPSS and SPSS portable Yes (.sav and .por) Yes (.sav only)
"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
SAS and SAS XPORT Yes (.sas7bdat and .xpt)
Minitab (.mtp) Yes
Epiinfo (.rec) Yes
Systat (.syd) Yes
Data Interchange Format (.dif) Yes
OpenDocument Spreadsheet (.ods) Yes
Fortran data (no recognized extension) 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.

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:


Build Status Downloads


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')"