Import, Export, and Convert Data Files

The idea behind rio is to simplify the process of importing data into R and exporting data from R. This process is, probably unnecessarily, extremely complex for beginning R users. Indeed, R supplies an entire manual describing the process of data import/export. And, despite all of that text, most of the packages described are (to varying degrees) out-of-date. Faster, simpler, packages with fewer dependencies have been created for many of the file types described in that document. rio aims to unify data I/O (importing and exporting) into two simple functions: import and export so that beginners (and experienced R users) never have to think twice (or even once) about the best way to read and write R data.

The core advantage of rio is that it makes assumptions that the user is probably willing to make. Specifically, 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.

By making import and export easy, it's an obvious next step to also use R as a simple data conversion utility. Transferring data files between various proprietary formats is always a pain and often expensive. The convert function therefore combines import and export to easily convert between file formats (thus providing a FOSS replacement for programs like Stat/Transfer or Sledgehammer).

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
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.


rio allows you to import files in almost any format using one, typically single-argument, function. import infers the file format from the file's extension and calls the appropriate data import function for you, returning a simple data.frame. This works for any for the formats listed above.


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

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

If for some reason a file does not have an extension, or has a file extension that does not match its actual type, you can manually specify a file format to override the format inference step. For example, we can read in a CSV file that does not have a file extension by specifying csv:

head(import("mtcars_noext", format = "csv"))
##    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## 6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1


The export capabilities of rio are somewhat more limited than the import capabilities, given the availability of different functions in various R packages and because import functions are often written to make use of data from other applications and it never seems to be a development priority to have functions to export to the formats used by other applications. That said, rio currently supports the following formats:


export(mtcars, "mtcars.csv")
export(mtcars, "mtcars.rds")
export(mtcars, "mtcars.dta")

It is also easy to use export as part of an R pipeline (from magrittr or dplyr). For example, the following code uses export to save the results of a simple data transformation:

mtcars %>% subset(hp > 100) %>%  aggregate(. ~ cyl + am, data = ., FUN = mean) %>% export(file = "mtcars2.dta")


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.

Because convert is just a thin wrapper for import and export, it is very easy to use. For example, we can convert

# create file to convert
export(mtcars, "mtcars.dta")

# convert Stata to SPSS
convert("mtcars.dta", "mtcars.sav")

convert also accepts lists of arguments for controlling import (in_opts) and export (out_opts). This can be useful for passing additional arguments to import or export methods. This could be useful, for example, for reading in a fixed-width format file and converting it to a comma-separated values file:

# create an ambiguous file
fwf <- tempfile(fileext = ".fwf")
cat(file = fwf, "123456", "987654", sep = "\n")

# see two ways to read in the file
identical(import(fwf, widths = c(1,2,3)), import(fwf, widths = c(1,-2,3)))
## [1] FALSE
# convert to CSV
convert(fwf, "fwf.csv", in_opts = list(widths = c(1,2,3)))
import("fwf.csv") # check conversion
##   V1 V2  V3
## 1  1 23 456
## 2  9 87 654

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('mtcars.dta', 'mtcars.csv')"