Import, Export, and Convert Data Files

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 And, despite all of that text, most of the packages described are (to varying degrees) out-of-date.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. To keep the package slim, all non-essential formats are supported via “Suggests” packages, which are not installed (or loaded) by default. To ensure rio is fully functional, install these packages the first time you use rio via:

install_formats()

The full list of supported formats is below:

Name	Extensions / “format”	Import Package	Export Package	Type	Note
Archive files (handled by tar)	tar / tar.gz / tgz / tar.bz2 / tbz2	utils	utils	Default
Bzip2	bz2 / bzip2	base	base	Default
Gzip	gz / gzip	base	base	Default
Zip files	zip	utils	utils	Default
Ambiguous file format	dat	data.table		Default	Attempt as delimited text data
CSVY (CSV + YAML metadata header)	csvy	data.table	data.table	Default
Comma-separated data	csv	data.table	data.table	Default
Comma-separated data (European)	csv2	data.table	data.table	Default
Data Interchange Format	dif	utils		Default
Epiinfo	epiinfo / rec	foreign		Default
Excel	excel / xlsx	readxl	writexl	Default
Excel (Legacy)	xls	readxl		Default
Fixed-width format data	fwf	readr	utils	Default
Fortran data	fortran	utils		Default	No recognized extension
Google Sheets	googlesheets	data.table		Default	As comma-separated data
Minitab	minitab / mtp	foreign		Default
Pipe-separated data	psv	data.table	data.table	Default
R syntax	r	base	base	Default
SAS	sas / sas7bdat	haven	haven	Default	Export is deprecated
SAS XPORT	xport / xpt	haven	haven	Default
SPSS	sav / spss	haven	haven	Default
SPSS (compressed)	zsav	haven	haven	Default
SPSS Portable	por	haven		Default
Saved R objects	rda / rdata	base	base	Default
Serialized R objects	rds	base	base	Default
Stata	dta / stata	haven	haven	Default
Systat	syd / systat	foreign		Default
Tab-separated data	/ tsv / txt	data.table	data.table	Default
Text Representations of R Objects	dump	base	base	Default
Weka Attribute-Relation File Format	arff / weka	foreign	foreign	Default
XBASE database files	dbf	foreign	foreign	Default
Apache Arrow (Parquet)	parquet	nanoparquet	nanoparquet	Suggest
Clipboard	clipboard	clipr	clipr	Suggest	default is tsv
EViews	eviews / wf1	hexView		Suggest
Fast Storage	fst	fst	fst	Suggest
Feather R/Python interchange format	feather	arrow	arrow	Suggest
Graphpad Prism	pzfx	pzfx	pzfx	Suggest
HTML Tables	htm / html	xml2	xml2	Suggest
JSON	json	jsonlite	jsonlite	Suggest
Matlab	mat / matlab	rmatio	rmatio	Suggest
OpenDocument Spreadsheet	ods	readODS	readODS	Suggest
OpenDocument Spreadsheet (Flat)	fods	readODS	readODS	Suggest
Serialized R objects (Quick)	qs	qs	qs	Suggest
Shallow XML documents	xml	xml2	xml2	Suggest
YAML	yaml / yml	yaml	yaml	Suggest

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.

Data Import

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.

library("rio")

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

# confirm identical
all.equal(x, y, 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

Importing Data Lists

Sometimes you may have multiple data files that you want to import. import() only ever returns a single data frame, but import_list() can be used to import a vector of file names into R. This works even if the files are different formats:

str(import_list(dir()), 1)

Similarly, some single-file formats (e.g. Excel Workbooks, Zip directories, HTML files, etc.) can contain multiple data sets. Because import() is type safe, always returning a data frame, importing from these formats requires specifying a which argument to import() to dictate which data set (worksheet, file, table, etc.) to import (the default being which = 1). But import_list() can be used to import all (or only a specified subset, again via which) of data objects from these types of files.

Data Export

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:

library("rio")

export(mtcars, "mtcars.csv")
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:

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

Some file formats (e.g., Excel workbooks, Rdata files) can support multiple data objects in a single file. export() natively supports output of multiple objects to these types of files:

# export to sheets of an Excel workbook
export(list(mtcars = mtcars, iris = iris), "multi.xlsx")

It is also possible to use the new (as of v0.6.0) function export_list() to write a list of data frames to multiple files using either a vector of file names or a file pattern:

export_list(list(mtcars = mtcars, iris = iris), "%s.tsv")

File Conversion

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)))

## Warning: The `widths` argument of `import()` is deprecated as of rio 1.0.2.
## ℹ `widths` is kept for backward compatibility. Please use `col_positions` or
##   unset `widths` to allow automatic guessing, see `?readr::read_fwf`. The
##   parameter `widths` will be dropped in v2.0.0.
## ℹ The deprecated feature was likely used in the rio package.
##   Please report the issue at <https://github.com/gesistsa/rio/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Rows: 2 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## 
## dbl (3): X1, X2, X3
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 2 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## 
## dbl (3): X1, X2, X3
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## [1] FALSE

# convert to CSV
convert(fwf, "fwf.csv", in_opts = list(widths = c(1, 2, 3)))

## Rows: 2 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## 
## dbl (3): X1, X2, X3
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

import("fwf.csv") # check conversion

##   X1 X2  X3
## 1  1 23 456
## 2  9 87 654

With metadata-rich file formats (e.g., Stata, SPSS, SAS), it can also be useful to pass imported data through characterize() or factorize() when converting to an open, text-delimited format: characterize() converts a single variable or all variables in a data frame that have “labels” attributes into character vectors based on the mapping of values to value labels (e.g., export(characterize(import("file.dta")), "file.csv")). An alternative approach is exporting to CSVY format, which records metadata in a YAML-formatted header at the beginning of a CSV file.

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')"