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  • Created almost 9 years ago
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Repository Details

A Swiss-Army Knife for Data I/O

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

CRAN Version Downloads

Overview

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

  • import() provides a painless data import experience by automatically choosing the appropriate import/read function based on file extension (or a specified format argument)
  • export() provides the same painless file recognition for data export/write functionality

Installation

The package is available on CRAN and can be installed directly in R using install.packages().

install.packages("rio")

The latest development version on GitHub can be installed using:

if (!require("remotes")){
    install.packages("remotes")
}
remotes::install_github("gesistsa/rio")

Optional: Installation of additional formats (see below: Supported file formats)

library(rio)
install_formats()

Usage

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.

Import

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

library("rio")
import("starwars.xlsx")
##                  Name homeworld species
## 1      Luke Skywalker  Tatooine   Human
## 2               C-3PO  Tatooine   Human
## 3               R2-D2  Alderaan   Human
## 4         Darth Vader  Tatooine   Human
## 5         Leia Organa  Tatooine   Human
## 6           Owen Lars  Tatooine   Human
## 7  Beru Whitesun lars   Stewjon   Human
## 8               R5-D4  Tatooine   Human
## 9   Biggs Darklighter  Kashyyyk Wookiee
## 10     Obi-Wan Kenobi  Corellia   Human
import("starwars.csv")
##                  Name homeworld species
## 1      Luke Skywalker  Tatooine   Human
## 2               C-3PO  Tatooine   Human
## 3               R2-D2  Alderaan   Human
## 4         Darth Vader  Tatooine   Human
## 5         Leia Organa  Tatooine   Human
## 6           Owen Lars  Tatooine   Human
## 7  Beru Whitesun lars   Stewjon   Human
## 8               R5-D4  Tatooine   Human
## 9   Biggs Darklighter  Kashyyyk Wookiee
## 10     Obi-Wan Kenobi  Corellia   Human

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.

Export

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

A particularly useful feature of rio is the ability to import from and export to compressed (e.g., zip) directories, saving users the extra step of compressing a large exported file, e.g.:

export(mtcars, "mtcars.tsv.zip")

export() can also write multiple data frames to respective sheets of an Excel workbook or an HTML file:

export(list(mtcars = mtcars, iris = iris), file = "mtcars.xlsx")

Supported file formats

rio supports a wide range of file formats. To keep the package slim, several 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) bzip2 / xz / tar utils utils Default
Gzip files gz / gzip base base Default
Zip files zip utils utils Default
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 arrow arrow 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.

Other functions

Convert

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')"

*_list

import_list() allows users to import a list of data frames from a multi-object file (such as an Excel workbook, .Rdata file, zip directory, or HTML file):

str(m <- import_list("mtcars.xlsx"))
## List of 2
##  $ mtcars:'data.frame':  32 obs. of  11 variables:
##   ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##   ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
##   ..$ disp: num [1:32] 160 160 108 258 360 ...
##   ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
##   ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##   ..$ wt  : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
##   ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
##   ..$ vs  : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...
##   ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
##   ..$ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
##   ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
##  $ iris  :'data.frame':  150 obs. of  5 variables:
##   ..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##   ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##   ..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##   ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##   ..$ Species     : chr [1:150] "setosa" "setosa" "setosa" "setosa" ...

export_list() makes it easy to export a list of (possibly named) data frames to multiple files:

export_list(m, "%s.tsv")
c("mtcars.tsv", "iris.tsv") %in% dir()
## [1] TRUE TRUE

Other projects

GUIs

  • rioweb that provides access to the file conversion features of rio.
  • GREA is an RStudio add-in that provides an interactive interface for reading in data using rio.

Similar packages

  • reader handles certain text formats and R binary files
  • io offers a set of custom formats
  • ImportExport focuses on select binary formats (Excel, SPSS, and Access files) and provides a Shiny interface.
  • SchemaOnRead iterates through a large number of possible import methods until one works successfully

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