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gesistsa/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 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

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

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()

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.

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

##                  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

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 archives (e.g., zip), 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")

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. You can check which formats are not supported via:

show_unsupported_formats()

You can install the suggested packages individually, depending your own needs. If you want to install all suggested packages:

The full list of supported formats is below:

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.

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')"

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()

• datamods provides Shiny modules for importing data via rio.
• 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.

• 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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