Package rio. R topics documented: January 14, Type Package Title A Swiss-Army Knife for Data I/O Version 0.3.

Transcription

1 Type Package Title A Swiss-Army Knife for Data I/O Version Date Package rio January 14, 2016 Maintainer Thomas J. Leeper <thosjleeper@gmail.com> Description Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., 'stringsasfactors=false'), webbased import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate. Depends R (>= ) Imports tools, stats, utils, urltools, foreign, haven, openxlsx, readods, jsonlite, XML, curl (>= 0.6), data.table (>= 1.9.5), readxl, yaml Suggests datasets, bit64, testthat, knitr, magrittr License GPL-2 VignetteBuilder knitr NeedsCompilation no Author Chung-hong Chan [aut], Geoffrey CH Chan [aut], Thomas J. Leeper [aut, cre], Christopher Gandrud [aut], Ista Zahn [aut] Repository CRAN Date/Publication :52:34 R topics documented: convert export import rio

2 2 convert Index 8 convert Convert from one file format to another Description Usage This function constructs a data frame from a data file using codeimport and uses export to write the data to disk in the format indicated by the file extension. convert(in_file, out_file, in_opts = list(), out_opts = list()) Arguments in_file out_file in_opts out_opts a character string naming an input file. a character string naming an output file. a named list of options to be passed to import a named list of options to be passed to export Value See Also The name of the output file (invisibly). Luca Braglia has created a Shiny app called rioweb that provides access to the file conversion features of rio through a web browser. The app is featured in the RStudio Shiny Gallery. Examples # create a file to convert export(iris, "iris.dta") # convert Stata to CSV and open converted file convert("iris.dta", "iris.csv") head(import("iris.csv")) # correct an erroneous file format export(iris, "iris.csv", format = "tsv") convert("iris.csv", "iris.csv", in_opts = list(format = "tsv")) # convert serialized R data.frame to JSON export(iris, "iris.rds") convert("iris.rds", "iris.json") # cleanup unlink("iris.csv")

3 export 3 unlink("iris.dta") unlink("iris.rds") unlink("iris.json") ## Not run: # convert from the command line: Rscript -e "rio::convert( iris.dta, iris.csv )" ## End(Not run) export Write data.frame or matrix to a file Description This function exports a data frame or matrix into a file with file format based on the file extension (or the manually specified format, if format is specified). export supports many file formats. See the documentation for the underlying export functions for optional arguments that can be passed via... Tab-separated data (.tsv), using write.table with row.names = FALSE Comma-separated data (.csv), using write.csv with row.names = FALSE Pipe-separated data (.psv), using write.table with sep = and row.names = FALSE Fixed-width format data (.fwf), using write.table with row.names = FALSE, quote = FALSE, and col.names = FALSE Serialized R objects (.rds), using saverds Saved R objects (.RData), using save JSON (.json), using tojson Stata (.dta), using write_dta SPSS (.sav), using write_sav "XBASE" database files (.dbf), using write.dbf Weka Attribute-Relation File Format (.arff), using write.arff R syntax object (.R), using dput (by default) or dump (if format = dump Excel (.xlsx), using write.xlsx XML (.xml), using a custom method to create an XML tree and write to disk using savexml Clipboard export (on Windows and Mac OS), using write.table with row.names = FALSE Usage export(x, file, format,...)

4 4 export Arguments x file format A data frame or matrix to be written into a file. A character string naming a file. Must specify file and/or format. An optional character string containing the file format, which can be used to override the format inferred from file or, in lieu of specifying file, a file with the symbol name of x and the specified file extension will be created. Must specify file and/or format. Shortcuts include:, (for comma-separated values), ; (for semicolon-separated values), (for pipe-separated values), and dump for dump.... Additional arguments for the underlying export functions. Value The name of the output file as a character string (invisibly). Examples # specify only file argument export(iris, "iris.csv") # specify only format argument "iris.dta" export(iris, format = "stata") "iris.dta" # specify file and format to override default format export(iris, file = "iris.txt", format = "csv") # export to JSON export(iris, "iris.json") # pass arguments to underlying export function export(iris, "iris.csv", col.names = FALSE) # write data to.r syntax file and append additional data export(iris, file = "data.r", format = "dump") export(mtcars, file = "data.r", format = "dump", append = TRUE) source("data.r", echo = TRUE) # cleanup unlink("iris.csv") unlink("iris.dta") unlink("iris.json") unlink("data.r")

5 import 5 import Read data.frame or matrix from a file Description This function imports a data frame or matrix from a data file with the file format based on the file extension (or the manually specified format, if format is specified). import supports the following file formats: Tab-separated data (.tsv), using read.table with row.names = FALSE and stringsasfactors = FALSE (or, if fread = TRUE, fread) Comma-separated data (.csv), using read.csv with row.names = FALSE and stringsasfactors = FALSE (or, if fread = TRUE, fread) CSVY (CSV with a YAML metadata header) using read.csv with row.names = FALSE and stringsasfactors = FALSE (or, if fread = TRUE, fread) Pipe-separated data (.psv), using read.table with sep =, row.names = FALSE, and stringsasfactors = FALSE (or, if fread = TRUE, fread) Fixed-width format data (.fwf), using a faster version of read.fwf that requires a widths argument and by default in rio has stringsasfactors = FALSE Serialized R objects (.rds), using readrds Saved R objects (.RData), using load for single-object.rdata files JSON (.json), using fromjson Stata (.dta), using read_dta. If haven = FALSE, read.dta can be used. If haven = TRUE metadata will be stored as object-level attributes by default; if column.labels = TRUE metadata will be stored as column-level attributes. SPSS and SPSS portable (.sav and.por), using read_sav and read_por. If haven = FALSE, read.spss can be used for.sav files. If haven = TRUE metadata will be stored as objectlevel attributes by default; if column.labels = TRUE metadata will be stored as column-level attributes. "XBASE" database files (.dbf), using read.dbf Weka Attribute-Relation File Format (.arff), using read.arff R syntax object (.R), using dget Excel (.xls and.xlsx), using read_excel. If readxl = FALSE, read.xlsx can be used. SAS (.sas7bdat) and SAS XPORT (.xpt), using read_sas and read.xport. If haven = TRUE metadata will be stored as object-level attributes by default; if column.labels = TRUE metadata will be stored as column-level attributes. Minitab (.mtp), using read.mtp Epiinfo (.rec), using read.epiinfo Systat (.syd), using read.systat Data Interchange Format (.dif), using read.dif

6 6 import Usage Arguments Value Note file OpenDocument Spreadsheet (.ods), using read.ods Shallow XML documents (.xml), using xmltodataframe. Note: optional arguments not recognized by xmltodataframe are passed to xmlparse. Clipboard import (on Windows and Mac OS), using read.table with row.names = FALSE Fortran data (no recognized extension), using read.fortran Google Sheets, as Comma-separated data (.csv) format setclass import(file, format, setclass,...) A character string naming a file, URL, or single-file.zip or.tar archive. An optional character string code of file format, which can be used to override the format inferred from file. Shortcuts include:, (for comma-separated values), ; (for semicolon-separated values), and (for pipe-separated values). An optional character vector specifying one or more classes to set on the import. By default, all the return object is always a data.frame. Reasonable values for this might be tbl_df (if using dplyr) or data.table (if using data.table). Warnings will be produced if a class is used from a package that is not loaded and/or available.... Additional arguments passed to the underlying import functions. For example, this can control column classes for delimited file types, or control the use of haven for Stata and SPSS or readxl for Excel (.xlsx) format. See details below. An R data.frame. If setclass is used, this data.frame may have additional class attribute values. For csv and txt files with row names exported from export, it may be helpful to specify row.names as the column of the table which contain row names. See example below. Examples # create CSV to import export(iris, "iris1.csv") # specify format to override default format export(iris, "iris.tsv", format = "csv") stopifnot(identical(import("iris1.csv"), import("iris.tsv", format = "csv"))) # import CSV as a data.table stopifnot(inherits(import("iris1.csv", setclass = "data.table"), "data.table")) stopifnot(inherits(import("iris1.csv", setclass = "data.table"), "data.table"))

7 rio 7 # pass arguments to underlying import function iris1 <- import("iris1.csv") identical(names(iris), names(iris1)) export(iris, "iris2.csv", col.names = FALSE) iris2 <- import("iris2.csv") identical(names(iris), names(iris2)) # set class for the response data.frame as "tbl_df" (from dplyr) stopifnot(inherits(import("iris1.csv", setclass = "tbl_df"), "tbl_df")) # cleanup unlink("iris.tsv") unlink("iris1.csv") unlink("iris2.csv") rio A Swiss-army knife for data I/O Description The aim of rio is to make data file I/O in R as easy as possible. The export and import serve as a Swiss-army knife for painless data I/O for data from almost any file format by inferring the data structure from the file extension, natively reading web-based data sources, setting reasonable defaults for import and export, and relying on efficient data import and export packages.

8 Index convert, 2 dget, 5 dput, 3 dump, 3, 4 export, 2, 3, 6 fread, 5 fromjson, 5 import, 2, 5 load, 5 read.arff, 5 read.csv, 5 read.dbf, 5 read.dif, 5 read.dta, 5 read.epiinfo, 5 read.fortran, 6 read.fwf, 5 read.mtp, 5 read.ods, 6 read.spss, 5 read.systat, 5 read.table, 5, 6 read.xlsx, 5 read.xport, 5 read_dta, 5 read_excel, 5 read_por, 5 read_sas, 5 read_sav, 5 readrds, 5 rio, 7 rio-package (rio), 7 savexml, 3 tojson, 3 write.arff, 3 write.csv, 3 write.dbf, 3 write.table, 3 write.xlsx, 3 write_dta, 3 write_sav, 3 xmlparse, 6 xmltodataframe, 6 save, 3 saverds, 3 8