Read flat files (csv, tsv, fwf) into R
Branch: master
Clone or download
thays42 and jimhester Replace data_frame with tibble (#960)
* Replace data_frame with tibble

* Update
Latest commit 01fbd53 Feb 17, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Revert "Turn off message temporarily, to reduce spam" Sep 25, 2018
R Replace data_frame with tibble (#960) Feb 17, 2019
data-raw Add challenging data for guessing column types Jul 10, 2016
inst Install shared library to a architecture specific library (if available) Nov 29, 2018
man Merge branch 'master' into mkearney-master Jan 19, 2019
notes Ignore the correct vignette. Oct 16, 2015
pkgdown/favicon Switch to a svg logo and build favicons Nov 15, 2018
revdep Tweak ignore Dec 7, 2018
src Fix skipping with windows newlines Dec 18, 2018
tests Replace data_frame with tibble (#960) Feb 17, 2019
travis Try moving logic to Apr 19, 2017
vignettes Fix spelling and add it to tests Nov 21, 2018
.Rbuildignore Install shared library to a architecture specific library (if available) Nov 29, 2018
.gitattributes Set merge strategy for the news file Jun 8, 2016
.gitignore Just gitignore all .o and .so files Dec 6, 2018
.travis.yml Try updating the condition Dec 20, 2018 Add code of conduct Dec 14, 2017
DESCRIPTION Increment version number Dec 21, 2018
LICENSE Add license file May 21, 2015
NAMESPACE Add read_built() Jan 19, 2019 Replace data_frame with tibble (#960) Feb 17, 2019
README.Rmd typo Dec 17, 2018 typo Dec 17, 2018
_pkgdown.yml Remove leading slash for article Feb 11, 2019
appveyor.yml Add appveyor builds Aug 2, 2016
codecov.yml Use tidy CI, testing back to R 3.1 Nov 29, 2018 Change cran-comments for release Dec 20, 2018
rcon Rename library to rcon Nov 20, 2018
readr.Rproj Don't build vignettes (to save time when checking) Sep 3, 2015


CRAN_Status_Badge Build Status AppVeyor Build Status Coverage Status


The goal of readr is to provide a fast and friendly way to read rectangular data (like csv, tsv, and fwf). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes. If you are new to readr, the best place to start is the data import chapter in R for data science.


# The easiest way to get readr is to install the whole tidyverse:

# Alternatively, install just readr:

# Or the the development version from GitHub:
# install.packages("devtools")



readr is part of the core tidyverse, so load it with:

#> ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
#> ✔ tibble  1.4.2     ✔ dplyr   0.7.7
#> ✔ tidyr   0.8.2     ✔ stringr 1.3.1
#> ✔ readr   1.2.0     ✔ forcats 0.3.0
#> ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()

To accurately read a rectangular dataset with readr you combine two pieces: a function that parses the overall file, and a column specification. The column specification describes how each column should be converted from a character vector to the most appropriate data type, and in most cases it’s not necessary because readr will guess it for you automatically.

readr supports seven file formats with seven read_ functions:

  • read_csv(): comma separated (CSV) files
  • read_tsv(): tab separated files
  • read_delim(): general delimited files
  • read_fwf(): fixed width files
  • read_table(): tabular files where columns are separated by white-space.
  • read_log(): web log files

In many cases, these functions will just work: you supply the path to a file and you get a tibble back. The following example loads a sample file bundled with readr:

mtcars <- read_csv(readr_example("mtcars.csv"))
#> Parsed with column specification:
#> cols(
#>   mpg = col_double(),
#>   cyl = col_double(),
#>   disp = col_double(),
#>   hp = col_double(),
#>   drat = col_double(),
#>   wt = col_double(),
#>   qsec = col_double(),
#>   vs = col_double(),
#>   am = col_double(),
#>   gear = col_double(),
#>   carb = col_double()
#> )

Note that readr prints the column specification. This is useful because it allows you to check that the columns have been read in as you expect, and if they haven’t, you can easily copy and paste into a new call:

mtcars <- read_csv(readr_example("mtcars.csv"), col_types = 
    mpg = col_double(),
    cyl = col_integer(),
    disp = col_double(),
    hp = col_integer(),
    drat = col_double(),
    vs = col_integer(),
    wt = col_double(),
    qsec = col_double(),
    am = col_integer(),
    gear = col_integer(),
    carb = col_integer()

vignette("readr") gives more detail on how readr guesses the column types, how you can override the defaults, and provides some useful tools for debugging parsing problems.


There are two main alternatives to readr: base R and data.table’s fread(). The most important differences are discussed below.

Base R

Compared to the corresponding base functions, readr functions:

  • Use a consistent naming scheme for the parameters (e.g. col_names and col_types not header and colClasses).

  • Are much faster (up to 10x).

  • Leave strings as is by default, and automatically parse common date/time formats.

  • Have a helpful progress bar if loading is going to take a while.

  • All functions work exactly the same way regardless of the current locale. To override the US-centric defaults, use locale().

data.table and fread()

data.table has a function similar to read_csv() called fread. Compared to fread, readr functions:

  • Are slower. If you want absolutely the best performance, use data.table::fread().

  • Use a slightly more sophisticated parser, recognising both doubled ("""") and backslash escapes ("\""), and can produce factors and date/times directly.

  • Forces you to supply all parameters, where fread() saves you work by automatically guessing the delimiter, whether or not the file has a header, and how many lines to skip.

  • Are built on a different underlying infrastructure. Readr functions are designed to be quite general, which makes it easier to add support for new rectangular data formats. fread() is designed to be as fast as possible.


Thanks to:

  • Joe Cheng for showing me the beauty of deterministic finite automata for parsing, and for teaching me why I should write a tokenizer.

  • JJ Allaire for helping me come up with a design that makes very few copies, and is easy to extend.

  • Dirk Eddelbuettel for coming up with the name!

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.