Read Rectangular Text Data

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.

The goal of readr is to provide a fast and friendly way to read tabular data into R. The most important functions are:

  • Read delimited files: read_delim(), read_csv(), read_tsv(), read_csv2().
  • Read fixed width files: read_fwf(), read_table().
  • Read lines: read_lines().
  • Read whole file: read_file().
  • Re-parse existing data frame: type_convert().

readr is now available from CRAN.


You can try out the dev version with:

# install.packages("devtools")
mtcars_path <- tempfile(fileext = ".csv")
write_csv(mtcars, mtcars_path)
# Read a csv file into a data frame
# Read lines into a vector
# Read whole file into a single string

See vignette("column-types") on how readr parses columns, and how you can override the defaults.

read_csv() produces a data frame with the following properties:

  • Characters are never automatically converted to factors (i.e. no more stringsAsFactors = FALSE).

  • Valid column names are left as is, not munged into valid R identifiers (i.e. there is no check.names = TRUE). Missing column names are filled in with X1, X2 etc, and duplicated column names are deduplicated.

  • The data frame is given class c("tbl_df", "tbl", "data.frame") so if you also use dplyr you'll get an enhanced display.

  • Row names are never set.

If there are any problems parsing the file, the read_ function will throw a warning telling you how many problems there are. You can then use the problems() function to access a data frame that gives information about each problem:

df <- read_csv(col_types = "dd", col_names = c("x", "y"), skip = 1, "
#> Warning message: There were 2 problems. See problems(x) for more details
#>   row col expected actual
#> 1   2   1 a double      a
#> 2   2   2 a double      b

It's likely that there will be cases that you can never load without some manual regexp-based munging in R. Load those columns with col_character(), fix them up as needed, then use convert_types() to re-run the automated conversion on every character column in the data frame. Alternatively, you can use parse_integer(), parse_numeric(), parse_date() etc to parse a single character vector at a time.

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 faster).

  • 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 has a function similar to read_csv() called fread. Compared to fread, readr:

  • Is slower (currently ~1.2-2x slower. If you want absolutely the best performance, use data.table::fread().

  • Readr has a slightly more sophisticated parser, recognising both doubled ("""") and backslash escapes ("""). Readr allows you to read factors and date times directly from disk.

  • fread() saves you work by automatically guessing the delimiter, whether or not the file has a header, how many lines to skip by default and more. Readr forces you to supply these parameters.

  • The underlying designs are quite different. Readr is designed to be general, and dealing with new types of rectangular data just requires implementing a new tokenizer. fread() is designed to be as fast as possible. fread() is pure C, readr is C++ (and Rcpp).

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!


readr 1.0.0

The process by which readr guesses the types of columns has received a substantial overhaul to make it easier to fix problems when the initial guesses aren't correct, and to make it easier to generate reproducible code. Now column specifications are printing by default when you read from a file:

challenge <- read_csv(readr_example("challenge.csv"))
#> Parsed with column specification:
#> cols(
#>   x = col_integer(),
#>   y = col_character()
#> )

And you can extract those values after the fact with spec():

#> cols(
#>   x = col_integer(),
#>   y = col_character()
#> )

This makes it easier to quickly identify parsing problems and fix them (#314). If the column specification is long, the new cols_condense() is used to condense the spec by identifying the most common type and setting it as the default. This is particularly useful when only a handful of columns have a different type (#466).

You can also generating an initial specification without parsing the file using spec_csv(), spec_tsv(), etc.

Once you have figured out the correct column types for a file, it's often useful to make the parsing strict. You can do this either by copying and pasting the printed output, or for very long specs, saving the spec to disk with write_rds(). In production scripts, combine this with stop_for_problems() (#465): if the input data changes form, you'll fail fast with an error.

You can now also adjust the number of rows that readr uses to guess the column types with guess_max:

challenge <- read_csv(readr_example("challenge.csv"), guess_max = 1500)
#> Parsed with column specification:
#> cols(
#>   x = col_double(),
#>   y = col_date(format = "")
#> )

You can now access the guessing algorithm from R. guess_parser() will tell you which parser readr will select for a character vector (#377). We've made a number of fixes to the guessing algorithm:

  • New example extdata/challenge.csv which is carefully created to cause problems with the default column type guessing heuristics.

  • Blank lines and lines with only comments are now skipped automatically without warning (#381, #321).

  • Single '-' or '.' are now parsed as characters, not numbers (#297).

  • Numbers followed by a single trailing character are parsed as character, not numbers (#316).

  • We now guess at times using the time_format specified in the locale().

We have made a number of improvements to the reification of the col_types, col_names and the actual data:

  • If col_types is too long, it is subsetted correctly (#372, @jennybc).

  • If col_names is too short, the added names are numbered correctly (#374, @jennybc).

  • Missing colum name names are now given a default name (X2, X7 etc) (#318). Duplicated column names are now deduplicated. Both changes generate a warning; to suppress it supply an explicit col_names (setting skip = 1 if there's an existing ill-formed header).

  • col_types() accepts a named list as input (#401).

The date time parsers recognise three new format strings:

  • %I for 12 hour time format (#340).

  • %AD and %AT are "automatic" date and time parsers. They are both slightly less flexible than previous defaults. The automatic date parser requires a four digit year, and only accepts - and / as separators (#442). The flexible time parser now requires colons between hours and minutes and optional seconds (#424).

%y and %Y are now strict and require 2 or 4 characters respectively.

Date and time parsing functions received a number of small enhancements:

  • parse_time() returns hms objects rather than a custom time class (#409). It now correctly parses missing values (#398).

  • parse_date() returns a numeric vector (instead of an integer vector) (#357).

  • parse_date(), parse_time() and parse_datetime() gain an na argument to match all other parsers (#413).

  • If the format argument is omitted parse_date() or parse_time(), date and time formats specified in the locale will be used. These now default to %AD and %AT respectively.

  • You can now parse partial dates with parse_date() and parse_datetime(), e.g. parse_date("2001", "%Y") returns 2001-01-01.

parse_number() is slightly more flexible - it now parses numbers up to the first ill-formed character. For example parse_number("-3-") and parse_number("...3...") now return -3 and 3 respectively. We also fixed a major bug where parsing negative numbers yielded positive values (#308).

parse_logical() now accepts 0, 1 as well as lowercase t, f, true, false.

  • read_file_raw() reads a complete file into a single raw vector (#451).

  • read_*() functions gain a quoted_na argument to control whether missing values within quotes are treated as missing values or as strings (#295).

  • write_excel_csv() can be used to write a csv file with a UTF-8 BOM at the start, which forces Excel to read it as UTF-8 encoded (#375).

  • write_lines() writes a character vector to a file (#302).

  • write_file() to write a single character or raw vector to a file (#474).

  • Experimental support for chunked reading a writing (read_*_chunked()) functions. The API is unstable and subject to change in the future (#427).

  • Printing double values now uses an implementation of the grisu3 algorithm which speeds up writing of large numeric data frames by ~10X. (#432) '.0' is appended to whole number doubles, to ensure they will be read as doubles as well. (#483)

  • readr imports tibble so that you get consistent tbl_df behaviour (#317, #385).

  • New example extdata/challenge.csv which is carefully created to cause problems with the default column type guessing heuristics.

  • default_locale() now sets the default locale in readr.default_locale rather than regenerating it for each call. (#416).

  • locale() now automatically sets decimal mark if you set the grouping mark. It throws an error if you accidentally set decimal and grouping marks to the same character (#450).

  • All read_*() can read into long vectors, substantially increasing the number of rows you can read (#309).

  • All read_*() functions return empty objects rather than signaling an error when run on an empty file (#356, #441).

  • read_delim() gains a trim_ws argument (#312, noamross)

  • read_fwf() received a number of improvements:

    • read_fwf() now can now reliably read only a partial set of columns (#322, #353, #469)

    • fwf_widths() accepts negative column widths for compatibility with the widths argument in read.fwf() (#380, @leeper).

    • You can now read fixed width files with ragged final columns, by setting the final end position in fwf_positions() or final width in fwf_widths() to NA (#353, @ghaarsma). fwf_empty() does this automatically.

    • read_fwf() and fwf_empty() can now skip commented lines by setting a comment argument (#334).

  • read_lines() ignores embedded null's in strings (#338) and gains a na argument (#479).

  • readr_example() makes it easy to access example files bundled with readr.

  • type_convert() now accepts only NULL or a cols specification for col_types (#369).

  • write_delim() and write_csv() now invisibly return the input data frame (as documented, #363).

  • Doubles are parsed with boost::spirit::qi::long_double to work around a bug in the spirit library when parsing large numbers (#412).

  • Fix bug when detecting column types for single row files without headers (#333).

readr 0.2.2

  • Fix bug when checking empty values for missingness (caused valgrind issue and random crashes).

readr 0.2.1

  • Fixes so that readr works on Solaris.

readr 0.2.0

readr now has a strategy for dealing with settings that vary from place to place: locales. The default locale is still US centric (because R itself is), but you can now easily override the default timezone, decimal separator, grouping mark, day & month names, date format, and encoding. This has lead to a number of changes:

  • read_csv(), read_tsv(), read_fwf(), read_table(), read_lines(), read_file(), type_convert(), parse_vector() all gain a locale argument.

  • locale() controls all the input settings that vary from place-to-place.

  • col_euro_double() and parse_euro_double() have been deprecated. Use the decimal_mark parameter to locale() instead.

  • The default encoding is now UTF-8. To load files that are not in UTF-8, set the encoding parameter of the locale() (#40). New guess_encoding() function uses stringi to help you figure out the encoding of a file.

  • parse_datetime() and parse_date() with %B and %b use the month names (full and abbreviate) defined in the locale (#242). They also inherit the tz from the locale, rather than using an explicit tz parameter.

See vignette("locales") for more details.

  • cols() lets you pick the default column type for columns not otherwise explicitly named (#148). You can refer to parsers either with their full name (e.g. col_character()) or their one letter abbreviation (e.g. c).

  • cols_only() allows you to load only named columns. You can also choose to override the default column type in cols() (#72).

  • read_fwf() is now much more careful with new lines. If a line is too short, you'll get a warning instead of a silent mistake (#166, #254). Additionally, the last column can now be ragged: the width of the last field is silently extended until it hits the next line break (#146). This appears to be a common feature of "fixed" width files in the wild.

  • In read_csv(), read_tsv(), read_delim() etc:

    • comment argument allows you to ignore comments (#68).

    • trim_ws argument controls whether leading and trailing whitespace is removed. It defaults to TRUE (#137).

    • Specifying the wrong number of column names, or having rows with an unexpected number of columns, generates a warning, rather than an error (#189).

    • Multiple NA values can be specified by passing a character vector to na (#125). The default has been changed to na = c("", "NA"). Specifying na = "" now works as expected with character columns (#114).

Readr gains vignette("column-types") which describes how the defaults work and how to override them (#122).

  • parse_character() gains better support for embedded nulls: any characters after the first null are dropped with a warning (#202).

  • parse_integer() and parse_double() no longer silently ignore trailing letters after the number (#221).

  • New parse_time() and col_time() allows you to parse times (hours, minutes, seconds) into number of seconds since midnight. If the format is omitted, it uses a flexible parser that looks for hours, then optional colon, then minutes, then optional colon, then optional seconds, then optional am/pm (#249).

  • parse_date() and parse_datetime():

    • parse_datetime() no longer incorrectly reads partial dates (e.g. 19, 1900, 1900-01) (#136). These triggered common false positives and after re-reading the ISO8601 spec, I believe they actually refer to periods of time, and should not be translated in to a specific instant (#228).

    • Compound formats "%D", "%F", "%R", "%X", "%T", "%x" are now parsed correctly, instead of using the ISO8601 parser (#178, @kmillar).

    • "%." now requires a non-digit. New "%+" skips one or more non-digits.

    • You can now use %p to refer to AM/PM (and am/pm) (#126).

    • %b and %B formats (month and abbreviated month name) ignore case when matching (#219).

    • Local (non-UTC) times with and without daylight savings are now parsed correctly (#120, @andres-s).

  • parse_number() is a somewhat flexible numeric parser designed to read currencies and percentages. It only reads the first number from a string (using the grouping mark defined by the locale).

  • parse_numeric() has been deprecated because the name is confusing - it's a flexible number parser, not a parser of "numerics", as R collectively calls doubles and integers. Use parse_number() instead.

As well as improvements to the parser, I've also made a number of tweaks to the heuristics that readr uses to guess column types:

  • New parse_guess() and col_guess() to explicitly guess column type.

  • Bumped up row inspection for column typing guessing from 100 to 1000.

  • The heuristics for guessing col_integer() and col_double() are stricter. Numbers with leading zeros now default to being parsed as text, rather than as integers/doubles (#266).

  • A column is guessed as col_number() only if it parses as a regular number when you ignoring the grouping marks.

  • Now use R's platform independent iconv wrapper, thanks to BDR (#149).

  • Pathological zero row inputs (due to empty input, skip or n_max) now return zero row data frames (#119).

  • When guessing field types, and there's no information to go on, use character instead of logical (#124, #128).

  • Concise col_types specification now understands ? (guess) and - (skip) (#188).

  • count_fields() starts counting from 1, not 0 (#200).

  • format_csv() and format_delim() make it easy to render a csv or delimited file into a string.

  • fwf_empty() now works correctly when col_names supplied (#186, #222).

  • parse_*() gains a na argument that allows you to specify which values should be converted to missing.

  • problems() now reports column names rather than column numbers (#143). Whenever there is a problem, the first five problems are printing out in a warning message, so you can more easily see what's wrong.

  • read_*() throws a warning instead of an error is col_types specifies a non-existent column (#145, @alyst).

  • read_*() can read from a remote gz compressed file (#163).

  • read_delim() defaults to escape_backslash = FALSE and escape_double = TRUE for consistency. n_max also affects the number of rows read to guess the column types (#224).

  • read_lines() gains a progress bar. It now also correctly checks for interrupts every 500,000 lines so you can interrupt long running jobs. It also correctly estimates the number of lines in the file, considerably speeding up the reading of large files (60s -> 15s for a 1.5 Gb file).

  • read_lines_raw() allows you to read a file into a list of raw vectors, one element for each line.

  • type_convert() gains NA and trim_ws arguments, and removes missing values before determining column types.

  • write_csv(), write_delim(), and write_rds() all invisably return their input so you can use them in a pipe (#290).

  • write_delim() generalises write_csv() to write any delimited format (#135). write_tsv() is a helpful wrapper for tab separated files.

    • Quotes are only used when they're needed (#116): when the string contains a quote, the delimiter, a new line or NA.

    • Double vectors are saved using same amount of precision as as.character() (#117).

    • New na argument that specifies how missing values should be written (#187)

    • POSIXt vectors are saved in a ISO8601 compatible format (#134).

    • No longer fails silently if it can't open the target for writing (#193, #172).

  • write_rds() and read_rds() wrap around readRDS() and saveRDS(), defaulting to no compression (#140, @nicolasCoutin).

Reference manual

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1.1.1 by Jim Hester, 7 months ago,

Report a bug at

Browse source code at

Authors: Hadley Wickham [aut], Jim Hester [aut, cre], Romain Francois [aut], R Core Team [ctb] (Date time code adapted from R), RStudio [cph, fnd], Jukka Jylänki [ctb, cph] (grisu3 implementation), Mikkel Jørgensen [ctb, cph] (grisu3 implementation)

Documentation:   PDF Manual  

GPL (>= 2) | file LICENSE license

Imports Rcpp, tibble, hms, R6

Suggests curl, testthat, knitr, rmarkdown, stringi, covr

Linking to Rcpp, BH

Imported by ArchaeoPhases, BIS, DMwR2, DiagrammeR, EventStudy, FedData, GCalignR, GSODR, GetDFPData, GetHFData, GetITRData, GetLattesData, HURDAT, PKPDmisc, PNADcIBGE, PWFSLSmoke, REDCapR, RQGIS, Rilostat, Rnightlights, SanFranBeachWater, ShinyTester, TeXCheckR, abbyyR, abcrf, actogrammr, aire.zmvm, alakazam, alphavantager, asciiSetupReader, banR, benthos, bigrquery, biomartr, bomrang, breathtestcore, cdcfluview, cleanNLP, crosswalkr, dataRetrieval, dataonderivatives, datapasta, datasus, ddpcr, dwapi, ecoseries, eemR, epidata, epitable, esc, esmisc, estatapi, etl, eurostat, evaluator, exampletestr, eyelinker, farff, fastqcr, fingertipsR, geojsonio, geomnet, getlandsat, ggCompNet, ggguitar, ggplotgui, googlesheets, gutenbergr, haven, hypoparsr, ipumsr, jpmesh, jpndistrict, kableExtra, macleish, metacoder, mljar, mosaic, mudata2, myTAI, nandb, ncappc, nesRdata, neuroim, oec, openadds, openwindfarm, pcr, pdfetch, photobiologyInOut, pointblank, predatory, rPraat,, rcv, readODS, rgeopat2, rgho, rmsfuns, rpdo, rrefine, rsoi, rtimicropem, rtweet, sasMap, sergeant, softermax, sparklyr, spatialEco, starmie, stationaRy, statsDK, stplanr, sugrrants, survivALL, swirlify, tidycensus, tidyquant, tidystats, tidyverse, timetk, traits, ukbtools, valr, vpc, webTRISr, webreadr, worldmet, xpose, xpose4.

Depended on by MOQA, MicroDatosEs, efreadr.

Suggested by IalsaSynthesis, MazamaSpatialUtils, RSocrata, auk, csvy, cytominer,, descriptr, eechidna, enc, epicontacts, europepmc, europop, fastR2, fuzzyjoin, getCRUCLdata, googleCloudStorageR, gsheet, httr, inferr, kokudosuuchi, leaflet.esri, leaflet.extras, olsrr, pccc, phonics, pollstR, prophet, rakeR, raw, rcongresso, rio, rmonad, rrr, spup, sweep, tidytext.

See at CRAN