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readr (development version)

readr 2.1.5

  • No major user-facing changes. Patch release with housekeeping changes and internal changes requested by CRAN around format specification in compiled code.

readr 2.1.4

CRAN release: 2023-02-10

  • No user-facing changes. Patch release with internal changes requested by CRAN.

readr 2.1.3

CRAN release: 2022-10-01

  • Help files below man/ have been re-generated, so that they give rise to valid HTML5. (This is the impetus for this release, to keep the package safely on CRAN.)

  • mini-gapminder-africa.csv and friends are new example datasets accessible via readr_example(), which have been added to illustrate reading multiple files at once, into a single data frame.

readr 2.1.2

CRAN release: 2022-01-30

readr 2.1.1

CRAN release: 2021-11-30

  • Jenny Bryan is now the maintainer.

  • Fix buffer overflow when trying to parse an integer from a field that is over 64 characters long (#1326)

readr 2.1.0

CRAN release: 2021-11-11

  • All readr functions again read eagerly by default. Unfortunately many users experienced frustration from the drawbacks of lazy reading, in particular locking files on Windows, so it was decided to disable lazy reading default. However options(readr.read_lazy = TRUE) can be used to set the default to by lazy if desired.
  • New readr.read_lazy global option to control if readr reads files lazily or not (#1266)

readr 2.0.2

CRAN release: 2021-09-27

  • minor test tweak for compatibility with testthat 3.1.0 (#@lionel-, #1304)

  • write_rds() gains a text= argument, to control using a text based object representation, like the ascii= argument in saveRDS() (#1270)

readr 2.0.1

CRAN release: 2021-08-10

readr 2.0.0

CRAN release: 2021-07-20

second edition changes

readr 2.0.0 is a major release of readr and introduces a new second edition parsing and writing engine implemented via the vroom package.

This engine takes advantage of lazy reading, multi-threading and performance characteristics of modern SSD drives to significantly improve the performance of reading and writing compared to the first edition engine.

We will continue to support the first edition for a number of releases, but eventually this support will be first deprecated and then removed.

You can use the with_edition() or local_edition() functions to temporarily change the edition of readr for a section of code.


  • with_edition(1, read_csv("my_file.csv")) will read my_file.csv with the first edition of readr.

  • readr::local_edition(1) placed at the top of your function or script will use the first edition for the rest of the function or script.

Lazy reading

Edition two uses lazy reading by default. When you first call a read_*() function the delimiters and newlines throughout the entire file are found, but the data is not actually read until it is used in your program. This can provide substantial speed improvements for reading character data. It is particularly useful during interactive exploration of only a subset of a full dataset.

However this also means that problematic values are not necessarily seen immediately, only when they are actually read. Because of this a warning will be issued the first time a problem is encountered, which may happen after initial reading.

Run problems() on your dataset to read the entire dataset and return all of the problems found. Run problems(lazy = TRUE) if you only want to retrieve the problems found so far.

Deleting files after reading is also impacted by laziness. On Windows open files cannot be deleted as long as a process has the file open. Because readr keeps a file open when reading lazily this means you cannot read, then immediately delete the file. readr will in most cases close the file once it has been completely read. However, if you know you want to be able to delete the file after reading it is best to pass lazy = FALSE when reading the file.

Reading multiple files at once

Edition two has built-in support for reading sets of files with the same columns into one output table in a single command. Just pass the filenames to be read in the same vector to the reading function.

First we generate some files to read by splitting the nycflights dataset by airline.

  split(flights, flights$carrier),
  ~ { .x$carrier[[1]]; vroom::vroom_write(.x, glue::glue("flights_{.y}.tsv"), delim = "\t") }

Then we can efficiently read them into one tibble by passing the filenames directly to readr.

files <- fs::dir_ls(glob = "flights*tsv")

If the filenames contain data, such as the date when the sample was collected, use id argument to include the paths as a column in the data. You will likely have to post-process the paths to keep only the relevant portion for your use case.

Delimiter guessing

Edition two supports automatic guessing of delimiters. Because of this you can now use read_delim() without specifying a delim argument in many cases.

x <- read_delim(readr_example("mtcars.csv"))

Literal data

In edition one the reading functions treated any input with a newline in it or vectors of length > 1 as literal data. In edition two vectors of length > 1 are now assumed to correspond to multiple files. Because of this we now have a more explicit way to represent literal data, by putting I() around the input.


License changes

We are systematically re-licensing tidyverse and r-lib packages to use the MIT license, to make our package licenses as clear and permissive as possible.

To this end the readr and vroom packages are now released under the MIT license.

Deprecated or superseded functions and features

  • melt_csv(), melt_delim(), melt_tsv() and melt_fwf() have been superseded by functions in the same name in the meltr package. The versions in readr have been deprecated. These functions rely on the first edition parsing code and would be challenging to update to the new parser. When the first edition parsing code is eventually removed from readr they will be removed.

  • read_table2() has been renamed to read_table(), as most users expect read_table() to work like utils::read.table(). If you want the previous strict behavior of the read_table() you can use read_fwf() with fwf_empty() directly (#717).

  • Normalizing newlines in files with just carriage returns \r is no longer supported. The last major OS to use only CR as the newline was ‘classic’ Mac OS, which had its final release in 2001.

Other second edition changes

  • read_*_chunked() functions now include their specification as an attribute (#1143)

  • All read_*() functions gain a col_select argument to more easily choose which columns to select.

  • All read_*() functions gain a id argument to optionally store the file paths when reading multiple files.

  • All read_*() functions gain a name_repair argument to control how column names are repaired.

  • All read_*() and write_*() functions gain a num_threads argument to control the number of processing threads they use (#1201)

  • All write_*() and format_*() functions gain quote and escape arguments, to explicitly control how fields are quoted and how double quotes are escaped. (#653, #759, #844, #993, #1018, #1083)

  • All write_*() functions gain a progress argument and display a progress bar when writing (#791).

  • write_excel_csv() now defaults to quote = "all" (#759)

  • write_tsv() now defaults to quote = "none" (#993)

  • read_table() now handles skipped lines with unpaired quotes properly (#1180)

Additional features and fixes

  • The BH package is no longer a dependency. The boost C++ headers in BH have thousands of files, so can take a long time to extract and compiling them takes a great deal of memory, which made readr difficult to compile on systems with limited memory (#1147).

  • readr now uses the tzdb package when parsing date-times (@DavisVaughan, r-lib/vroom#273)

  • Chunked readers now support files with more than INT_MAX (~ 2 Billion) number of lines (#1177)

  • Memory no longer inadvertently leaks when reading memory from R connections (#1161)

  • Invalid date formats no longer can potentially crash R (#1151)

  • col_factor() now throws a more informative error message if given non-character levels (#1140)

  • problems() now takes .Last.value as its default argument. This lets you run problems() without an argument to see the problems in the previously read dataset.

  • read_delim() fails when sample of parsing problems contains non-ASCII characters (@hidekoji, #1136)

  • read_log() gains a trim_ws argument (#738)

  • read_rds() and write_rds() gain a refhook argument, to pass functions that handle references objects (#1206)

  • read_rds() can now read .Rds files from URLs (#1186)

  • read_*() functions gain a show_col_types argument, if set to FALSE this turns off showing the column types unconditionally.

  • type_convert() now throws a warning if the input has no character columns (#1020)

  • write_csv() now errors if given a matrix column (#1171)

  • write_csv() now again is able to write data with duplicated column names (#1169)

  • write_file() now forces its argument before opening the output file (#1158)

readr 1.4.0

CRAN release: 2020-10-05

Breaking changes

  • write_*() functions first argument is now file instead of path, for consistency with the read_*() functions. path has been deprecated and will be removed in a future version of readr (#1110, @brianrice2)

  • write_*() functions now output any NaN values in the same way as NA values, controlled by the na= argument. (#1082).

New features

  • It is now possible to generate a column specification from any tibble (or data.frame) with as.col_spec() and convert any column specification to a short representation with as.character()

    s <- as.col_spec(iris)
    #> cols(
    #>   Sepal.Length = col_double(),
    #>   Sepal.Width = col_double(),
    #>   Petal.Length = col_double(),
    #>   Petal.Width = col_double(),
    #>   Species = col_factor(levels = c("setosa", "versicolor", "virginica"), ordered = FALSE, include_na = FALSE)
    #> )
    #> [1] "ddddf"
  • The cli package is now used for all messages.

  • The runtime performance for tables with an extreme number of columns is greatly improved (#825)

  • Compressed files are now detected by magic numbers rather than by the file extension (#1125)

  • A memory leak when reading files is now fixed (#1092)

  • write_*() functions gain a eol = argument to control the end of line character used (#857). This allows writing of CSV files with Windows newlines (CRLF) if desired.

  • The Rcpp dependency has been removed in favor of cpp11.

  • The build system has been greatly simplified so should work on more systems.

Additional features and fixes

readr 1.3.1

CRAN release: 2018-12-21

  • Column specifications are now coloured when printed. This makes it easy to see at a glance when a column is input as a different type then the rest. Colouring can be disabled by setting options(crayon.enabled = FALSE).

  • as.col_spec() can now use named character vectors, which makes read_csv("file.csv", col_types = c(xyz = "c")) equivalent to read_csv("file.csv", col_types = cols(xyz = col_character())

  • Fix skipping when single quotes are embedded in double quoted strings, and single quotes in skipped or commented lines (#944, #945).

  • Fix for compilation using custom architectures on macOS (#919)

  • Fix for valgrind errors (#941)

readr 1.3.0

CRAN release: 2018-12-11

Breaking Changes

Blank line skipping

readr’s blank line skipping has been modified to be more consistent and to avoid edge cases that affected the behavior in 1.2.0. The skip parameter now behaves more similar to how it worked previous to readr 1.2.0, but in addition the parameter skip_blank_rows can be used to control if fully blank lines are skipped. (#923)

tibble data frame subclass

readr 1.3.0 returns results with a spec_tbl_df subclass. This differs from a regular tibble only that the spec attribute (which holds the column specification) is lost as soon as the object is subset (and a normal tbl_df object is returned).

Historically tbl_df’s lost their attributes once they were subset. However recent versions of tibble retain the attributes when subetting, so the spec_tbl_df subclass is needed to ensure the previous behavior.

This should only break compatibility if you are explicitly checking the class of the returned object. A way to get backwards compatible behavior is to call subset with no arguments on your object, e.g. x[].


  • hms objects with NA values are now written without whitespace padding (#930).
  • read_*() functions now return spec_tbl_df objects, which differ from regular tbl_df objects only in that the spec attribute is removed (and they are demoted to regular tbl_df objects) as soon as they are subset (#934).
  • write_csv2() now properly respects the na argument (#928)
  • Fixes compilation with multiple architectures on linux (#922).
  • Fixes compilation with R < 3.3.0

readr 1.2.1

CRAN release: 2018-11-22

This release skips the clipboard tests on CRAN servers

readr 1.2.0

CRAN release: 2018-11-22

Breaking Changes

Integer column guessing

readr functions no longer guess columns are of type integer, instead these columns are guessed as numeric. Because R uses 32 bit integers and 64 bit doubles all integers can be stored in doubles, guaranteeing no loss of information. This change was made to remove errors when numeric columns were incorrectly guessed as integers. If you know a certain column is an integer and would like to read them as such you can do so by specifying the column type explicitly with the col_types argument.

Blank line skipping

readr now always skips blank lines automatically when parsing, which may change the number of lines you need to pass to the skip parameter. For instance if your file had a one blank line then two more lines you want to skip previously you would pass skip = 3, now you only need to pass skip = 2.

New features

Melt functions

There is now a family of melt_*() functions in readr. These functions store data in ‘long’ or ‘melted’ form, where each row corresponds to a single value in the dataset. This form is useful when your data is ragged and not rectangular.

data <-"a,b,c

#> # A tibble: 9 x 4
#>     row   col data_type value
#>   <dbl> <dbl> <chr>     <chr>
#> 1     1     1 character a    
#> 2     1     2 character b    
#> 3     1     3 character c    
#> 4     2     1 integer   1    
#> 5     2     2 integer   2    
#> 6     3     1 character w    
#> 7     3     2 character x    
#> 8     3     3 character y    
#> 9     3     4 character z

Thanks to Duncan Garmonsway (@nacnudus) for great work on the idea an implementation of the melt_*() functions!

Connection improvements

readr 1.2.0 changes how R connections are parsed by readr. In previous versions of readr the connections were read into an in-memory raw vector, then passed to the readr functions. This made reading connections from small to medium datasets fast, but also meant that the dataset had to fit into memory at least twice (once for the raw data, once for the parsed data). It also meant that reading could not begin until the full vector was read through the connection.

Now we instead write the connection to a temporary file (in the R temporary directory), than parse that temporary file. This means connections may take a little longer to be read, but also means they will no longer need to fit into memory. It also allows the use of the chunked readers to process the data in parts.

Future improvements to readr would allow it to parse data from connections in a streaming fashion, which would avoid many of the drawbacks of either method.

Additional new features

Bug Fixes

  • standardise_path() now uses a case-insensitive comparison for the file extensions (#794).
  • parse_guess() now guesses logical types when given (lowercase) ‘true’ and ‘false’ inputs (#818).
  • read_*() now do not print a progress bar when running inside a RStudio notebook chunk (#793)
  • read_table2() now skips comments anywhere in the file (#908).
  • parse_factor() now handles the case of empty strings separately, so you can have a factor level that is an empty string (#864).
  • read_delim() now correctly reads quoted headers with embedded newlines (#784).
  • fwf_positions() now always returns col_names as a character (#797).
  • format_*() now explicitly marks it’s output encoding as UTF-8 (#697).
  • read_delim() now ignores whitespace between the delimiter and quoted fields (#668).
  • read_table2() now properly ignores blank lines at the end of a file like read_table() and read_delim() (#657).
  • read_delim(), read_table() and read_table() now skip blank lines at the start of a file (#680, #747).
  • guess_parser() now guesses a logical type for columns which are all missing. This is useful when binding multiple files together where some files have missing columns. (#662).
  • Column guessing will now never guess an integer type. This avoids issues where double columns are incorrectly guessed as integers if they have only integer values in the first 1000 (#645, #652).
  • read_*() now converts string files to UTF-8 before parsing, which is convenient for non-UTF-8 platforms in most cases (#730, @yutannihilation).
  • write_csv() writes integers up to 10^15 without scientific notation (#765, @zeehio)
  • read_*() no longer throws a “length of NULL cannot be changed” warning when trying to resize a skipped column (#750, #833).
  • read_*() now handles non-ASCII paths properly with R >=3.5.0 on Windows (#838, @yutannihilation).
  • read*()’s trim_ws parameter now trims both spaces and tabs (#767)

readr 1.1.1

CRAN release: 2017-05-16

  • Point release for test compatibility with tibble v1.3.1.
  • Fixed undefined behavior in localtime.c when using locale(tz = "") after loading a timezone due to incomplete reinitialization of the global locale.

readr 1.1.0

CRAN release: 2017-03-22

New features

Parser improvements

  • parse_factor() gains a include_na argument, to include NA in the factor levels (#541).
  • parse_factor() will now can accept levels = NULL, which allows one to generate factor levels based on the data (like stringsAsFactors = TRUE) (#497).
  • parse_numeric() now returns the full string if it contains no numbers (#548).
  • parse_time() now correctly handles 12 AM/PM (#579).
  • problems() now returns the file path in additional to the location of the error in the file (#581).
  • read_csv2() gives a message if it updates the default locale (#443, @krlmlr).
  • read_delim() now signals an error if given an empty delimiter (#557).
  • write_*() functions witting whole number doubles are no longer written with a trailing .0 (#526).

Whitespace / fixed width improvements

Writing to connections

  • write_*() functions now support writing to binary connections. In addition output filenames with .gz, .bz2 or .xz will automatically open the appropriate connection and to write the compressed file. (#348)
  • write_lines() now accepts a list of raw vectors (#542).

Miscellaneous features

  • col_euro_double(), parse_euro_double(), col_numeric(), and parse_numeric() have been removed.
  • guess_encoding() returns a tibble, and works better with lists of raw vectors (as returned by read_lines_raw()).
  • ListCallback R6 Class to provide a more flexible return type for callback functions (#568, @mmuurr)
  • tibble::as.tibble() now used to construct tibbles (#538).
  • read_csv, read_csv2, and read_tsv gain a quote argument, (#631, @noamross)


  • parse_factor() now converts data to UTF-8 based on the supplied locale (#615).
  • read_*() functions with the guess_max argument now throw errors on inappropriate inputs (#588).
  • read_*_chunked() functions now properly end the stream if FALSE is returned from the callback.
  • read_delim() and read_fwf() when columns are skipped using col_types now report the correct column name (#573, @cb4ds).
  • spec() declarations that are long now print properly (#597).
  • read_table() does not print spec when col_types is not NULL (#630, @jrnold).
  • guess_encoding() now returns a tibble for all ASCII input as well (#641).

readr 1.0.0

CRAN release: 2016-08-03

Column guessing

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 column 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).

Column parsing

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_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.

New readers and writers

  • 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).

Minor features and bug fixes

  • 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_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

CRAN release: 2015-10-22

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

readr 0.2.1

CRAN release: 2015-10-21

  • Fixes so that readr works on Solaris.

readr 0.2.0

CRAN release: 2015-10-19


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:

See vignette("locales") for more details.

File parsing improvements

  • 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).

Column parsing improvements

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.

Minor improvements and bug fixes

  • 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 invisibly 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).