Releases: tidyverse/readr
readr v1.3.1
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[]
.
Bugfixes
hms
objects with NA values are now written without whitespace padding (#930).read_*()
functions now returnspec_tbl_df
objects, which differ from
regulartbl_df
objects only in that thespec
attribute is removed (and
they are demoted to regulartbl_df
objects) as soon as they are subset
(#934).write_csv2()
now properly respects thena
argument (#928)- Fixes compilation with multiple architectures on linux (#922).
- Fixes compilation with R < 3.3.0
readr 1.1.1
- 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 re-initialization of the global locale.
readr 1.1.0
readr 1.1.0
This release contains mainly bug fixes and feature improvements suggested by the community. A couple of more significant features are connection support for the write_*()
functions and parse_factor(levels = NULL)
.
Connection support for write_*()
functions allow one to write directly to compressed formats such as .gz
, bz2
or .xz
and readr will automatically open the appropriate connection if a filename with one of the those suffixes is supplied as an argument.
parse_factor(levels = NULL)
, will produce a factor column based on the levels in the data, which mimics parsing of factors in base R.
New features
Parser improvements
parse_factor()
gains ainclude_na
argument, to includeNA
in the factor levels (#541).parse_factor()
will now can acceptlevels = 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
fwf_cols()
allows for specifying thecol_positions
argument of
read_fwf()
with named arguments of either column positions or widths
(#616, @jrnold).fwf_empty()
gains ann
argument to control how many lines are read for whitespace to determine column structure (#518, @yeedle).read_fwf()
gives error message if specifications have overlapping columns (#534, @gergness)read_table()
can now handlepipe()
connections (#552).read_table()
can now handle files with many lines of leading comments (#563).read_table2()
which allows any number of whitespace characters as delimiters, a more exact replacement forutils::read.table()
(#608).
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()
, andparse_numeric()
have been removed.guess_encoding()
returns a tibble, and works better with lists of raw vectors (as returned byread_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
, andread_tsv
gain aquote
argument, (#631, @noamross)
Bugfixes
parse_factor()
now converts data to UTF-8 based on the supplied locale (#615).read_*()
functions with theguess_max
argument now throw errors on inappropriate inputs (#588).read_*_chunked()
functions now properly end the stream ifFALSE
is returned from the callback.read_delim()
andread_fwf()
when columns are skipped usingcol_types
now report the correct column name (#573, @cb4ds).spec()
declarations that are long now print properly (#597).read_table()
does not printspec
whencol_types
is notNULL
(#630, @jrnold).guess_encoding()
now returns a tibble for all ASCII input as well (#641).
readr 1.0.0
readr 1.0.0
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()
:
spec(challenge)
#> 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 thelocale()
.
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 explicitcol_names
(settingskip = 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_time()
returnshms
objects rather than a customtime
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()
andparse_datetime()
gain anna
argument to match all other parsers (#413).- If the format argument is omitted
parse_date()
orparse_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")
returns2001-01-01
.
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
.
New readers and writers
read_file_raw()
reads a complete file into a single raw vector (#451).read_*()
functions gain aquoted_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 inreadr.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 atrim_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 inread.fwf()
(#380, @leeper).- You can now read fixed width files with ragged final columns, by setting
the final end position infwf_positions()
or final width infwf_widths()
toNA
(#353, @ghaarsma).fwf_empty()
does this automatically. read_fwf()
andfwf_empty()
can now skip commented lines by setting a
comment
argument (#334).
read_lines()
ignores embedded null's in strings (#338) and gains ana
argument (#479).readr_example()
makes it easy to access example files bundled with readr.type_convert()
now accepts onlyNULL
or acols
specification for
col_types
(#369).write_delim()
andwrite_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
Internationalisation
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 alocale
argument.locale()
controls all the input settings that vary from place-to-place.col_euro_double()
andparse_euro_double()
have been deprecated.
Use thedecimal_mark
parameter tolocale()
instead.- The default encoding is now UTF-8. To load files that are not
in UTF-8, set theencoding
parameter of thelocale()
(#40).
Newguess_encoding()
function uses stringi to help you figure out the
encoding of a file. parse_datetime()
andparse_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
explicittz
parameter.
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 incols()
(#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 toTRUE
(#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 tona = 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()
andparse_double()
no longer silently ignore trailing
letters after the number (#221).- New
parse_time()
andcol_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()
andparse_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. Useparse_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()
andcol_guess()
to explicitly guess column type. - Bumped up row inspection for column typing guessing from 100 to 1000.
- The heuristics for guessing
col_integer()
andcol_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
orn_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()
andformat_delim()
make it easy to render a csv or
delimited file into a string.fwf_empty()
now works correctly whencol_names
supplied (#186, #222).parse_*()
gains ana
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 iscol_types
specifies a non-existent column (#145, @alyst).read_*()
can read from a remote gz compressed file (#163).read_delim()
defaults toescape_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()
gainsNA
andtrim_ws
arguments, and removes missing
values before determining column types.write_csv()
,write_delim()
, andwrite_rds()
all invisably return their
input so you can use them in a pipe (#290).write_delim()
generaliseswrite_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).
- Quotes are only used when they're needed (#116): when the string contains
write_rds()
andread_rds()
wrap aroundreadRDS()
andsaveRDS()
,
defaulting to no compression (#140, @NicolasCOUTIN).
readr 0.1.0
Initial release