Updated to ReadStat 1.1.9.
The experimental write_sas() function has been
deprecated (#224). The sas7bdat file format is complex and undocumented,
and as such writing SAS files is not officially supported by ReadStat.
write_xpt() should be used instead - it produces files in
the SAS transport format, which has limitations but will be reliably
read by SAS.
write_*() functions gain a new
adjust_tz argument to allow more control over time zone
conversion for date-time variables (#702). Thanks to @jmobrien for the detailed
issue and feedback.
Stata, SPSS and SAS do not have a concept of time zone. Since haven
2.4.0 date-time values in non-UTC time zones are implicitly converted
when writing to ensure the time displayed in Stata/SPSS/SAS will match
the time displayed to the user in R (see #555). This is the behaviour
when adjust_tz = TRUE (the default). Although this is in
line with general user expectations it can cause issues when the time
zone is important, for e.g. when looking at differences between time
points, since the underlying numeric data is changed to preserve the
displayed time. Use adjust_tz = FALSE to write the time as
the corresponding UTC value, which will appear different to the user but
preserves the underlying numeric data.
write_*() functions previously returned the data
frame with minor alterations made to date-time variables. These
functions now invisibly return the original input data frame unchanged
(@jmobrien,
#702).
Fix bug in string variable width calculation that treated
NA values as width
NA values are now treated as blanks for width
calculations (#699).All labelled() vectors now have left-aligned column
headers when printing in tibbles for better alignment with labels
(#676).
write_*() now accept functions as well as strings in
the .name_repair argument in line with the documentation.
Previously they only supported string values (#684).
write_sav() variable name validation no longer
treats all non-ASCII characters as invalid (#689).
All write_ functions can now write custom variable
widths by setting the width attribute (#650).
When writing files, the minimum width for character variables is now 1. This fixes issues with statistical software reading blank character variables with width 0 (#650).
write_dta() now uses strL when strings are too long
to be stored in an str# variable (#437). strL is used when strings are
longer than 2045 characters by default, which matches Stata’s behaviour,
but this can be reduced with the strl_threshold
argument.
write_xpt() can now write dataset labels with the
label argument, which defaults to the label
attribute of the input data frame, if present (#562).
write_sav() now checks for case-insensitive
duplicate variable names (@juansebastianl, #641) and verifies
that variable names are valid SPSS variables.
The compress argument for write_sav()
now supports all 3 SPSS compression modes specified as a character
string - “byte”, “none” and “zsav” (#614). TRUE and
FALSE can be used for backwards compatibility, and
correspond to the “zsav” and “none” options respectively.
write_sav() successfully writes user missing values
and ranges for labelled() integer vectors (#596).
POSIXct and POSIXlt values with no time component
(e.g. “2010-01-01”) were being converted to NA when
attempting to convert the output timezone to UTC. These now output
successfully (#634).
Fix bug in output timezone conversion that was causing variable labels and other variable attributes to disappear (#624).
Updated to ReadStat 1.1.8 RC.
labelled() vectors now throw a warning when
combining two vectors with conflicting labels (#667).
zap_labels() gains a user_na argument
to control whether user-defined missing values are converted to
NA or left as is (#638).
vctrs casting and coercion generics now do less work when working
with two identical labelled() vectors. This significantly
improves performance when working with labelled() vectors
in grouped data frames (#658).
Errors and warnings now use cli_abort() and
cli_warning() (#661).
R 3.4 is now the minimum supported version, in line with tidyverse policy.
cli >= 3.0.0 has been added to Imports to support new error messaging.
lifecycle has been added to Imports, and is now used to manage deprecations.
Updated to ReadStat 1.1.7 RC (#620).
read_dta() no longer crashes if it sees StrL
variables with missing values (@gorcha, #594, #600, #608).
write_dta() now correctly handles “labelled”-class
numeric (double) variables that don’t have value labels (@jmobrien, #606,
#609).
write_dta() now allows variable names up to 32
characters (@sbae,
#605).
Can now correctly combine labelled_spss() with
identical labels (@gorcha, #599).
labelled() with identical
labels.labelled_spss() gains full vctrs support thanks to
the hard work of @gorcha (#527, #534, #538, #557). This means
that they should now work seamlessly in dplyr 1.0.0, tidyr 1.0.0 and
other packages that use vctrs.
labelled() vectors are more permissive when
concatenating; output labels will be a combination of the left-hand and
the right-hand side, preferring values assigned to the left-hand side
(#543).
Date-times are no longer forced to UTC, but instead converted to the equivalent UTC (#555). This should ensure that you see the same date-time in R and in Stata/SPSS/SAS.
Updated to ReadStat 1.1.5. Most importantly this includes support for SAS binary compression.
as_factor(levels = "values") preserves values of
unlabelled elements (#570).
labelled_spss() is a little stricter: it prevents
na_range and na_value from containing missing
values, and ensures that na_range is in the correct order
(#574).
read_spss() now reads NA values and ranges of
character variables (#409).
write_dta() now correctly writes tagged NAs
(including tagged NAs in labels) (#583) and once again validates length
of variables names (#485).
write_*() now validate file and variable metadata
with ReadStat. This should prevent many invalid files from being written
(#408). Additionally, validation failures now provide more details about
the source of the problem (e.g. the column name of the problem)
(#463).
write_sav(compress = FALSE) now uses SPSS bytecode
compression instead of the rarely-used uncompressed mode.
compress = TRUE continues to use the newer (and not
universally supported, but more compact) zlib format (@oliverbock,
#544).
Add missing methods so median(),
quantile() and summary() work once more
(#520).
Add missing cast methods (#522).
labelled() gains the necessary support to work
seemlessly in dplyr 1.0.0, tidyr 1.0.0, and other packages that use
vctrs (@mikmart,
#496).
labelled() vectors now explicitly inherit from the
corresponding base types (e.g. integer, double, or character)
(#509).
ReadStat update, including read_sas() supports for
“any” encoding (#482), and fixes for compiler warnings.
Thanks to the hard work of @mikmart, all read_*()
functions gain three new arguments that allow you to read in only part
of a large file:
col_select: selects columns to read with a tidyselect
interface (#248).skip: skips rows before reading data (#370).n_max: limits the number of rows to read.This also brings with it a deprecation: cols_only in
read_sas() has been deprecated in favour of the new
col_select argument.
as_factor() allows non-unique labels when
levels = "label". This fixes a particularly annoying
printing bug (#424, @gergness)
read_sas() now supports (IS|E|B)8601(DT|DA|TM)
date/time formats (@mikmart).
All write_ functions gain a
.name_repair argument that controls what happens when the
input dataset has repeated column names (#436).
All write_ functions can now write labelled vectors
with NULL labels (#442).
write_dta() can now write dataset labels with the
label argument, which defaults to the label
attribute of the input data frame, if present (@gorcha, #449).
write_dta() works better with Stata 15, thanks to
updated ReadStat (#461)
labelled objects get pretty printing that shows the
labels and NA values when inside of a tbl_df. Turn this
behaviour off with behavior using
option(haven.show_pillar_labels = FALSE) (#340, @gergness).
labelled() and labelled_spss() now allow
NULL labels. This makes both classes more flexible,
allowing you to use them for their other attributes (#219).
labelled() tests that value labels are unique (@larmarange, #364)
as_factor():
labelled method for backward compatbility
(#414).data.frame method now correctly passes ...
along (#407, @zkamvar).write_dta() now checks that the labelled values are
integers, not the values themselves (#401).
Updated to latest ReadStat from @evanmiller:
read_por() can now read files from SPSS 25 (#412)read_por() now uses base-30 instead of base-10 for the
exponent (#413)read_sas() can read zero column file (#420)read_sav() reads long strings (#381)read_sav() has greater memory limit allowing it to read
more labels (#418)read_spss() reads long variable labels (#422)write_sav() no longer creates incorrect column names
when >10k columns (#410)write_sav() no longer crashes when writing long label
names (#395)labelled() and labelled_spss() now
produce objects with class “haven_labelled” and “haven_labelled_spss”.
Previously, the “labelled” class name clashed with the labelled class
defined by Hmisc (#329).
Unfortunately I couldn’t come up with a way to fix this problem except to change the class name; it seems reasonable that haven should be the one to change names given that Hmisc has been around much longer. This will require some changes to packages that use haven, but shouldn’t affect user code.
labelled() and labelled_spss() now
support adding the label attribute to the resulting object.
The label is a short, human-readable description of the
object, and is now also used when printing, and can be easily removed
using the new zap_label() function. (#362, @huftis)
Previously, the label attribute was supported both when
reading and writing SPSS files, but it was not possible to actually
create objects in R having the label attribute using the
constructors labelled() or
labelled_spss().
haven can read and write non-ASCII paths in R 3.5 (#371).
labelled_spss objects preserve their attributes when
subsetted (#360, @gergness).
read_sav() gains an encoding argument
to override the encoding stored in the file (#305).
read_sav() can now read .zsav files
(#338).
write_*() functions now invisibly return the input
data frame (as documented) (#349, @austensen).
write_dta() allows non-ASCII variable labels for
version 14 and above (#383). It also uses a less strict check for
integers so that a labelled double containing only integer values can
written (#343).
write_sav() produces .zsav files when
compress = TRUE (#338).
write_xpt() can now set the “member” name, which
defaults to the file name san extension (#328).
Update to latest readstat.
Fix for when as_factor() with option
levels="labels" is used on tagged NAs (#340, @gergness)
Update to latest readstat. Includes:
encoding now affects value labels (#325)read_por() and read_xpt() now correctly
preserve attributes if output needs to be reallocated (which is typical
behaviour) (#313)
read_sas() recognises date/times format with
trailing separator and width specifications (#324)
read_sas() gains a catalog_encoding
argument so you can independently specify encoding of data and catalog
(#312)
write_*() correctly measures lengths of non-ASCII
labels (#258): this fixes the cryptic error “A provided string value was
longer than the available storage size of the specified
column.”
write_dta() now checks for bad labels in all
columns, not just the first (#326).
write_sav() no longer fails on empty factors or
factors with an NA level (#301) and writes out more
metadata for labelled_spss vectors (#334).
Update to latest readstat. Includes:
Share as_factor() with forcats package
(#256)
read_sav() once again correctly returns system
defined missings as NA (rather than NaN)
(#223). read_sav() and write_sav() preserve
SPSS’s display widths (@ecortens).
read_sas() gains experimental cols_only
argument to only read in specified columns (#248).
tibbles are created with tibble::as_tibble(), rather
than by “hand” (#229).
write_sav() checks that factors don’t have levels
with >120 characters (#262)
write_dta() no longer checks that all value labels
are at most 32 characters (since this is not a restriction of dta files)
(#239).
All write methds now check that you’re trying to write a data frame (#287).
Add support for reading (read_xpt()) and writing
(write_xpt()) SAS transport files.
write_* functions turn ordered factors into labelled
vectors (#285)
The ReadStat library is stored in a subdirectory of
src (#209, @krlmlr).
Import tibble so that tibbles are printed consistently (#154, @krlmlr).
Update to latest ReadStat (#65). Includes:
Added support for reading and writing variable formats. Similarly
to to variable labels, formats are stored as an attribute on the vector.
Use zap_formats() if you want to remove these attributes.
(@gorcha, #119,
#123).
Added support for reading file “label” and “notes”. These are not currently printed, but are stored in the attributes if you need to access them (#186).
Added support for “tagged” missing values (in Stata these are called “extended” and in SAS these are called “special”) which carry an extra byte of information: a character label from “a” to “z”. The downside of this change is that all integer columns are now converted to doubles, to support the encoding of the tag in the payload of a NaN.
New labelled_spss() is a subclass of
labelled() that can model user missing values from SPSS.
These can either be a set of distinct values, or for numeric vectors, a
range. zap_labels() strips labels, and replaces
user-defined missing values with NA. New
zap_missing() just replaces user-defined missing values
with NA.
labelled_spss() is potentially dangerous to work with in
R because base functions don’t know about labelled_spss()
functions so will return the wrong result in the presence of
user-defined missing values. For this reason, they will only be created
by read_spss() when user_na = TRUE (normally
user-defined missings are converted to NA).
as_factor() no longer drops the label
attribute (variable label) when used (#177, @itsdalmo).
Using as_factor() with
levels = "default or levels = "both" preserves
unused labels (implicit missing) when converting (#172, @itsdalmo). Labels (and
the resulting factor levels) are always sorted by values.
as_factor() gains a new
levels = "default" mechanism. This uses the labels where
present, and otherwise uses the labels. This is now the default, as it
seems to map better to the semantics of labelled values in other
statistical packages (#81). You can also use
levels = "both" to combine the value and the label into a
single string (#82). It also gains a method for data frames, so you can
easily convert every labelled column to a factor in one function
call.
New vignette("semantics", package = "haven")
discusses the semantics of missing values and labelling in SAS, SPSS,
and Stata, and how they are translated into R.
Support for hms() has been moved into the hms
package (#162). Time varibles now have class
c("hms", "difftime") and a units attribute
with value “secs” (#162).
labelled() is less strict with its checks: you can
mix double and integer value and labels (#86, #110, @lionel-), and
is.labelled() is now exported (#124). Putting a labelled
vector in a data frame now generates the correct column name
(#193).
read_dta() now recognises “%d” and custom date types
(#80, #130). It also gains an encoding parameter which you can use to
override the default encoding. This is particularly useful for Stata 13
and below which did not store the encoding used in the file
(#163).
read_por() now actually works (#35).
read_sav() now correctly recognises EDATE and JDATE
formats as dates (#72). Variables with format DATE, ADATE, EDATE, JDATE
or SDATE are imported as Date variables instead of
POSIXct. You can now set user_na = TRUE to
preserve user defined missing values: they will be given class
labelled_spss.
read_dta(), read_sas(), and
read_sav() have a better test for missing string values
(#79). They can all read from connections and compressed files (@lionel-, #109)
read_sas() gains an encoding parameter to overide
the encoding stored in the file if it is incorrect (#176). It gets
better argument names (#214).
Added type_sum() method for labelled objects so they
print nicely in tibbles.
write_dta() now verifies that variable names are
valid Stata variables (#132), and throws an error if you attempt to save
a labelled vector that is not an integer (#144). You can choose which
version of Stata’s file format to output (#217).
New write_sas() allows you to write data frames out
to sas7bdat files. This is still somewhat
experimental.
write_sav() writes hms variables to SPSS time
variables, and the “measure” type is set for each variable
(#133).
write_dta() and write_sav() support
writing date and date/times (#25, #139, #145). Labelled values are
always converted to UTF-8 before being written out (#87). Infinite
values are now converted to missing values since SPSS and Stata don’t
support them (#149). Both use a better test for missing values
(#70).
zap_labels() has been completely overhauled. It now
works (@markriseley, #69), and only drops label
attributes; it no longer replaces labelled values with NAs.
It also gains a data frame method that zaps the labels from every
column.
print.labelled() and
print.labelled_spss() now display the type.
fixed a bug in as_factor.labelled, which generated
zap_labels() now leaves unlabelled vectors
unchanged, making it easier to apply to all columns.
write_dta() and write_sav() take more
care to always write output as UTF-8 (#36)
write_dta() and write_sav() won’t crash
if you give them invalid paths, and you can now use ~ to
refer to your home directory (#37).
Byte variables are now correctly read into integers (not strings, #45), and missing values are captured correctly (#43).
Added read_stata() as alias to
read_dta() (#52).
read_spss() uses extension to automatically choose
between read_sav() and read_por()
(#53)
Updates from ReadStat. Including fixes for various parsing bugs, more encodings, and better support for large files.
hms objects deal better with missings when printing.
Fixed bug causing labels for numeric variables to be read in as
integers and associated error:
Error: `x` and `labels` must be same type
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