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 vlaues 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