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).
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_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_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)
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
haven can read and write non-ASCII paths in R 3.5 (#371).
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).
Update to latest readstat.
Update to latest readstat. Includes:
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.”
Update to latest readstat. Includes:
All write methds now check that you’re trying to write a data frame (#287).
write_* functions turn ordered factors into labelled vectors (#285)
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.
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
zap_missing() just replaces user-defined missing vlaues with
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
user_na = TRUE (normally user-defined missings are converted to NA).
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.
vignette("semantics", package = "haven") discusses the semantics of missing values and labelling in SAS, SPSS, and Stata, and how they are translated into R.
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_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
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).
write_sas() allows you to write data frames out to
sas7bdat files. This is still somewhat experimental.
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_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.
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