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).
read_sav() once again correctly returns system defined missings as
NA (rather than
write_sav() checks that factors don’t have levels with >120 characters (#262)
as_factor() with forcats package (#256)
write_dta() no longer checks that all value labels are at most 32 characters (since this is not a restriction of dta files) (#239).
Update to latest readstat. Includes:
SPSS: fixes for 0 byte strings (#245)
Add support for reading and writing of SPSS’s display widths (@ecortens).
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.
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).
as_factor() no longer drops the
label attribute (variable label) when used (#177, @itsdalmo).
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.
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
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).
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() writes hms variables to SPSS time variables, and the “measure” type is set for each variable (#133).
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.
Byte variables are now correctly read into integers (not strings, #45), and missing values are captured correctly (#43).
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