Skip to content

haven 1.0.0
Compare
Choose a tag to compare
@hadley hadley released this 23 Sep 21:36
v1.0.0
0ad8f56

  • 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:

    • Support for binary (aka Ross) compression for SAS (#31).
    • Support extended ASCII encoding for Stata (#71).
    • Support for Stata 14 files (#75, #212).
    • Support for SPSS value labels with more than 8 characters (#157).
    • More likely to get an error when attempting to create an invalid
      output file (#171).

  • 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.

Assets 2
Loading