Import foreign statistical formats into R via the embedded 'ReadStat' C library ( https://github.com/WizardMac/ReadStat).
Haven allows you to load foreign data formats (SAS, SPSS and Stata) in to R by wrapping the fantastic ReadStat C library written by Evan Miller. Haven offers similar functionality to the base foreign package but:
It reads SPSS files (
.por), reads Stata 13 and 14 files
(foreign only works up to Stata 12), and SAS's proprietary binary format
(SAS7BDAT + SAS7BCAT). It does not support many of the now more exotic
formats supported by foreign.
Can also write SPSS, Stata, and SAS files.
Date times are converted to corresponding R classes and labelled vectors are
returned as a new
labelled class. You can easily coerce to factors or
replace labelled values with missings as appropriate. All functions return
Uses underscores instead of dots ;)
Haven is still a work in progress so please file an issue if it fails to correctly load a file that you're interested in.
# Install the released version from CRAN:install.packages("haven")# Install the cutting edge development version from GitHub:# install.packages("devtools")devtools::install_github("hadley/haven")
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.
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
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
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
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_sav() have a better test for missing
string values (#79). They can all read from connections and compressed files
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
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
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 's and wrong
labels for integer labels.
zap_labels() now leaves unlabelled vectors unchanged, making it easier
to apply to all columns.
write_sav() take more care to always write output as
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).
read_stata() as alias to
read_spss() uses extension to automatically choose between
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