I’m pleased to announce the release of haven. Haven is designed to faciliate the transfer of data between R and SAS, SPSS, and Stata. It makes it easy to read SAS, SPSS, and Stata file formats in to R data frames, and makes it easy to save your R data frames in to SAS, SPSS, and Stata if you need to collaborate with others using closed source statistical software. Install haven by running:
haven 1.0.0 is a major release, and indicates that haven is now largely feature complete and has been tested on many real world datasets. There are four major changes in this version of haven:
Improvements to the underlying ReadStat library
Better handling of “special” missing values
Improved date/time support
Support for other file metadata.
There were also a whole bunch of other minor improvements and bug fixes: you can see the complete list in the release notes.
Can read binary/Ross compressed SAS files.
Support for reading and writing Stata 14 data files.
write_sas() allows you to write data frames out to
sas7bdat files. This is still somewhat experimental.
read_por() now actually works.
Many other bug fixes and minor improvements.
haven 1.0.0 includes comprehensive support for the “special” types of missing values found in SAS, SPSS, and Stata. All three tools provide a global “system missing value”, displayed as
.. This is roughly equivalent to R’s
NA, although neither Stata nor SAS propagate missingness in numeric comparisons (SAS treats the missing value as the smallest possible number and Stata treats it as the largest possible number).
Each tool also provides a mechanism for recording multiple types of missingness:
Stata has “extended” missing values,
SAS has “special” missing values,
SPSS has per-column “user” missing values. Each column can declare up to three distinct values or a range of values (plus one distinct value) that should be treated as missing.
Stata and SAS only support tagged missing values for numeric columns. SPSS supports up to three distinct values for character columns. Generally, operations involving a user-missing type return a system missing value.
Haven models these missing values in two different ways:
For SAS and Stata, haven provides
tagged_na() which extend R’s regular
NA to add a single character label.
For SPSS, haven provides
labelled_spss() that also models user defined values and ranges.
zap_missing() if you just want to convert to R’s regular
You can get more details in the semantics vignette.
Support for date/times has substantially improved:
read_dta() now recognises “%d” and custom date types.
read_sav() now correctly recognises EDATE and JDATE formats as dates. Variables with format DATE, ADATE, EDATE, JDATE or SDATE are imported as
Date variables instead of
write_sav() support writing date/times.
hms() has been moved into the hms package. Time varibles now have class
c("hms", "difftime") and a
unitsattribute with value “secs”.
Haven is slowly adding support for other types of metadata:
Variable formats can be read and written. Similarly to to variable labels, formats are stored as an attribute on the vector. Use
zap_formats() if you want to remove these attributes.
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.
Austin Chia, Founder of Any Instructor, describes his transition from studying biology to becoming a healthcare data analyst.
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