Haven makes it easy to read data from SAS, SPSS and Stata. Haven has the same goal as the foreign package, but it:
(Haven also has experimental support for writing SPSS and Stata data. This still has some rough edges but please try it out and report any problems that you find.)
Haven is a binding to the ReadStat C library by Evan Miller. Haven wouldn’t be possible without his hard work - thanks Evan! I’d also like to thank Matt Shotwell who spend a lot of time reverse engineering the SAS binary data format, and Dennis Fisher who tested the SAS code with thousands of SAS files.
Using haven is easy:
Then pick the appropriate read function:
These only need the name of the path. (
read_sas() optionally also takes the path to a catolog file.)
All functions return a data frame:
The output also has class
tbl_df which will improve the default print method (to only show the first ten rows and the variables that fit on one screen) if you have dplyr loaded. If you don’t use dplyr, it has no effect.
Variable labels are attached as an attribute to each variable. These are not printed (because they tend to be long), but if you have a preview version of RStudio, you’ll see them in the revamped viewer pane.
Missing values in numeric variables should be seemlessly converted. Missing values in character variables are converted to the empty string,
"": if you want to convert them to missing values, use
Dates are converted in to
Dates, and datetimes to
POSIXcts. Time variables are read into a new class called
hms which represents an offset in seconds from midnight. It has
format() methods to nicely display times, but otherwise behaves like an integer vector.
Variables with labelled values are turned into a new
labelled class, as described next.
SAS, Stata and SPSS all have the notion of a “labelled” variable. These are similar to factors, but:
Factors, by contrast, are always integers and every integer value must be associated with a label.
Haven provides a
labelled class to model these objects. It doesn’t implement any common methods, but instead focusses of ways to turn a labelled variable into standard R variable:
as_factor(): turns labelled integers into factors. Any values that don’t have a label associated with them will become a missing value. (NB: there’s no way to make
as.factor() work with labelled variables, so you’ll need to use this new function.)
zap_labels(): turns any labelled values into missing values. This deals with the common pattern where you have a continuous variable that has missing values indiciated by sentinel values.
If you have a use case that’s not covered by these function, please let me know.