Saturday, August 8, 2009

Example 7.9: Get data from SAS into R

Some people use both SAS and R in their daily work. They might be more familiar with SAS as a tool for manipulating data and R preferable for plotting purposes. While our goal in the book is to enable people to avoid having to switch back and forth, the following example shows how to move data from SAS into R. Our use of Stata format as an interchange mechanism is perhaps unorthodox, but eminently workable. Other file formats (see section 1.2.2, creating files for use by other packages) can also be specified.

Suppose we wanted to plot CESD over time for each individual. While we show how to do this sort of thing in SAS (see section 5.6.2), it’s hard to do without SAS version 9.2. Instead, we recall it’s easy using the lattice library in R (see section 5.2.2). But we need a ”long” data set with a row for each time point. This is the sort of data management one might prefer to do in SAS.


First, we read the data from a data set stored in SAS format. Then we use proc transpose (section 1.5.3) to get the data into the required shape. Finally, we save the ”long” data set in Stata format (1.2.2).

libname k "c:\book";

proc transpose out=ds1;
by id;
var cesd1 cesd2 cesd3 cesd4;

proc print data = ds1 (obs = 5); run;


1 1 CESD1 1 cesd 7
2 1 CESD2 2 cesd .
3 1 CESD3 3 cesd 8
4 1 CESD4 4 cesd 5
5 2 CESD1 1 cesd 11

proc export data=ds1 (rename=(_label_=timec1))
outfile = "c:\book\helpfromsas.dta" dbms=dta;

In R, we first read the file from Stata format (1.1.5), then attach() it (section 1.3.1) for ease of typing. Then we check the first few lines of data using the head() function (section 1.13.1). Noting that the time variable we brought from SAS is a character string, we convert it to a numeric variable using as.numeric() (section 1.4.2) and substr() (section 1.4.3). After loading the lattice library, we display the series for each subject. In this, we use the syntax for subsetting observations (1.5.1) to keep only the first 20 observations and the as.factor() function (1.4.10) to improve the labels in the output.

> library(foreign)
> xsas <- read.dta("C:\\book\\helpfromsas.dta")
> head(xsas)

id _name_ timec1 col1
1 1 CESD1 1 cesd 7
2 1 CESD2 2 cesd NA
3 1 CESD3 3 cesd 8
4 1 CESD4 4 cesd 5
5 2 CESD1 1 cesd 11
6 2 CESD2 2 cesd NA

> attach(xsas)
> time <- as.numeric(substr(timec1, 1, 1))
> library(lattice)
> xyplot(col1[id < 21] ~ time[id < 21]|as.factor(id[id < 21]))

In the next entry, we'll reverse this process to manipulate data in R and produce the plot in SAS.


Unknown said...

I believe there might be an easier way if you use library(sas7bdat.parso) and fread(). Have you considered this?

Ken Kleinman said...

I haven't tried that one. My go-to library for reading SAS data sets into R these days is haven, which was written by Hadley Wickham.

Anonymous said...

You should also try the haven library in R, it reads sas7bdat files directly and is works very well.

Randy Sherrod

Unknown said...

I agree, Haven is better. Doesn't work on compressed SAS files though.

Taylor G. White said...

Haven does handle compressed files now.