windows/sas.get: Convert a SAS Dataset to an S Data Frame

In harrelfe/Hmisc: Harrell Miscellaneous

Convert a SAS Dataset to an S Data Frame

Description

Converts a SAS dataset into an S data frame. You may choose to extract only a subset of variables or a subset of observations in the SAS dataset. The function will automatically convert

PROC FORMAT

-coded variables to factor objects. The original SAS codes are stored in an attribute called sas.codes and these may be added back to the levels of a factor variable using the code.levels function. Information about special missing values may be captured in an attribute of each variable having special missing values. This attribute is called special.miss, and such variables are given class special.miss. There are print, [], format, and is.special.miss methods for such variables. date, time, and date-time variables use respectively Dates, DateTimeClasses, and chron variables. If using S-Plus 5 or 6 or later, the timeDate function is used instead. If a date variable represents a partial date (0.5 added if month missing, 0.25 added if day missing, 0.75 if both), an attribute partial.date is added to the variable, and the variable also becomes a class imputed variable. The describe function uses information about partial dates and special missing values. There is an option to automatically PKUNZIP compressed SAS datasets.

sas.get works by composing and running a SAS job that creates various ASCII files that are read and analyzed by sas.get. You can also run the SAS sas_get macro, which writes the ASCII files for downloading, in a separate step or on another computer, and then tell sas.get (through the sasout argument) to access these files instead of running SAS.

Usage

sas.get(libraryName, member, variables=character(0), ifs=character(0),
     format.library=libraryName, id,
     dates.=c("sas","yymmdd","yearfrac","yearfrac2"),
     keep.log=TRUE, log.file="_temp_.log", macro=sas.get.macro,
     data.frame.out=existsFunction("data.frame"), clean.up=FALSE, quiet=FALSE,
     temp=tempfile("SaS"), formats=TRUE, 
     recode=formats, special.miss=FALSE, sasprog="sas",
     as.is=.5, check.unique.id=TRUE, force.single=FALSE, pos,
     uncompress=FALSE, defaultencoding="latin1", var.case="lower")

is.special.miss(x, code)

## S3 method for class 'special.miss'
x[..., drop=FALSE]

## S3 method for class 'special.miss'
print(x, ...)

## S3 method for class 'special.miss'
format(x, ...)

sas.codes(object)

code.levels(object)

Arguments

PROC FORMAT VALUE

LENGTH

LENGTH

Details

If you specify special.miss = TRUE and there are no special missing values in the data SAS dataset, the SAS step will bomb.

For variables having a

PROC FORMAT VALUE

format with some of the levels undefined, sas.get will interpret those values as NA if you are using recode.

If you leave the sasprog argument at its default value of ‘⁠sas⁠’, be sure that the SAS executable is in the ‘PATH’ specified in your ‘autoexec.bat’ file. Also make sure that you invoke S so that your current project directory is known to be the current working directory. This is best done by creating a shortcut in Windows95, for which the command to execute will be something like drive:\spluswin\cmd\splus.exe HOME=. and the program is flagged to start in ‘drive:\myproject’ for example. In this way, you will be able to examine the SAS log file easily since it will be placed in ‘drive:\myproject’ by default.

SAS will create ‘⁠SASWORK⁠’ and ‘⁠SASUSER⁠’ directories in what it thinks are the current working directories. To specify where SAS should put these instead, edit the ‘config.sas’ file or specify a sasprog argument of the following form: sasprog="\sas\sas.exe -saswork c:\saswork -sasuser c:\sasuser".

When sas.get needs to run SAS it is run in iconized form.

The SAS macro ‘sas_get’ uses record lengths of up to 4096 in two places. If you are exporting records that are very long (because of a large number of variables and/or long character variables), you may want to edit these ‘⁠LRECL⁠’s to quadruple them, for example.

Value

A data frame resembling the SAS dataset. If id was specified, that column of the data frame will be used as the row names of the data frame. Each variable in the data frame or vector in the list will have the attributes label and format containing SAS labels and formats. Underscores in formats are converted to periods. Formats for character variables have ‘⁠\$⁠’ placed in front of their names. If formats is TRUE and there are any appropriate format definitions in format.library, the returned object will have attribute formats containing lists named the same as the format names (with periods substituted for underscores and character formats prefixed by ‘⁠\$⁠’). Each of these lists has a vector called values and one called labels with the

PROC FORMAT; VALUE

... definitions.

Side Effects

if a SAS error occurs the SAS log file will be printed under the control of the pager function.

BACKGROUND

The references cited below explain the structure of SAS datasets and how they are stored. See SAS Language for a discussion of the

subsetting if

statement.

Note

If sasout is not given, you must be able to run SAS on your system.

If you are reading time or date-time variables, you will need to execute the command library(chron) to print those variables or the data frame.

Author(s)

Terry Therneau, Mayo Clinic
Frank Harrell, Vanderbilt University
Bill Dunlap, University of Washington and Insightful Corp.
Michael W. Kattan, Cleveland Clinic Foundation
Reinhold Koch (encoding)

References

SAS Institute Inc. (1990). SAS Language: Reference, Version 6. First Edition. SAS Institute Inc., Cary, North Carolina.

SAS Institute Inc. (1988). SAS Technical Report P-176, Using the SAS System, Release 6.03, under UNIX Operating Systems and Derivatives. SAS Institute Inc., Cary, North Carolina.

SAS Institute Inc. (1985). SAS Introductory Guide. Third Edition. SAS Institute Inc., Cary, North Carolina.

See Also

data.frame, describe, label, upData

Examples

## Not run: 
mice <- sas.get("saslib", mem="mice", var=c("dose", "strain", "ld50"))
plot(mice$dose, mice$ld50)

nude.mice <- sas.get(lib=unix("echo $HOME/saslib"), mem="mice",
	ifs="if strain='nude'")

nude.mice.dl <- sas.get(lib=unix("echo $HOME/saslib"), mem="mice",
	var=c("dose", "ld50"), ifs="if strain='nude'")

# Get a dataset from current directory, recode PROC FORMAT; VALUE \dots 
# variables into factors with labels of the form "good(1)" "better(2)",
# get special missing values, recode missing codes .D and .R into new
# factor levels "Don't know" and "Refused to answer" for variable q1
d <- sas.get(mem="mydata", recode=2, special.miss=TRUE)
attach(d)
nl <- length(levels(q1))
lev <- c(levels(q1), "Don't know", "Refused")
q1.new <- as.integer(q1)
q1.new[is.special.miss(q1,"D")] <- nl+1
q1.new[is.special.miss(q1,"R")] <- nl+2
q1.new <- factor(q1.new, 1:(nl+2), lev)
# Note: would like to use factor() in place of as.integer ... but
# factor in this case adds "NA" as a category level

d <- sas.get(mem="mydata")
sas.codes(d$x)    # for PROC FORMATted variables returns original data codes
d$x <- code.levels(d$x)   # or attach(d); x <- code.levels(x)
# This makes levels such as "good" "better" "best" into e.g.
# "1:good" "2:better" "3:best", if the original SAS values were 1,2,3

# For the following example, suppose that SAS is run on a
# different machine from the one on which S is run.
# The sas_get macro is used to create files needed by
# sas.get.  To make a text file containing the sas_get macro
# run the following S command, for example:
#   cat(sas.get.macro, file='/sasmacro/sas_get.sas', sep='\n')

# Here is the SAS job.  This job assumes that you put
# sas_get.sas in an autocall macro library.


#  libname db '/my/sasdata/area';
#  %sas_get(db.mydata, dict, data, formats, specmiss,
#           formats=1, specmiss=1)


# Substitute whatever file names you may want.
# Next the 4 files are moved to the S machine (using
# ASCII file transfer mode) and the following S
# program is run:


mydata <- sas.get(sasout=c('dict','data','formats','specmiss'),
                  id='idvar')


# If PKZIP is run after %sas_get, e.g. "PKZIP port dict data formats"
# (assuming that specmiss was not used here), use


mydata <- sas.get(sasout='a:port', id='idvar')


# which will run PKUNZIP port to unzip a:port.zip, creating the
# dict, data, and formats files which are generated (and later
# deleted) by sas.get


# Retrieve the same variables from another dataset (or an update of
# the original dataset)
mydata2 <- sas.get('mydata2', var=names(mydata))
# This only works if none of the original SAS variable names contained _

# Code from Don MacQueen to generate SAS dataset to test import of
# date, time, date-time variables
# data ssd.test;
#     d1='3mar2002'd ;
#     dt1='3mar2002 9:31:02'dt;
#     t1='11:13:45't;
#     output;
#
#     d1='3jun2002'd ;
#     dt1='3jun2002 9:42:07'dt;
#     t1='11:14:13't;
#     output;
#     format d1 mmddyy10. dt1 datetime. t1 time.;
# run;

## End(Not run)