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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 (.5 added if
month missing, .25 added if day missing, .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.
sas.get(library, member, variables=character(0), ifs=character(0),
format.library=library, 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=!.R., quiet=FALSE,
temp=tempfile("SaS"), formats=TRUE,
recode=formats, special.miss=FALSE, sasprog="sas",
as.is=.5, check.unique.id=TRUE, force.single=FALSE, where,
uncompress=FALSE)is.special.miss(x, code)
x[...]
## S3 method for class 'special.miss':
print(x, ...)
## S3 method for class 'special.miss':
format(x, ...)
sas.codes(object)
code.levels(object)
library=".", indicating that the current
directory is to be used.sas.get with special.miss=T
or with recode in effect.formats to FALSE to keep sas.get from telling the SAS macro to
retrieve value label formats from format.library. When you do not
specify formats or recode, sas.getT if formats is T. If it is
T, variables that have an appropriate format (see above) are
recoded as factor objects, which map the values
to the value labels for tspecial.miss to
T. This will cause the special.miss attribute and the
special.miss clasrow.names attribute of a data frame, but
the id variable is still retained as a variable in the data frame.
You can also specify a vector of vaas.is=FALSE or if as.is is a number between 0 and 1 inclusive and
the number of unique values of the variable is less than
the number of observations (n) tid is specified, the row names are checked for
uniqueness if check.unique.id=T. If any are duplicated, a warning
is printed. Note that if a data frame is being created with duplicate
row names, statements such as my.daLENGTHs > 4 are stored as
S double precision numerics, which allow for the same precision as
a SAS LENGTH 8 variable. Set force.single=T to store every
numeric variable in siFALSE, delete the SAS log file upon completion.FALSE to make the result a list instead of a data frameTRUE, remove all temporary files when finished. You
may want to keep these while debugging the SAS macro. Not needed for R.FALSE, print the contents of the
SAS log file if there has been an error.FALSE by default. Set it
to T to automatically invoke the DOS PKUNZIP command
if member.zip exists,
to uncompress the SAS dataset before
proceeding. This assumes you have the file permissions towhere, each individual variable
is placed into a separate object (whose name is the name
of the variable) using the assign function wicode is omitted, is.special.miss will return a T for each
observation that has any special missing value.sas.getid
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 T 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.pager function.special.miss=T 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 LRECLs to quadruple them, for example.
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.
data.frame, describe,
label, upDatamice <- 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 $HOME/saslib"), mem="mice",
var=c("dose", "ld50"), ifs="if strain='nude'")
# Get a dataset from current directory, recode PROC FORMAT; VALUE \dots
# variables into factors with labels of the form "good(1)" "better(2)",
# get special missing values, recode missing codes .D and .R into new
# factor levels "Don't know" and "Refused to answer" for variable q1
d <- sas.get(mem="mydata", recode=2, special.miss=TRUE)
attach(d)
nl <- length(levels(q1))
lev <- c(levels(q1), "Don't know", "Refused")
q1.new <- as.integer(q1)
q1.new[is.special.miss(q1,"D")] <- nl+1
q1.new[is.special.miss(q1,"R")] <- nl+2
q1.new <- factor(q1.new, 1:(nl+2), lev)
# Note: would like to use factor() in place of as.integer \dots but
# factor in this case adds "NA" as a category level
d <- sas.get(mem="mydata")
sas.codes(d$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;Run the code above in your browser using DataLab