| sas.get {Hmisc} | R Documentation |
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.
You may have the function 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.
The chron function is used to set up date, time, and date-time variables.
If using S-Plus 5 or 6 or later, the timeDate function is used
instead.
Under R, Dates is used for dates and chron
for date-times. For times without
dates, these still need to be stored in date-time format in POSIX.
Such SAS time variables are given a major class of timePOSIXt and a
format.timePOSIXt function so that the date portion (which will
always be 1/1/1970) will not print by default.
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 uncompress (or gunzip) compressed
SAS datasets.
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 |
character string naming the directory in which the dataset is kept. |
member |
character string giving the second part of the two part SAS dataset name. (The first part is irrelevant here - it is mapped to the UNIX directory name.) |
x |
a variable that may have been created by sas.get with
special.miss=T or with recode in effect.
|
variables |
vector of character strings naming the variables in the SAS dataset.
The S dataset will contain only those variables from the
SAS dataset.
To get all of the variables (the default), an empty string may be given.
It is a fatal error if any one of the variables is not
in the SAS dataset. You can use sas.contents to get
the variables in the SAS dataset.
If you have retrieved a subset of the variables
in the SAS dataset and which to retrieve the same list of variables
from another dataset, you can program the value of variables - see
one of the last examples.
|
ifs |
a vector of character strings, each containing one SAS "subsetting if" statement. These will be used to extract a subset of the observations in the SAS dataset. |
format.library |
The UNIX directory containing the file formats.sct, which contains the definitions of the user defined formats used in this dataset. By default, we look for the formats in the same directory as the data. The user defined formats must be available (so SAS can read the data). |
formats |
Set formats to F 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.get will set format to T if a
SAS format catalog (.sct or .sc2) file exists in format.library.
Value label formats if present are stored as the formats attribute of the returned
object (see below). A format is used if it is referred to by one or more
variables
in the dataset, if it contains no ranges of values (i.e., it identifies
value labels for single values), and if it is a character format
or a numeric format that is not used just to label missing values.
If you set recode to TRUE, 1, or 2, formats defaults to TRUE.
To fetch the values and labels for variable x in the dataset d you
could type:
f <- attr(d$x, "format")
formats <- attr(d, "formats")
formats$f$values; formats$f$labels
|
recode |
This parameter defaults to TRUE if formats is TRUE. If it is
TRUE, variables that have an appropriate format (see above) are
recoded as factor objects, which map the values
to the value labels for the format. Alternatively, set recode to
1 to use labels of the form value:label, e.g. 1:good 2:better 3:best.
Set recode to 2 to use labels such as good(1) better(2) best(3).
Since sas.codes and code.levels add flexibility, the usual choice
for recode is T or TRUE.
|
special.miss |
For numeric variables, any missing values are stored as NA in S.
You can recover special missing values by setting special.miss to
TRUE. This will cause the special.miss attribute and the
special.miss class to be added
to each variable that has at least one special missing value.
Suppose that variable y was .E in observation 3 and .G
in observation 544. The special.miss attribute for y then has the
value
list(codes=c("E","G"),obs=c(3,544))
To fetch this information for variable y you would say for example
s <- attr(y, "special.miss")
s$codes; s$obs
or use is.special.miss(x) or the print.special.miss method, which
will replace NA values for the variable with E or G if they
correspond to special missing values.
The describe
function uses this information in printing a data summary.
|
id |
The name of the variable to be used as the row names of the S dataset.
The id variable becomes the row.names attribute of a data frame, but
the id variable is still retained as a variable in the data frame.
(if data.frame.out is FALSE, this will be the attribute "id" of the S
dataset.) You can also specify a vector of variable names as the id
parameter. After fetching the data from SAS, all these variables will be
converted to character format and concatenated (with a space as a separator)
to form a (hopefully) unique ID variable.
|
dates. |
specifies the format for storing SAS dates in the resulting data frame |
as.is |
IF data.frame.out=T, SAS character variables are converted to S factor
objects if as.is=F 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) times as.is. The default if as.is is .5,
so character variables are converted to factors only if they have fewer
than n/2 unique values. The primary purpose of this is to keep unique
identification variables as character values in the data frame instead
of using more space to store both the integer factor codes and the
factor labels.
|
check.unique.id |
If id 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.data.frame["B23",] will retrieve
only the first row with a row name of "B23".
|
force.single |
By default, SAS numeric variables having LENGTHs > 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 single precision (7 digits of precision).
This option is useful when the creator of the SAS dataset has
failed to use a LENGTH statement.
R does not have single precision, so no attempt is made to convert to
single if running R.
|
dates |
One of the character strings "sas", "yearfrac", "yearfrac2", "yymmdd".
If a SAS variable has a date format (one of "DATE", "MMDDYY", "YYMMDD",
"DDMMYY", "YYQ", "MONYY", "JULIAN"), it will be converted to the format
specified by dates before being given to S. "sas" gives
days from 1/1/1960 (from 1/1/1970 if using chron),
"yearfrac" gives days from 1/1/1900 divided by
365.25, "yearfrac2" gives year plus fraction of current year,
and "yymmdd" gives a 6 digit number YYMMDD (year%%100, month, day).
Note that S will store these as numbers, not as
character strings. If dates="sas" and a variable has one of the SAS
date formats listed above, the variable will be given a class of "date"
to work with Terry Therneau's implementation of the "date" class in S.
If the chron package or timeDate function is available, these are
used instead.
|
keep.log |
logical flag: if FALSE, delete the SAS log file upon completion.
|
log.file |
the name of the SAS log file. |
macro |
the name of an S object in the current search path that contains the text of the SAS macro called by S. The S object is a character vector that can be edited using for example sas.get.macro <- editor(sas.get.macro). |
data.frame.out |
logical flag: if TRUE, the return value will be an S data frame,
otherwise it will be a list.
|
clean.up |
logical flag: if TRUE, remove all temporary files when finished. You
may want to keep these while debugging the SAS macro. Not needed for R.
|
quiet |
logical flag: if FALSE, print the contents of the SAS log file if
there has been an error.
|
temp |
the prefix to use for the temporary files. Two characters will be added to this, the resulting name must fit on your file system. |
sasprog |
the name of the system command to invoke SAS |
uncompress |
set to T to automatically invoke the UNIX gunzip command
(if member.ssd01.gz exists) or the uncompress command
(if member.ssd01.Z exists) to uncompress the SAS dataset before
proceeding. This assumes you have the file permissions to allow
uncompressing in place. If the file is already uncompressed, this
option is ignored.
|
where |
by default, a list or data frame which contains all the variables is returned.
If you specify where, each individual variable is placed into a
separate object (whose name is the name of the variable) using the
assign function with the where argument. For example, you can
put each variable in its own file in a directory, which in some cases
may save memory over attaching a data frame.
|
code |
a special missing value code (A through Z or underscore) to check
against. If code is omitted, is.special.miss will return
a T for each observation that has any special missing value.
|
object |
a variable in a data frame created by sas.get |
... |
ignored |
If you specify 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.
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.
if data.frame.out is TRUE, the output will
be 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 $).
Each of these lists has a vector called values and one called
labels with the PROC FORMAT; VALUE ... definitions.
If data.frame.out is FALSE, the output will
be a list of vectors, each containing a variable from the SAS
dataset. If id was specified, that element of the list will
be used as the id attribute of the entire list.
if a SAS error occurs and quiet is FALSE, then the SAS log file will be
printed under the control of the less pager.
The references cited below explain the structure of SAS datasets and how they are stored under UNIX. See SAS Language for a discussion of the "subsetting if" statement.
You must be able to run SAS (by typing sas) on your system.
If the S command !sas does not start SAS, then this function cannot work.
If you are reading time or
date-time variables, you will need to execute the command library(chron)
to print those variables or the data frame if the timeDate function
is not available.
Terry Therneau, Mayo Clinic
Frank Harrell, Vanderbilt University
Bill Dunlap, University of Washington and Insightful Corporation
Michael W. Kattan, Cleveland Clinic Foundation
SAS Institute Inc. (1990). SAS Language: Reference, Version 6. First Edition. SAS Institute Inc., Cary, North Carolina.
SAS Institute Inc. (1988). SAS Technical Report P-176, Using the SAS System, Release 6.03, under UNIX Operating Systems and Derivatives. SAS Institute Inc., Cary, North Carolina.
SAS Institute Inc. (1985). SAS Introductory Guide. Third Edition. SAS Institute Inc., Cary, North Carolina.
data.frame, describe,
label,
upData,
cleanup.import
## Not run:
sas.contents("saslib", "mice")
# [1] "dose" "ld50" "strain" "lab_no"
attr(, "n"):
# [1] 117
mice <- sas.get("saslib", mem="mice", var=c("dose", "strain", "ld50"))
plot(mice$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 $HOME/saslib"), mem="mice",
var=c("dose", "ld50"), ifs="if strain='nude'")
# Get a dataset from current directory, recode PROC FORMAT; VALUE ...
# variables into factors with labels of the form "good(1)" "better(2)",
# get special missing values, recode missing codes .D and .R into new
# factor levels "Don't know" and "Refused to answer" for variable q1
d <- sas.get(".", "mydata", recode=2, special.miss=TRUE)
attach(d)
nl <- length(levels(q1))
lev <- c(levels(q1), "Don't know", "Refused")
q1.new <- as.integer(q1)
q1.new[is.special.miss(q1,"D")] <- nl+1
q1.new[is.special.miss(q1,"R")] <- nl+2
q1.new <- factor(q1.new, 1:(nl+2), lev)
# Note: would like to use factor() in place of as.integer ... but
# factor in this case adds "NA" as a category level
d <- sas.get(".", "mydata")
sas.codes(d$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
# Retrieve the same variables from another dataset (or an update of
# the original dataset)
mydata2 <- sas.get('mydata2', var=names(d))
# This only works if none of the original SAS variable names contained _
mydata2 <- cleanup.import(mydata2) # will make true integer variables
# 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)