sas.get {Hmisc} | R Documentation

## 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")

    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

`libraryName` | character string naming the directory in which the dataset is kept. The default is `libraryName = "."`, indicating that the current directory is to be used.
`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 directory name.)
`x` | a variable that may have been created by `sas.get` with `special.miss=TRUE` or with `recode` in effect.

`variables`

vector of character strings naming the variables in the SAS dataset. The resulting data frame 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. 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 directory containing the file ‘formats.sc2’, 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 `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.get` will set `format` to `TRUE` if a SAS format catalog (‘.sct’ or ‘.sc2’) file exists in `format.library`. `sas.get` stores SAS

    PROC FORMAT VALUE

definitions 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. 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 `TRUE`.

`drop` | logical. If `TRUE` the result is coerced to the lowest possible dimension.

`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. 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 identification variable.

`dates.` | specifies the format for storing SAS dates in the resulting data frame.

`as.is`

SAS character variables are converted to S factor objects if `as.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`) times `as.is`. The default if `as.is` is 0.5, 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 = TRUE`. 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 LENGTH > 4 are stored as S double precision numerics, which allow for the same precision as a SAS

    LENGTH

8 variable. Set `force.single = TRUE` 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.

`keep.log` | logical: 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` | set to `FALSE` to make the result a list instead of a data frame
`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 `FALSE` by default. Set it to `TRUE` 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 to allow uncompressing in place. If the file is already uncompressed, this option is ignored.
`pos` | by default, a list or data frame which contains all the variables is returned. If you specify `pos`, each individual variable is placed into a separate object (whose name is the name of the variable) using the `assign` function with the `pos` 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 \_) to check against. If `code` is omitted, `is.special.miss` will return a `TRUE` for each observation that has any special missing value.
`defaultencoding` | encoding to assume if the SAS dataset does not specify one. Defaults to "latin1".
`object` | a variable in a data frame created by `sas.get`
`...` | ignored

### 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 LRECLs 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)

[Package Hmisc version 4.0-3 Index]