RxSpssData: Generate SPSS Data Source Object

Artigo
07/15/2019

Description

Generate an RxSpssData object that contains information about an SPSS data set to be imported or analyzed. RxSpssData is an S4 class, which extends RxDataSource.

Usage

  RxSpssData(file, stringsAsFactors = FALSE, colClasses = NULL, colInfo = NULL,
             rowsPerRead = 500000, labelsAsLevels = TRUE, labelsAsInfo = TRUE,
             mapMissingCodes = "all", varsToKeep = NULL, varsToDrop = NULL, 
             checkVarsToKeep = FALSE)


 ## S3 method for class `RxSpssData':
head  (x, n = 6L, reportProgress = 0L, ...)
 ## S3 method for class `RxSpssData':
tail  (x, n = 6L, addrownums = TRUE, reportProgress = 0L, ...)

Arguments

`file`

character string specifying an SPSS data file of type .sav.

`stringsAsFactors`

logical indicating whether or not to automatically convert strings to factors on import. This can be overridden by specifying "character" in colClasses and colInfo. If TRUE, the factor levels will be coded in the order encountered. Since this factor level ordering is row dependent, the preferred method for handling factor columns is to use colInfo with specified "levels".

`colClasses`

character vector specifying the column types to use when converting the data. The element names for the vector are used to identify which column should be converted to which type.

Allowable column types are:
- "logical" (stored as uchar),
- "integer" (stored as int32),
- "float32" (the default for floating point data for .xdf files),
- "numeric" (stored as float64 as in R),
- "character" (stored as string),
- "factor" (stored as uint32),
- "int16" (alternative to integer for smaller storage space),
- "uint16" (alternative to unsigned integer for smaller storage space)
- "Date" (stored as Date, i.e. float64)
Note for "factor" type, the levels will be coded in the order encountered. Since this factor level ordering is row dependent, the preferred method for handling factor columns is to use colInfo with specified "levels".
Note that equivalent types share the same bullet in the list above; for some types we allow both 'R-friendly' type names, as well as our own, more specific type names for .xdf data.
Note also that specifying the column as a "factor" type is currently equivalent to "string" - for the moment, if you wish to import a column as factor data you must use the colInfo argument, documented below.

`colInfo`

list of named variable information lists. Each variable information list contains one or more of the named elements given below. The information supplied for colInfo overrides that supplied for colClasses.

Currently available properties for a column information list are:
type - character string specifying the data type for the column. See colClasses argument description for the available types.
newName - character string specifying a new name for the variable.
description - character string specifying a description for the variable.
levels - character vector containing the levels when type = "factor". If the levels property is not provided, factor levels will be determined by the values in the source column. If levels are provided, any value that does not match a provided level will be converted to a missing value.
newLevels - new or replacement levels specified for a column of type "factor". It must be used in conjunction with the levels argument. After reading in the original data, the labels for each level will be replaced with the newLevels.
low - the minimum data value in the variable (used in computations using the F() function.
high - the maximum data value in the variable (used in computations using the F() function.

`rowsPerRead`

number of rows to read at a time. This will determine the size of a block in the .xdf file if using rxImport.

`labelsAsLevels`

logical. If TRUE, variables containing value labels in the SPSS file will be converted to factors, using the value labels as factor levels.

`labelsAsInfo`

logical. If TRUE, variables containing value labels in the SPSS file that are not converted to factors will retain the information as valueInfoCodes and valueInfoLabels in the .xdf file. This information can be obtained using rxGetVarInfo. This information will also be returned as attributes for the columns in a dataframe when using rxDataStep.

`mapMissingCodes`

character string specifying how to handle SPSS variables with multiple missing value codes. If "all", all of the values set as missing in SPSS will be treated as NA. If "none", the missing value specification in SAS will be ignored and the original values will be imported. If "first", the values equal to the first missing value code will be imported as NA, while any other missing value codes will be treated as the original values.

`varsToKeep`

character vector of variable names to include when reading from the input data file. If NULL, argument is ignored. Cannot be used with varsToDrop.

`varsToDrop`

character vector of variable names to exclude when reading from the input data file. If NULL, argument is ignored. Cannot be used with varsToKeep.

`checkVarsToKeep`

logical value. If TRUE variable names specified in varsToKeep will be checked against variables in the data set to make sure they exist. An error will be reported if not found. Ignored if more than 500 variables in the data set.

`x`

an RxSpssData object

`n`

positive integer. Number of rows of the data set to extract.

`addrownums`

logical. If TRUE, row numbers will be created to match the original data set.

`reportProgress`

integer value with options:

0: no progress is reported.
1: the number of processed rows is printed and updated.
2: rows processed and timings are reported.
3: rows processed and all timings are reported.

`...`

arguments to be passed to underlying functions

Details

The tail method is not functional for this data source type and will report an error.

Value

object of class RxSpssData.

Author(s)

Microsoft Corporation Microsoft Technical Support

Examples


 # Create a SPSS data source
 claimsSpssFileName <- file.path(rxGetOption("sampleDataDir"), "claims.sav")
 claimsSpssSource <- RxSpssData(claimsSpssFileName)

 # Specify an xdf data source
 claimsXdfFileName <- file.path(tempdir(), "importedClaims.xdf")

 # Import the data into the xdf file
 myXdfDataSource <- rxImport(claimsSpssSource, claimsXdfFileName, overwrite = TRUE)

 # Instead, import the (small) data set into a data frame
 claimsIn <- rxImport(claimsSpssSource)
 head(claimsIn)

 # Clean up
 file.remove(claimsXdfFileName)

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