rxSort: Variable sorting of an .xdf file or data frame.

Articolo
07/15/2019

Description

Efficient multi-key sorting of the variables in an .xdf file or data frame in a local compute context.

Usage


  rxSort(inData, outFile = NULL, sortByVars, decreasing = FALSE,
         type = "auto", missingsLow = TRUE, caseSensitive = FALSE,
         removeDupKeys = FALSE, varsToKeep = NULL, varsToDrop = NULL, 
         dupFreqVar = NULL, overwrite = FALSE, maxRowsByCols = 3000000, 
         bufferLimit = -1, reportProgress = rxGetOption("reportProgress"), 
         verbose = 0, xdfCompressionLevel = rxGetOption("xdfCompressionLevel"), 
         ...)

Arguments

`inData`

a data frame, a character string denoting the path to an existing .xdf file, or an RxXdfData object.

`inFile`

either an RxXdfData object or a character string denoting the path to an existing .xdf file.

`outFile`

an .xdf path to store the sorted output. If NULL, a data frame with the sorted output will be returned from rxSort.

`sortByVars`

character vector containing the names of the variables to use for sorting. If multiple variables are used, the first sortByVars variable is sorted and common values are grouped. The second variable is then sorted within the first variable groups. The third variable is then sorted within groupings formed by the first two variables, and so on.

`decreasing`

a logical scalar or vector defining the whether or not the sortByVars variables are to be sorted in decreasing or increasing order. If a vector, the length decreasing must be that of sortByVars. If a logical scalar, the value of decreasing is replicated to the length sortByVars.

`type`

a character string defining the sorting method to use. Type "auto" automatically determines the sort method based on the amount of memory required for sorting. If possible, all of the data will be sorted in memory. Type "mergeSort" uses a merge sort method, where chunks of data are pre-sorted, then merged together. Type "varByVar" uses a variable-by-variable sort method, which assumes that the sortByVars variables and the calculated sorted index variable can be held in memory simultaneously. If type="varByVar", the variables in the sorted data are re-ordered so that the variables named in sortByVars come first, followed by any remaining variables.

`missingsLow`

a logical scalar for controlling the treatment of missing values. If TRUE, missing values in the data are treated as the lowest value; if FALSE, they are treated as the highest value.

`caseSensitive`

a logical scalar. If TRUE, case sensitive sorting is performed for character data.

`removeDupKeys`

logical scalar. If TRUE, only the first observation will be kept when duplicate values of the key (sortByVars) are encountered. The sort type must be set to "auto" or "mergeSort".

`varsToKeep`

character vector defining variables to keep when writing the output to file. If NULL, this argument is ignored. This argument takes precedence over varsToDrop if both are specified, i.e., both are not NULL. If both varsToKeep and varsToDrop are NULL then all variables are written to the data source.

`varsToDrop`

character vector of variable names to exclude when writing the output data file. If NULL, this argument is ignored. If both varsToKeep and varsToDrop are NULL then all variables are written to the data source.

`dupFreqVar`

character string denoting name of new variable for frequency counts if removeDupKeys is set to TRUE. Ignored if removeDupKeys is set to FALSE. If NULL, a new variable for frequency counts will not be created.

`overwrite`

logical value. If TRUE, an existing outFile will be overwritten.

`maxRowsByCols`

the maximum size of a data frame that will be returned if outFile is set to NULL and inData is an .xdf file , measured by the number of rows times the number of columns. If the number of rows times the number of columns being created from the .xdf file exceeds this, a warning will be reported and the number of rows in the returned data frame will be truncated. If maxRowsByCols is set to be too large, you may experience problems from loading a huge data frame into memory.

`bufferLimit`

integer specifying the maximum size of the memory buffer (in Mb) to use in sorting when type is set to "auto". The default value of bufferLimit = -1 will attempt to determine an appropriate buffer limit based on system memory.

`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.

`verbose`

integer value. If 0, no additional output is printed. If 1, additional summary information is printed.

`xdfCompressionLevel`

integer in the range of -1 to 9. The higher the value, the greater the amount of compression for the output file - resulting in smaller files but a longer time to create them. If xdfCompressionLevel is set to 0, there will be no compression and the output file will be compatible with the 6.0 release of Revolution R Enterprise. If set to -1, a default level of compression will be used.

`blocksPerRead`

deprecated argument. It will be ignored.

`...`

additional arguments to be passed directly to the Revolution Compute Engine.

Details

Using sortbyVars, multiple keys can be specified to perform an iterative, within-category sorting of the variables in the inData or inFile .xdf file. The argument varsToKeep (or alternatively varsToDrop) is used to define the set of sorted variables to store in the specified outFile file or the returned data frame if outData is NULL. The sortbyVars variables are automatically prepended to the set of output variables defined by varsToKeep or varsToDrop.

Value

If an outFile is not specified, a data frame with the sorted data is returned. If an outFile is specified, an RxXdfData data source is returned that can be used in subsequent RevoScaleR analysis. If sorting is unsuccessful, FALSE is returned.

Author(s)

Microsoft Corporation Microsoft Technical Support

Examples


 ###
 # Small data example
 ###

 # sort data frame, decreasing by one column and increasing by the other
 sortByVars <- c("Sepal.Length", "Sepal.Width")
 decreasing <- c(TRUE, FALSE)
 sortedIris <- rxSort( inData = iris, sortByVars = sortByVars,
       decreasing = decreasing)

 # define file names and locations
 inXDF <- file.path(tempdir(), ".rxInFileTemp.xdf")
 outXDF1 <- file.path(tempdir(), ".rxOutFileTemp1.xdf")

 # Create xdf file for iris data
 rxDataStep(inData = iris, outFile = inXDF, overwrite = TRUE)
 rxGetInfo(inXDF)

 # sort the iris data set, first by sepal length in decreasing order
 # and then by sepal width in increasing order
 sortByVars <- c("Sepal.Length", "Sepal.Width")
 decreasing <- c(TRUE, FALSE)
 rxSort(inData = inXDF, outFile = outXDF1, sortByVars = sortByVars,
    decreasing = decreasing)
 z1 <- rxDataStep(inData = outXDF1)
 print(head(z1,10))

 # clean up
 if (file.exists(inXDF)) file.remove(inXDF)
 if (file.exists(outXDF1)) file.remove(outXDF1)

 ###
 # larger data example
 ###
 sampleDataDir <- rxGetOption("sampleDataDir")
 CensusPath <- file.path(sampleDataDir, "CensusWorkers.xdf")
 outXDF <- file.path(tempdir(), ".rxCensusSorted.xdf")

 # sort census data by 'age' and then 'incwage' in increasing 
 # and decreasing order, respectively. drop the 'perwt' and 'wkswork1
 # variables from the output.
 rxSort(inData = CensusPath, outFile = outXDF, sortByVars = c("age", "incwage"), 
           decreasing = c(FALSE, TRUE), varsToDrop = c("perwt", "wkswork1"))
 z <- rxDataStep(outXDF)
 print(head(z, 10))

 if (file.exists(outXDF)) file.remove(outXDF)

 ###
 # example removing duplicates and creating duplicate counts variable
 ###
 sampleDataDir <- rxGetOption("sampleDataDir")
 airDemo <- file.path(sampleDataDir, "AirlineDemoSmall.xdf")
 airDedup <- file.path(tempdir(), ".rxAirDedup.xdf")
 rxSort(inData = airDemo, outFile = airDedup, 
     sortByVars =  c("DayOfWeek", "CRSDepTime", "ArrDelay"),
     removeDupKeys = TRUE, dupFreqVar = "FreqWt")

 # Use the duplicate frequency as frequency weights in a regression
 linModObj <- rxLinMod(ArrDelay~CRSDepTime + DayOfWeek, 
     data = airDedup, fweights = "FreqWt")
 summary(linModObj)

 if (file.exists(airDedup)) file.remove(airDedup)

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