rxCreateColInfo: Function to generate a 'colInfo' list from a data source

Article
07/15/2019

Description

Generates a colInfo list from a data source that can be used in rxImport or an RxDataSource constructor.

Usage

  rxCreateColInfo(data, includeLowHigh = FALSE, factorsOnly = FALSE, 
       varsToKeep = NULL, sortLevels = FALSE, computeInfo = TRUE,
        useFactorIndex = FALSE)

Arguments

`data`

An RxDataSource object, a character string containing an .xdf file name, or a data frame. An object returned from rxGetVarInfo is also supported.

`includeLowHigh`

If TRUE, the low/high values will be included in the colInfo object. Note that this will override any actual low/high values in the data set if the colInfo object is applied to a different data source.

`factorsOnly`

If TRUE, only column information for factor variables will be included in the output.

`varsToKeep`

NULL to include all variables, or character vector of variables to include.

`sortLevels`

If TRUE, factor levels will be sorted. If factor levels represent integers, they will be put in numeric order.

`computeInfo`

If TRUE, a pass through the data will be taken for non-xdf data sources in order to compute factor levels and low/high values.

`useFactorIndex`

If TRUE, the factorIndex variable type will be used instead of factor.

Details

This function can be used to ensure consistent factor levels when importing a series of text files to xdf. It is also useful for repeated analysis on non-xdf data sources.

Value

A colInfo list that can be used as input for rxImport and in data sources such as RxTextData and RxSqlServerData.

Author(s)

Microsoft Corporation Microsoft Technical Support

Examples


 # Get the low/high values and factor levels before using a data source
 # for import or analysis

 # Create a text data source, specifying the 'yearsEmploy' should be a factor
 mort1 <- file.path(rxGetOption("sampleDataDir"), "mortDefaultSmall2000.csv")
 mort1DS <- RxTextData(file = mort1, colClasses = c(yearsEmploy = "factor", default = "logical"))

 # By default, rxCreateColInfo will make a pass through the data to compute factor levels
 # and low/high values.  We'll also request that the levels be sorted
 mortColInfo <- rxCreateColInfo(data = mort1DS, includeLowHigh = TRUE, sortLevels = TRUE)

 # Re-create the data source, now using the computed colInfo
 mort1DS <- RxTextData(file = mort1, colInfo = mortColInfo)
 # Import the data
 mort1DF <- rxImport(mort1DS)
 levels(mort1DF$yearsEmploy)

 # Or use the text data source directly in an analysis 
 # (not needing a pass through the data to compute the factor levels)
 logitObj <- rxLogit(default~yearsEmploy, data = mort1DS)

 ##############################################################################################

 # Train a model on one imported data set, then score using another
 # Train a model on the first year of the data, importing it from text to a data frame

 mort1 <- file.path(rxGetOption("sampleDataDir"), "mortDefaultSmall2000.csv")
 mort1DS <- RxTextData(file = mort1, colClasses = c(yearsEmploy = "factor", default = "logical"))
 # Since we haven't specified factor levels, they will be created 'first come, first serve'
 mort1DF <- rxImport(mort1DS)
 levels(mort1DF$yearsEmploy)
 # Estimate a logit model
 logitObj <- rxLogit(default~yearsEmploy, data = mort1DF)

 # Now import the second year of data
 mort2 <- file.path(rxGetOption("sampleDataDir"), "mortDefaultSmall2001.csv")
 mort2DS <- RxTextData(file = mort2, colClasses = c(yearsEmploy = "factor", default = "logical"))
 mort2DF <- rxImport(mort2DS)
 # The levels are in a different order
 levels(mort2DF$yearsEmploy)

 # If we try to use the model estimated from the first data set to predict on the seoond,
 # predOut <- rxPredict(logitObj, data = mort2DF)
 # We will get an error
 #ERROR: order of factor levels in the data are inconsistent with
 #the order of the model coefficients

 # Instead, we can extract the colInfo from the first data set
 mortColInfo <- rxCreateColInfo(data = mort1DF)
 # And use it when importing the second
 mort2DS <- RxTextData(file = mort2, colInfo = mortColInfo)
 mort2DF <- rxImport(mort2DS)
 predOut <- rxPredict(logitObj, data = mort2DF)
 head(predOut)

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