sdcTable (version 0.21.5)

makeProblem: create sdcProblem-class-objects

Description

Function makeProblem is used to create sdcProblem-class-objects.

Usage

makeProblem(data, dimList, dimVarInd, freqVarInd = NULL, numVarInd = NULL, weightInd = NULL, sampWeightInd = NULL)

Arguments

data

a data frame featuring at least one column for each desired dimensional variable. Optionally the input data can feature variables that contain information on cell counts, weights that should be used during the cut and branch algorithm, additional numeric variables or variables that hold information on sampling weights.

dimList

a named list with each list element being either a data-frame or a link to a .csv-file containing the complete level-hierarchy of a dimensional variable using a top-to-bottom approach. The list names correspond to variable names that must exist in argument data. The level-hierarchy must be specified as follows:

• list-element is a data-frame that must contain exactly 2 columns with the first column specifying levels and the second column holding variable-codes.
  • first column: a character vector specifying levels with each vector element being a string only containing of '@'s from length 1 to n. If a vector element consists of i-chars, the corresponding code is of level i. The code '@' (one character) equals the grand total (level=1).
  • second column: a character vector specifying level codes

• list-element is full path to a .csv-file with two columns seperated by semicolons (;) having the same structure as the data.frame described above

dimVarInd

numeric vector (or NULL) defining the column-indices of dimensional variables (defining the table) within argument data

freqVarInd

numeric vector (or NULL) defining the column-indices of a variable holding counts within argument data

numVarInd

numeric vector (or NULL) defining the column-indices of additional numeric variables available in argument data

weightInd

numeric vector of length 1 (or NULL) defining the column-index of a variable holding weights that should be used during as objective coefficients during the cut and branch algorithm to protect primary sensitive cells within argument data

sampWeightInd

numeric vector of length 1 (or NULL) defining the column-index of a variable holding sampling weights within argument data

Value

a sdcProblem-class-object

Examples

# loading micro data
sp <- searchpaths()
fn <- paste(sp[grep("sdcTable", sp)], "/data/microData1.RData", sep="")
microData <- get(load(fn))

# having a look at the data structure
str(microData)

# we can observe that we have a micro data set consisting of two spanning
# variables ('region' and 'gender') and one numeric variable ('val')

# specify structure of hierarchical variable 'region'
# levels 'A' to 'D' sum up to a Total
dim.region <- data.frame(
 levels=c('@','@@','@@','@@','@@'),
 codes=c('Total', 'A','B','C','D'),
 stringsAsFactors=FALSE)

# specify structure of hierarchical variable 'gender'
# levels 'male' and 'female' sum up to a Total
dim.gender <- data.frame(
 levels=c('@','@@','@@'),
 codes=c('Total', 'male','female'),
 stringsAsFactors=FALSE)

# create a list with each element being a data-frame containing information
# on a dimensional variables
dimList <- list(dim.region, dim.gender)

# name the list:
# - first list-element: corresponds to variable 'region'
# - second list-element: corresponds to variable 'gender'
names(dimList) <- c('region', 'gender')

# specify the indices where dimensional variables are located
# within the input data

# - variable 'region': first column
# - variable 'gender': second column
dimVarInd <- c(1,2)

# third column containts a numeric variable
numVarInd <- 3

# no variables holding counts, numeric values, weights or sampling
# weights are available in the input data
freqVarInd <- weightInd <- sampWeightInd <- NULL

# creating an object of class \code{\link{sdcProblem-class}}
problem <- makeProblem(
 data=microData,
 dimList=dimList,
 dimVarInd=dimVarInd,
 freqVarInd=freqVarInd,
 numVarInd=numVarInd,
 weightInd=weightInd,
 sampWeightInd=sampWeightInd)

# what do we have?
print(class(problem))