sdcProb2df {sdcTable}

Transform a problem instance

Description

sdcProb2df() returns a data.table given an sdcProblem input object.

Usage

sdcProb2df(obj, addDups = TRUE, addNumVars = FALSE, dimCodes = "both")

Arguments

obj

an sdcProblem object

addDups

(logical), if TRUE, duplicated cells are included in the output

addNumVars

(logical), if TRUE, numerical variables (if defined in makeProblem() will be included in the output.

dimCodes

(character) allows to specify in which coding the dimensional variables should be returned. Possible choices are:

• "both": both original and internally used, standardized codes are included in the output

• "original": only original codes of dimensional variables are included in the output

• "default": only internally used, standardized codes are included in the output

Value

a data.table containing information about all cells of the given problem

Examples

# loading micro data
utils::data("microdata1", package = "sdcTable")

# 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'
# using create_node() and add_nodes() (see ?manage_hierarchies)
dim.gender <- hier_create(root = "Total", nodes = c("male", "female"))
hier_display(dim.gender)

# create a named list with each element being a data-frame
# containing information on one dimensional variable and
# the names referring to variables in the input data
dimList <- list(region = dim.region, gender = dim.gender)

# third column containts a numeric variable
numVarInd <- 3

# no variables holding counts, numeric values, weights or sampling
# weights are available in the input data
# creating an problem instance using numeric indices
p1 <- makeProblem(
  data = microdata1,
  dimList = dimList,
  numVarInd = 3 # third variable in `data`
)

# using variable names is also possible
p2 <- makeProblem(
  data = microdata1,
  dimList = dimList,
  numVarInd = "val"
)

# what do we have?
print(class(p1))

# have a look at the data
df1 <- sdcProb2df(p1, addDups = TRUE,
  addNumVars = TRUE, dimCodes = "original")
df2 <- sdcProb2df(p2, addDups=TRUE,
  addNumVars = TRUE, dimCodes = "original")
print(df1)

identical(df1, df2)