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Extract Data

Source: R/extractData.R
extractData.Rd

Extract data.frame from a GADSdat object for analyses in R. Value labels can be selectively applied via defining convertLabels and covertVariables. For extracting meta data see extractMeta.

Usage

extractData(
  GADSdat,
  convertMiss = TRUE,
  convertLabels = c("character", "factor", "numeric"),
  convertVariables = NULL,
  dropPartialLabels = TRUE
)

Arguments

GADSdat

A GADSdat object.

convertMiss

Should values tagged as missing values be recoded to NA?

convertLabels

If "numeric", values remain as numerics. If "factor" or "character", values are recoded to their labels. Corresponding variable type is applied.

convertVariables

Character vector of variables names, which labels should be applied to. All other variables remain as numeric variables in the data. If not specified [default], value labels are applied to all variables for which labels are available. Variable names not in the actual GADS are silently dropped.

dropPartialLabels

Should value labels for partially labeled variables be dropped? If TRUE, the partial labels will be dropped. If FALSE, the variable will be converted to the class specified in convertLabels.

Value

Returns a data frame.

Details

A GADSdat object includes actual data (GADSdat$dat) and the corresponding meta data information (GADSdat$labels). extractData extracts the data and applies relevant meta data on value level (missing conversion, value labels), so the data can be used for analyses in R. Variable labels are retained as label attributes on column level.

If factor are extracted via convertLabels == "factor", an attempt is made to preserve the underlying integers. If this is not possible, a warning is issued. As SPSS has almost no limitations regarding the underlying values of labeled integers and R's factor format is very strict (no 0, only integers increasing by + 1), this procedure can lead to frequent problems.

Examples

# Extract Data for Analysis
dat <- extractData(pisa)

# convert labeled variables to factors
dat <- extractData(pisa, convertLabels = "factor")

# convert only some variables to factor, all others remain numeric
dat <- extractData(pisa, convertLabels = "factor", convertVariables = c("schtype", "ganztag"))

# convert only some variables to character, all others remain numeric
dat <- extractData(pisa, convertLabels = "factor", convertVariables = c("schtype", "ganztag"))
# schtype is now character
table(dat$schtype)
#> 
#>                Gymnasium (academic track) 
#>                                       221 
#>                                Realschule 
#>                                       168 
#> schools with several courses of education 
#>                                       111 
# schtype remains numeric
table(dat$gender)
#> 
#>   1   2 
#> 257 243