Extract `data.frame`

from a `GADSdat`

object for analyses in `R`

. Per default, missing codes are applied but
value labels are dropped. Alternatively, value labels can be selectively applied via
`labels2character`

, `labels2factor`

, and `labels2ordered`

.
For extracting meta data see `extractMeta`

.

## Usage

```
extractData2(
GADSdat,
convertMiss = TRUE,
labels2character = NULL,
labels2factor = NULL,
labels2ordered = NULL,
dropPartialLabels = TRUE
)
```

## Arguments

- GADSdat
A

`GADSdat`

object.- convertMiss
Should values tagged as missing values be recoded to

`NA`

?- labels2character
For which variables should values be recoded to their labels? The resulting variables are of type

`character`

.- labels2factor
For which variables should values be recoded to their labels? The resulting variables are of type

`factor`

.- labels2ordered
For which variables should values be recoded to their labels? The resulting variables are of type

`ordered`

.- 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`labels2character`

,`labels2factor`

, or`labels2ordered`

.

## Details

A `GADSdat`

object includes actual data (`GADSdat$dat`

) and the corresponding meta data information
(`GADSdat$labels`

). `extractData2`

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 `labels2factor`

or `labels2ordered`

, 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 <- extractData2(pisa)
# convert only some variables to character, all others remain numeric
dat <- extractData2(pisa, labels2character = c("schtype", "ganztag"))
# convert only some variables to factor, all others remain numeric
dat <- extractData2(pisa, labels2factor = c("schtype", "ganztag"))
# convert all labeled variables to factors
dat <- extractData2(pisa, labels2factor = namesGADS(pisa))
# convert somme variables to factor, some to character
dat <- extractData2(pisa, labels2character = c("schtype", "ganztag"),
labels2factor = c("migration"))
```