---
title: Handling questionnaire items
output: rmarkdown::html_vignette
vignette: >
  % \VignetteIndexEntry{Handling questionnaire items}
  % \VignetteEngine{knitr::rmarkdown}
  % \VignetteEncoding{UTF-8}
---

```{r,echo=FALSE,message=FALSE}
knitr::opts_chunk$set(comment=NA,
               fig.align="center",
               results="markup")
```

# Motivation

R is a great tool to do data analysis and for data management tasks that arise
in the context of big data analytics. Nevertheless there is still room for
improvement in terms of the support for data management tasks that arise in the
social sciences, especially when it comes to handling data that come from social
surveys and opinion surveys. The main reason for this is that the way that
questionnaire item responses as they are usually coded in machine-readable
survey data sets do not directly and easily translate into R's data types for
numeric and categorical data, that is, numerical vectors and factors. As a
consequence, many social scientists exercise their everyday data management
tasks with commercial software packages such as SPSS or Stata, but there may be
social scientists who either cannot afford such commercial software or prefer to
use, out of principle, open-source software for all steps of data management and
analysis.

It is one of the aim of the "memisc" package to provide a bridge between social
science data sets of variables that contain coded responses to questionnaire
items, with their typical structures involving labelled numeric response codes
and numeric codes declared as "missing values". As an illustrative example,
suppose in a pre-election survey, respondents are asked about which party they
are going to vote for in their constituency in the framework of a
first-past-the-post electoral system. Suppose the response categories offered to
the respondents are "Conservative", "Labour", "Liberal Democrat", "Other
party".[^1] A survey agency that actually conducts the interviews with a sample
of voters may, according to common practice, use the following codes to collect
the responses to the question about the vote intention:

<div class="booktable">
<style>
.booktable table th {
    border-top: 0 none;
    border-bottom: 0 none;
    background-color: unset;
}
.booktable table td {
    border-top: 0 none;
    border-bottom: 0 none;
    background-color: unset;
}

.booktable table tr.even {
    background-color: unset;
}

.booktable table tr.header {
    border-top: 2px solid;
    border-bottom: 1px solid;
}

.booktable table tbody tr:last-child {
    border-bottom: 2px solid;
}
</style>

Response category     Code        
-------------------- ----- ----- 
Conservative            1      
Labour                  2      
Liberal Democrat        3      
Other Party             4      
Will not vote           9      
Don't know             97   (M)
Answer refused         98   (M)
Not applicable         99   (M)

</div>

In data sets that contain the results of such coding are essentially numeric
data -- with some additional information about the "value labels" (the labels
attached to the numeric values) and about the "missing values" (those numeric
values that indicate responses that one usually does not want to include into
statistical analysis). While this coding frame for responses to survey
questionnaires is far from uncommon in the social sciences, it is not
straightforward to retain this information in *R* objects. Here there are two
main alternatives, (1) one could store the responses as a numeric vector,
thereby losing the information about the labelled values, or (2) one could store
the responses as a factor, thereby losing the information contained in the
codes. Either way, one will lose the information about the "missing values". Of
course, one can filter out these missing values before data analysis by
replacing them with `NA`, but it would convenient to have facilities that do
that automatically.

[^1]: Those familiar with British politics will realise that this is a
    simplification of the menu of available choices that voters in England
    typically face in an election of the House of Commons.

# Standard attributes of survey items

The "memisc" package introduces a new data type (more correctly an `S4` class)
that allows to handle such data, that allows to adjust labels or missing values
definitions and to translate such data as needed either into numeric vectors of
factors, thereby automatically filtering out the missing values. This data time
(or `S4` class) is, for lack of a better term, called `"item"`. In general,
users do not bother with the construction of such item vectors. Usually they are
generated when data sets are imported from data files in SPSS or Stata
format. This page is mainly concerned with describing the structure of such item
vectors and how they can be manipulated in the data management step that usually
precedes data analysis. It is thus possible to do all the data management in *R*
from importing the pristine data obtained from data archives or other data
providers, such as the survey institutes to which a principal investigator has
delegated data collection. Of course, the facilities introduced by the `"item"`
data type also allow to create appropriate representations of survey item
responses if a principal investigator obtains only raw numeric codes. In the
following, the construction of `"item"` vectors from raw numeric data is mainly
used to highlight their structure.

## Value labels {#value_labels}

```{r,echo=FALSE}
set.seed(20)
vote.probs <- c(.361,.29,.23,1-.361-.29-.23)
decis.probs <- c(vote.probs*.651,1-.651)
all.probs <- c(.9*decis.probs,.1*rep(1/3,3))
voteint <- sample(c(1:4,9,97,98,99),
                  prob=all.probs,
                  replace=TRUE,
                  size=200)                
library(memisc)
```
Suppose a numeric vector of responses to the question about their vote intention coded using
the coding frame shown above looks as follows
```{r}
voteint
```

This numeric vector is transformed into an `"item"` vector by attaching labels
to the codes. The *R* code to attach labels that reflect the coding frame shown
above may look like follows (if formatted nicely):

```{r}
# This is to be run *after* memisc has been loaded.
labels(voteint) <- c(Conservative       =  1,
                     Labour             =  2,
                     "Liberal Democrat" =  3, # We have whitespace in the label, 
                     "Other Party"      =  4, # so we need quotation marks
                     "Will not vote"    =  9,
                     "Don't know"       = 97,
                     "Answer refused"   = 98,
                     "Not applicable"   = 99)
```
`voteint` is now an item vector, for which a particular `"show"` method is defined:
```{r}
class(voteint)
str(voteint)
voteint
```

Like with factors, if *R* shows the contents of the vector, the labels are shown
(instead of the codes). Since item vectors typically are quite long, because
they come from interviewing a survey sample and usual survey sample sizes are
about 2000, we usually do not want to see all the values in the
vector. `"memisc"` anticipates this and shows at most a single line of
output. (In the output, also the "level of measurement" is shown, which at this
point does not have a consquence. It will become clear later what the
implications of the "level of measurement" are.)

In line with the usual semantics `labels(voteint)` will now show us a
description of the labels and to which values they are assigned:

```{r}
labels(voteint)
```
Now if we rather want shorter labels, we can change them either by something like `labels(voteint) <- ...` or by changing the labels using `relabel()`:
```{r}
voteint <- relabel(voteint,
                   "Conservative"     = "Cons",
                   "Labour"           = "Lab",
                   "Liberal Democrat" = "LibDem",
                   "Other Party"      = "Other",
                   "Will not vote"    = "NoVote",
                   "Don't know"       = "DK",
                   "Answer refused"   = "Refused",
                   "Not applicable"   = "N.a.")
```
Let us take a look at the result:
```{r}
labels(voteint)
voteint
str(voteint)
```

## Missing values {#missing_values}

In the coding plan shown above, the values 97, 98, and 99 are marked as "missing
values", that is, while they represent coded responses, they are not to be
considered as valid in the sense of providing information about the respondent's
vote intention. For the statistical analysis of vote intention it is natural to
replace them by `NA`. Yet replacing codes 97, 98, and 99 already at the stage of
importing data into *R* memory would mean a loss of potentially precious
information since it precludes, e.g. the motivation to refuse responding to the
vote intention question or the antencedents of undecidedness. Hence it is better
to mark those values and to delay their replacement by `NA` to a later stage in
the analysis of vote intentions and to be able to undo or change the
"missingness" of these values. For example, not only may one be interested in
the antecedents of response refusals but also be interested to analyse vote
intention with non-voting excluded or included. The memisc package provides,
like SPSS and PSPP, facilities to mark particular values of an item vector as
"missing" and change such designations throughout the data preperation stage.

There are several ways with `"memisc"` to make distinctions between valid and
missing values. The first way that mirrors the way it is done in SPSS. To
illustrate this we return to the fictitious vote intention example. The values
97,98,99 of `voteint` are designated as "missing" by

```{r}
missing.values(voteint) <- c(97,98,99)
```

The missing values are reflected in the output of `voteint`, (labels of) missing
values are marked with `*` in the output:

```{r}
voteint
```

It is also possible to extend the set of missing values: We add another value to
the set of missing values.

```{r}
missing.values(voteint) <- missing.values(voteint) + 9
```
The missing values can be recalled as usual:
```{r}
missing.values(voteint)
```
The missing values are turned into `NA` if `voteint` is coerced into a numeric vector or a factor, which is what usually happens before the eventual statistical analysis:

```{r}
as.numeric(voteint)[1:30]
as.factor(voteint)[1:30]
```
It is also possible to drop all missing value designations:
```{r}
missing.values(voteint) <- NULL
missing.values(voteint)
as.numeric(voteint)[1:30]
```
In contrast to SPSS it is possible with `"memisc"` to designate the valid, i.e. non-missing values:
```{r}
valid.values(voteint) <- 1:4
valid.values(voteint)
missing.values(voteint)
```
Instead of individual valid or missing values it is also possible to define a *range* of values as valid:
```{r}
valid.range(voteint) <- c(1,9)
missing.values(voteint)
```

## Other attributes of survey items {#annotations}
Other software packages targeted at social scientists also allow to add
annotations to the variables in a data set, which are not subject to the
syntactic constraints of variable names. These annotations are usually called
"variable labels" in these software packages. In `"memisc"` the corresponding
term is "description". In continuation of the running example, we add a
description to the vote intention variable:

```{r}
description(voteint) <- "Vote intention"
description(voteint)
```

In contrast to other software, `"memisc"` allows to attach arbitrarily
annotation to survey items, such as the wording of a survey question:

```{r}
wording(voteint) <- "Which party are you going to vote for in the general election next Tuesday?"
wording(voteint)
annotation(voteint)
annotation(voteint)["wording"]
```

## Codebooks of survey items

It is common in survey research to describe a data set in the form of a
*codebook*. A codebook summarises each variable in the data set in terms of its
relevant attributes, that is, the label attached to the variable (in the context
of the `memisc` package this is called its "description"), the labels attached
to the values of the variable, which values of the variable are supposed to be
*missing* or *valid*, as well as univariate summary statistics of each variable,
usually without and with missing variables included. Such functionality is
provided in this package by the function `codebook()`. `codebook()` when applied
to an `"item"` object returns a `"codebook"` object, which when printed to the
console gives an overview of the variable usually required for the codebook of a
data set (the production of codebooks for whole data sets is described further
below). To illustrate the `codebook()` function we now produce a codebook of the
`voteint` item variable created above:

```{r}
codebook(voteint)
```

As can be seen in the output, the `codebook()` function reports the name of the
variable, the description (if defined for the variable), and the question
wording (again if defined). Further it reports the storage mode (which is use by
*R*), the level of measurement ("nominal", "ordinal", "interval", or "ratio")
and the range of valid values (or alternatively, individually defined valid
values, individually defined missing values, or ranges of missing values). For
item variables with value labels, it shows a table of frequencies of the
labelled values, and the percentages of valid values and all values with
missings included.

Codebooks are particularly useful to find "wild codes", that is codes that are
not labelled, and usually produced by coding errors. Such coding errors may be
less common in data sets produced by CAPI or CATI or online surveys, but they
may occur in older data sets from before the age of computer-assisted
interviewing and also during the course of data management. This use of
codebooks is demonstrated in the following by deliberatly adding some coding
errors into a copy of our `voteint` variable:

```{r}
voteint1 <- voteint
voteint1[sample(length(voteint),size=20)] <- c(rep(5,13),rep(7,7))
```

The presence of these "wild codes" can now be spotted using `codebook()`:

```{r}
codebook(voteint1)
```

The output shows that 20 observations contain wild codes in this variable. Why
don't we get a list of wild codes as part of the codebook? The reason is that
codebook is supposed also to work with continuous variables that have thousands
of unique, unlabelled values. Users certainly will not like to see them all as
part of a codebook.

In order to get a list of wild codes the development version of "memisc"
contains the function `wild.codes()`, which we apply to the variable `voteint1`

```{r}
wild.codes(voteint1)
```

We see that 6.5 and 3.5 percent of the observations have the wild codes 5 and 7.

To see how `codebook()` works with variables without value labels, we create an
unlabelled copy of our `voteint` variable:

```{r}
voteint2 <- voteint
labels(voteint2) <- NULL # This deletes all value labels
codebook(voteint2)
```

Usually, variables without labelled values represent measures on an interval or
ratio scale. In that case, we do not want to see how many unlabelled values
there are, but we want to get some other statistics, such as mean, variance,
etc. To this purpose, we decleare the variable `voteint2` to have an
interval-scale level of measurement.[^2]

```{r}
measurement(voteint2) <- "interval"
codebook(voteint2)
```
[^2]: Of course, substantially it does not make sense at all to form averages
    etc. of voting choices, so "do not try this at home". This example is merely
    to demonstrate codebooks and the setting of scale-levels.

For convenience of including them into word-processor documents, there is also
the possibility to export codebooks into HTML:

```{r,results='asis'}
show_html(codebook(voteint))
```

# Data sets: Containers of survey items

Usually one expects to be able handle data on responses to survey items not in
isolation, but as part of a data set, which contains a multitude of observations
on many variables. The usual data structure in *R* to contain
observation-on-variables data is the *data frame*. In principle it is possible
to put survey item vectors as described above into a data frame, nevertheless
the `"memisc"` package provides a special data structure to contain survey item
data called data sets or data set-objects, that is, objects of class
`"data.set"`. This opens up the possibility to automatically translate survey
items into regular vectors and factors, as expected by typical data analysis
functions, such as `lm()` or `glm()`.

## The structure of `"data.set"` objects

Data set objects have essentially the same row-by-column structure as data
frames: They are a set of vectors (however of class `"item"`) all with the same
length, so that in each row of the data set there are values in these vectors.
Observations can be addressed as rows of a `"data.set"` and variabels can be
addressed as columns, just as one may used to with regards to data frames. Most
data management operations that you can do with data frames can also be done
with data sets (such as merging them or using the functions `with()` or
`within()`). Yet in contrast to data frames, data sets are always expected to
contain objects of class `"item"`, and any vectors or factors from which a
`"data.set"` object is constructed are changed into `"item"` objects.

Another difference is the way that `"data.set"` objects are shown on the
console. As `S4` objects, if a user types in the name of a `"data.set"` objects,
the function `show()` (and not `print()`) is applied to it. The `show()`-method
for data set objects is defined in such a way that only the first few
observations of the first few variables are shown on the console -- in contrast
to `print()` as applied to a data frame, which shows *all* observations on *all*
variables. While it may be intuitive and convenient to be shown all observations
in a small data frame, this is not what you will want if your data set contains
more than 2000 observations on several hundred variables, the dimensions that
typical social science data sets have that you can download from data archives
such as that of ICPSR or GESIS.

The main facilitites of `"data.set"` objects are demonstrated in what follows.
First, we create a data set with fictional survey responses
```{r}
Data <- data.set(
          vote = sample(c(1,2,3,4,8,9,97,99),
                        size=300,replace=TRUE),
          region = sample(c(rep(1,3),rep(2,2),3,99),
                          size=300,replace=TRUE),
          income = round(exp(rnorm(300,sd=.7))*2000)
          )
```
Then, we take a look at this already sizeable `"data.set"`" object:
```{r}
Data
```

In this case, our data set has only three variables, all of which are shown, but
of the observations we see only the first 25. Actually the number of
observations shown can be determined by the option `"show.max.obs"` which
defaults to 25, but can be changed as convenient:

```{r}
options(show.max.obs=5)
Data
# Back to the default
options(show.max.obs=25)
```
If you *really* want to see the complete data on your console, then you can use `print()` instead:
```{r,eval=FALSE}
print(Data)
```
but you should not do this with large data sets, such as the Eurobarometer trend file ...

## Manipulating data in data sets

Typical data management tasks that you would otherwise have done in commercial packages like SPSS or
Stata can be conducted within data set objects. Actually to provide this possibility (to the author
of the package) was the main reason that the `"memisc"` package was created.
To demonstrate this, we continue with our fictional data which we prepare for further analysis:
```{r}
Data <- within(Data,{
  description(vote) <- "Vote intention"
  description(region) <- "Region of residence"
  description(income) <- "Household income"
  wording(vote) <- "If a general election would take place next Tuesday,
                    the candidate of which party would you vote for?"
  wording(income) <- "All things taken into account, how much do all
                    household members earn in sum?"
  foreach(x=c(vote,region),{
    measurement(x) <- "nominal"
    })
  measurement(income) <- "ratio"
  labels(vote) <- c(
                    Conservatives         =  1,
                    Labour                =  2,
                    "Liberal Democrats"   =  3,
                    "Other"               =  4,
                    "Don't know"          =  8,
                    "Answer refused"      =  9,
                    "Not applicable"      = 97,
                    "Not asked in survey" = 99)
  labels(region) <- c(
                    England               =  1,
                    Scotland              =  2,
                    Wales                 =  3,
                    "Not applicable"      = 97,
                    "Not asked in survey" = 99)
  foreach(x=c(vote,region,income),{
    annotation(x)["Remark"] <- "This is not a real survey item, of course ..."
    })
  missing.values(vote) <- c(8,9,97,99)
  missing.values(region) <- c(97,99)

  # These to variables do not appear in the
  # the resulting data set, since they have the wrong length.
  junk1 <- 1:5
  junk2 <- matrix(5,4,4)
  
})
```

Now that we have added information to the data set that reflects the code plan
of the variables, we take a look how the it looks like:

```{r}
Data
```

As you can see, labelled item look a bit like factors, but with a difference:
User-defined missing values are marked with an asterisk.

Subsetting a data set object works as expected:
```{r}
EnglandData <- subset(Data,region == "England")
EnglandData
```


## Codebooks of data sets {#codebooks}

Previouly, we created a code book for individual survey items. But it is also
possible to create a codebook for a whole data set (what one usually wants to
have a codebook of). Obtaining a codebook is simple, by applying the function
`codebook()` to the data frame:

```{r}
codebook(Data)
```
On a website, it looks better in HTML:
```{r,results='asis'}
show_html(codebook(Data))
```

## Translating data sets into data frames

The punchline of the existence of `"data.set"` objects however is that they can
be coerced into regular data frames, using `as.data.frame()`, which causes
survey items to be translated into regular numeric vectors or factors using
`as.numeric()`, `as.factor()` or `as.ordered()` as above, and pre-determined
missing values changed into `NA`. Whether a survey item is changed into a
numerical vector, an unordered or an ordered factor depends on the declared
measurement level (which can be manipulated by `measurement()` as shown above).

In the example developed so far, the variables `vote` and `region` are declared
to have a nominal level of measurement, while `income` is declared to have a
ratio scale. That is, in statistical analyses, we want the first two variables
to be handled as (unordered) factors, and the income variable as a numerical
vector. In addition, we want all the user-declared missing values to be changed
into `NA` so that observations where respondents stated to "don't know" what
they are goint go vote for are excluded from the analysis. So let's see whether
this works - we coerce our data set into a data frame:

```{r}
DataFr <- as.data.frame(Data)
## Looking a the data frame structure
str(DataFr)
## Looking at the first 25 observations
DataFr[1:25,]
```

Indeed the translation works as expected, so we can use it for statistical
analysis, here a simple cross tab:

```{r}
xtabs(~vote+region,data=DataFr)
```

In fact, since many functions such as `xtabs()`, `lm()`, `glm()`, etc. coerce
theire `data=` argument into a data frame, an explicit coercion with
`as.data.frame()` is not always needed:

```{r}
xtabs(~vote+region,data=Data)
```
Sometimes we do want missing values to be included, and this is possible too:
```{r}
xtabs(~vote+region,data=within(Data, 
                               vote <- include.missings(vote)))
```

For convenience, there is also a codebook method for data frames:
```{r,results='asis'}
show_html(codebook(DataFr))
```

# More tools for data preparation

When social scientists work with survey data, these are not always organised and
coded in a way that suits the intended data analysis. For this reasons, the
`"memisc"` package provides the two functions `recode()` and `cases()`. The
former is -- as the name suggests -- for recoding, while the second allows for
complex distinctions of cases and can be seen as a more general version of
`ifelse()`. These two functions are demonstrated with a "real-life" example.

## Recoding {#recode}

The function `recode()` is similar in semantics to the function of the same name
in package [`"car"`][CAR] and designed in such a way that it does not conflict
with this function.  In fact, if `recode()` is called in the way as expected in
package `"car"`, it will dispatch processing to this function. In other words,
users of this other package may use `recode()` as they are used to.  The version
of the `recode()` function provided by `"memisc"` differs from the `"car"`
version in so far as its syntax is more R-ish (or so I believe).

[CAR]: https://cran.r-project.org/package=car

Here we load an example data set -- a subset of the German Longitudinal Election
Study for 2013[^3] -- into R's memory.

```{r}
load(system.file("gles/gles2013work.RData",package="memisc"))
```

As a simple example for the use of `recode()` we use this function to recode
German Bundesländer into an item with two values or East and West Germany. But
first we create a codebook for the variable that contains the Bundesländer
codes:

```{r,results='asis'}
with(gles2013work,
     show_html(codebook(bula)))
```
We now recode the Bundesländer codes into a new variable:
```{r}
gles2013work <- within(gles2013work,
                       east.west <- recode(bula,
                                          East = 1 <- c(3,4,8,13,14,16),
                                          West = 2 <- c(1,2,5:7,9:12,15)
                                          ))
```
and check whether this was successful:
```{r}
xtabs(~bula+east.west,data=gles2013work)
```
as can be seen, `recode()` was called in such a way that not only old
codes are transferred into new ones, but also the new codes are labelled.


[^3]: The [German Longitudinal Election
Study](https://www.gesis.org/gles/about-gles) is funded by the German National
Science Foundation (DFG) and carried out outin close cooperation with the
[DGfW](https://www.dgfw.info/), German Society for Electoral Studies.  Principal
investigators are Hans Rattinger (University of Mannheim, until 2014), Sigrid
Roßteutscher (University of Frankfurt), Rüdiger Schmitt-Beck (University of
Mannheim), Harald Schoen (Mannheim Centre for European Social Research, from
2015), Bernhard Weßels (Social Science Research Center Berlin), and Christof
Wolf (GESIS – Leibniz Institute for the Social Sciences, since 2012). Neither
the funding organisation nor the principal investigators bear any responsibility
for the example code shown here.

## Case distinctions 

Recoding can be used to combine the codes of an item into a smaller set, but
sometimes one needs to do more complex data preparations, in which the values of
some variable are set conditional on values of another one, etc.  For such
tasks, the `"memisc"` package provides the function `cases()`. This function
takes several expressions that evaluate to logical vectors as arguments and
returns a numeric vector or a factor, the values or level of which indicate for
each observation which of the expressions evaluates to `TRUE` the respective
observation. The factor levels are named after the logical expressions. A simple
example looks thus:

```{r}
x <- 1:10
xc <- cases(x <= 3, 
            x > 3 & x <= 7, 
            x > 7)
data.frame(x,xc)
```
In this example `cases()` returns a factor. It can also be made to return a numeric value:
```{r}
xn <- cases(1 <- x <= 3, 
            2 <- x > 3 & x <= 7, 
            3 <- x > 7)
data.frame(x,xn)
```

This example shows the way `cases()` works in the abstract. How this can be made
used of in practical example is best demonstrated by a real-world example, again
using data from the German Longitudinal Election Study.

In the 2013 election module, the intention to vote during the pre-election of
respondents interviewed in the pre-election wave (`wave==1`) and the
participation in the election of respondents interviewed in the post-election
wave (`wave==2`) are recorded in different data set variables, named here
`intent.turnout` and `turnout`.  The variable `intent.voteint` has codes for
whether the respondents were sure to participate (1), were likely to participate
(2), were undecided (3), likely not to (4), sure not to participate (5), or
whether they have cast a postal vote (6). Variable `turnout` has codes for those
who participated in the election (1) or did not (2).

The intention for the candidate vote is recorded in variable `voteint.candidate`
and the intention for the list vote is recoded in variable `voteint.list` for
the pre-election wave.  A postal vote for party candidate is recorded in
variable `postal.vote.candidate` and for a party list is in variable
`postal.vote.list`. Recalled votes in the post-election wave are recorded in
variables `vote.candidate` and `vote.list`.

These various variables are combined into two variables that has valid values
for both waves, `candidate.vote` and `list.vote`. For this, several conditions
have to be handled: whether a respondent is in the pre-election or the
post-election wave, whether s/he is not likely or sure not to vote, or whether
she has cast a postal vote. Thus the variable `cases()` is helpful here:

```{r}
gles2013work <- within(gles2013work,{

  candidate.vote <- cases(
              wave == 1 & intent.turnout == 6 -> postal.vote.candidate,
              wave == 1 & intent.turnout %in% 4:5 -> 900,
              wave == 1 & intent.turnout %in% 1:3 -> voteint.candidate,
              wave == 2 & turnout == 1 -> vote.candidate,
              wave == 2 & turnout == 2 -> 900
            )

  list.vote <- cases(
              wave == 1 & intent.turnout == 6 -> postal.vote.list,
              wave == 1 & intent.turnout %in% 4:5 -> 900,
              wave == 1 & intent.turnout %in% 1:3 -> voteint.list,
              wave == 2 & turnout ==1 -> vote.list,
              wave == 2 & turnout ==2 -> 900
            )
})
```

The code shown above does the following: In the pre-election wave (`wave == 1`),
the `candidate.vote` variable receives the value of the postal vote variable
`postal.vote.candidate` if a postal vote was cast (`intent.turnout == 6`), it
receives the value `900` for those respondents who where likely or sure not to
vote (`intent.turnout %in% 4:5`), and the value of the variable
`voteint.candidate` for all others (`intent.turnout %in% 1:3`).  In the
post-election wave (`wave == 2`) variable `candidate.vote` receives the value of
variable `vote.candidate` if the respondent has voted (`turnout == 1`) or the
value `900` if s/he has not voted (`turnout == 2`).  The variable `list.vote` is
constructed in an analogous manner from the variables `wave`, `intent.turnout`,
`turnout`, `postal.vote.list`, `voteint.list` and `vote.list`.  After the
constructin, the resulting variables `candidate.vote` and `list.vote` are
labelled and missing values are declared:

```{r}
gles2013work <- within(gles2013work,{
  candidate.vote <- recode(as.item(candidate.vote),
                      "CDU/CSU"   =  1 <- 1,
                      "SPD"       =  2 <- 4,
                      "FDP"       =  3 <- 5,
                      "Grüne"     =  4 <- 6,
                      "Linke"     =  5 <- 7,
                      "NPD"       =  6 <- 206,
                      "Piraten"   =  7 <- 215,
                      "AfD"       =  8 <- 322,
                      "Other"     = 10 <- 801,
                      "No Vote"   = 90 <- 900,
                      "WN"        = 98 <- -98,
                      "KA"        = 99 <- -99
                  )
  list.vote <- recode(as.item(list.vote),
                      "CDU/CSU"   =  1 <- 1,
                      "SPD"       =  2 <- 4,
                      "FDP"       =  3 <- 5,
                      "Grüne"     =  4 <- 6,
                      "Linke"     =  5 <- 7,
                      "NPD"       =  6 <- 206,
                      "Piraten"   =  7 <- 215,
                      "AfD"       =  8 <- 322,
                      "Other"     = 10 <- 801,
                      "No Vote"   = 90 <- 900,
                      "WN"        = 98 <- -98,
                      "KA"        = 99 <- -99
                  )
  
   missing.values(candidate.vote) <- 98:99
   missing.values(list.vote) <- 98:99
   measurement(candidate.vote) <- "nominal"
   measurement(list.vote) <- "nominal"
})
```

Finally, we can get a cross-tabulation of list votes and the East-West factor and
a cross tabulation of candidate votes against list votes:
```{r,width=120}
xtabs(~list.vote+east.west,data=gles2013work)
xtabs(~list.vote+candidate.vote,data=gles2013work)
```