# Handling questionnaire items with memisc

## 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:

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

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.

## 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

Suppose a numeric vector of responses to the question about their vote intention coded using the coding frame shown above looks as follows

voteint
  [1]  4  3  9  2 97 99  9  9  1  1  3  3  9  3  9  1  1  9  9  3  1  9  1  9  9
[26]  9 98 99  9  2  1  1  4  9  1  1  1 98  2  9  2  9  1  1  3  1  2  3  1  2
[51]  9  1  9 97  9  1  9  1  9  9  1  9 97  9 97  9  4  2  9  2  9  1  9  2  4
[76]  1  2  1  2  9  9  4  9 97  3  1  1  1  9  9  1  9  3 99  3  4  4  3  1  9
[101]  4 97  1 99  2  2 98  3  3 98  1  9 98 99  1  3  9  9  2  1  1  9  1  2  1
[126]  9  9  1  4  9  9  1  4  4  9 99  3  9  9  9  3  4  9  9  4  4  9  4  4  9
[151]  2  1  1  1  1  9  9  9  1  3  1  2 99  3  2  9  2 99  2  3  9  1  1  1  2
[176]  9  4  1 98  3 99 99  9  9  3  9  1  2  1  9  2  4 98  1  4 99  9  2  2  2

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):

# 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,
"Not applicable"   = 99)

voteint is now an item vector, for which a particular "show" method is defined:

class(voteint)
[1] "double.item"
attr(,"package")
[1] "memisc"
str(voteint)
 Nmnl. item w/ 8 labels for 1,2,3,...  num [1:200] 4 3 9 2 97 99 9 9 1 1 ...
voteint

Item (measurement: nominal, type: double, length = 200)

[1:200] Other Party Liberal Democrat Will not vote Labour Don't know ...

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:

labels(voteint)

Values and labels:

1 'Conservative'
2 'Labour'
3 'Liberal Democrat'
4 'Other Party'
9 'Will not vote'
97 'Don't know'
99 'Not applicable'  

Now if we rather want shorter labels, we can change them either by something like labels(voteint) <- ... or by changing the labels using relabel():

voteint <- relabel(voteint,
"Conservative"     = "Cons",
"Labour"           = "Lab",
"Liberal Democrat" = "LibDem",
"Other Party"      = "Other",
"Will not vote"    = "NoVote",
"Don't know"       = "DK",
"Not applicable"   = "N.a.")

Let us take a look at the result:

labels(voteint)

Values and labels:

1 'Cons'
2 'Lab'
3 'LibDem'
4 'Other'
9 'NoVote'
97 'DK'
98 'Refused'
99 'N.a.'   
voteint

Item (measurement: nominal, type: double, length = 200)

[1:200] Other LibDem NoVote Lab DK N.a. NoVote NoVote Cons Cons LibDem ...
str(voteint)
 Nmnl. item w/ 8 labels for 1,2,3,...  num [1:200] 4 3 9 2 97 99 9 9 1 1 ...

### 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

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:

voteint

Item (measurement: nominal, type: double, length = 200)

[1:200] Other LibDem NoVote Lab *DK *N.a. NoVote NoVote Cons Cons LibDem ...

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

missing.values(voteint) <- missing.values(voteint) + 9

The missing values can be recalled as usual:

missing.values(voteint)
97, 98, 99, 9

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:

as.numeric(voteint)[1:30]
 [1]  4  3 NA  2 NA NA NA NA  1  1  3  3 NA  3 NA  1  1 NA NA  3  1 NA  1 NA NA
[26] NA NA NA NA  2
as.factor(voteint)[1:30]
 [1] Other  LibDem <NA>   Lab    <NA>   <NA>   <NA>   <NA>   Cons   Cons
[11] LibDem LibDem <NA>   LibDem <NA>   Cons   Cons   <NA>   <NA>   LibDem
[21] Cons   <NA>   Cons   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   Lab
Levels: Cons Lab LibDem Other

It is also possible to drop all missing value designations:

missing.values(voteint) <- NULL
missing.values(voteint)
NULL
as.numeric(voteint)[1:30]
 [1]  4  3  9  2 97 99  9  9  1  1  3  3  9  3  9  1  1  9  9  3  1  9  1  9  9
[26]  9 98 99  9  2

In contrast to SPSS it is possible with "memisc" to designate the valid, i.e. non-missing values:

valid.values(voteint) <- 1:4
valid.values(voteint)
1, 2, 3, 4
missing.values(voteint)
9, 97, 98, 99

Instead of individual valid or missing values it is also possible to define a range of values as valid:

valid.range(voteint) <- c(1,9)
missing.values(voteint)
97, 98, 99

### Other attributes of survey items

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:

description(voteint) <- "Vote intention"
description(voteint)
[1] "Vote intention"

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

wording(voteint) <- "Which party are you going to vote for in the general election next Tuesday?"
wording(voteint)
[1] "Which party are you going to vote for in the general election next Tuesday?"
annotation(voteint)
description:
Vote intention

wording:
Which party are you going to vote for in the general election next
Tuesday?
annotation(voteint)["wording"]
                                                                      wording
"Which party are you going to vote for in the general election next Tuesday?" 

### 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:

codebook(voteint)
================================================================================

voteint 'Vote intention'

"Which party are you going to vote for in the general election next
Tuesday?"

--------------------------------------------------------------------------------

Storage mode: double
Measurement: nominal
Valid range: 1 - 9

Values and labels     N Valid Total

1   'Cons'          49  27.8  24.5
2   'Lab'           26  14.8  13.0
3   'LibDem'        21  11.9  10.5
4   'Other'         19  10.8   9.5
9   'NoVote'        61  34.7  30.5
97 M 'DK'             6         3.0
98 M 'Refused'        7         3.5
99 M 'N.a.'          11         5.5

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:

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():

codebook(voteint1)
================================================================================

voteint1 'Vote intention'

"Which party are you going to vote for in the general election next
Tuesday?"

--------------------------------------------------------------------------------

Storage mode: double
Measurement: nominal
Valid range: 1 - 9

Values and labels     N Valid Total

1   'Cons'          44  25.0  22.0
2   'Lab'           24  13.6  12.0
3   'LibDem'        16   9.1   8.0
4   'Other'         17   9.7   8.5
9   'NoVote'        55  31.2  27.5
97 M 'DK'             6         3.0
98 M 'Refused'        7         3.5
99 M 'N.a.'          11         5.5
(unlab.val.)    20  11.4  10.0

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

wild.codes(voteint1)
  Counts Percent
5   13.0     6.5
7    7.0     3.5

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:

voteint2 <- voteint
labels(voteint2) <- NULL # This deletes all value labels
codebook(voteint2)
================================================================================

voteint2 'Vote intention'

"Which party are you going to vote for in the general election next
Tuesday?"

--------------------------------------------------------------------------------

Storage mode: double
Measurement: nominal
Valid range: 1 - 9

Values               N Valid Total

(unlab.val.)   176 100.0  88.0
M (unlab.mss.)    24        12.0

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

measurement(voteint2) <- "interval"
codebook(voteint2)
================================================================================

voteint2 'Vote intention'

"Which party are you going to vote for in the general election next
Tuesday?"

--------------------------------------------------------------------------------

Storage mode: double
Measurement: interval
Valid range: 1 - 9

Min: 1.000
Max: 9.000
Mean: 4.483
Std.Dev.: 3.413

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

show_html(codebook(voteint))

voteint‘Vote intention’

“Which party are you going to vote for in the general election next Tuesday?”

 Storage mode: double Measurement: nominal Valid range: 1 - 9

 Values and labels N Valid Total 1 ‘Cons’ 49 27 . 8 24 . 5 2 ‘Lab’ 26 14 . 8 13 . 0 3 ‘LibDem’ 21 11 . 9 10 . 5 4 ‘Other’ 19 10 . 8 9 . 5 9 ‘NoVote’ 61 34 . 7 30 . 5 97 M ‘DK’ 6 3 . 0 98 M ‘Refused’ 7 3 . 5 99 M ‘N.a.’ 11 5 . 5

## 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

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:

Data

Data set with 300 observations and 3 variables

vote region income
1    2      3   4950
2   99     99    727
3    2      3   1667
4   97     99   2970
5    1      1   2943
6    9      2   1351
7    1      1   1540
8    4      1   2270
9    3      1   2047
10    8      1   6042
11    9     99   1589
12    3     99   5126
13    1      1   1206
14    8      2   8878
15    8      1   2859
16    3      1   1038
17    2      2   1844
18    2      1   2928
19    9     99    921
20   97      1   2885
21    1      2   1453
22    4      3   1185
23    8      2   3593
24    2      3   4981
25    2      2   8243
.. .... ...... ......
(25 of 300 observations shown)

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:

options(show.max.obs=5)
Data

Data set with 300 observations and 3 variables

vote region income
1    2      3   4950
2   99     99    727
3    2      3   1667
4   97     99   2970
5    1      1   2943
. .... ...... ......
(5 of 300 observations shown)
# 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:

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:

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,
"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)

})
Warning in within(Data, {: Variables 'junk1','junk2' have wrong length, removing
them.

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:

Data

Data set with 300 observations and 3 variables

vote               region income
1               Labour                Wales   4950
3               Labour                Wales   1667
4      *Not applicable *Not asked in survey   2970
5        Conservatives              England   2943
7        Conservatives              England   1540
8                Other              England   2270
9    Liberal Democrats              England   2047
10          *Don't know              England   6042
12    Liberal Democrats *Not asked in survey   5126
13        Conservatives              England   1206
14          *Don't know             Scotland   8878
15          *Don't know              England   2859
16    Liberal Democrats              England   1038
17               Labour             Scotland   1844
18               Labour              England   2928
20      *Not applicable              England   2885
21        Conservatives             Scotland   1453
22                Other                Wales   1185
23          *Don't know             Scotland   3593
24               Labour                Wales   4981
25               Labour             Scotland   8243
.. .................... .................... ......
(25 of 300 observations shown)

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:

EnglandData <- subset(Data,region == "England")
EnglandData

Data set with 132 observations and 3 variables

vote  region income
1        Conservatives England   2943
2        Conservatives England   1540
3                Other England   2270
4    Liberal Democrats England   2047
5          *Don't know England   6042
6        Conservatives England   1206
7          *Don't know England   2859
8    Liberal Democrats England   1038
9               Labour England   2928
10      *Not applicable England   2885
11                Other England   2155
12                Other England   1280
13      *Not applicable England   4111
14               Labour England    689
15 *Not asked in survey England   2421
16                Other England   5511
17 *Not asked in survey England   4628
18          *Don't know England    896
19          *Don't know England    842
20          *Don't know England    948
21        Conservatives England   2346
22        Conservatives England   1234
23                Other England   1186
24        Conservatives England   1215
25      *Not applicable England   5516
.. .................... ....... ......
(25 of 132 observations shown)

### Codebooks of data sets

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:

codebook(Data)
================================================================================

vote 'Vote intention'

"If a general election would take place next Tuesday, the candidate of which
party would you vote for?"

--------------------------------------------------------------------------------

Storage mode: double
Measurement: nominal
Missing values: 8, 9, 97, 99

Values and labels              N Valid Total

1   'Conservatives'          32  21.1  10.7
2   'Labour'                 41  27.0  13.7
3   'Liberal Democrats'      36  23.7  12.0
4   'Other'                  43  28.3  14.3
8 M 'Don't know'             47        15.7
9 M 'Answer refused'         29         9.7
97 M 'Not applicable'         28         9.3
99 M 'Not asked in survey'    44        14.7

Remark:
This is not a real survey item, of course ...

================================================================================

region 'Region of residence'

--------------------------------------------------------------------------------

Storage mode: double
Measurement: nominal
Missing values: 97, 99

Values and labels              N Valid Total

1   'England'               132  51.4  44.0
2   'Scotland'               87  33.9  29.0
3   'Wales'                  38  14.8  12.7
99 M 'Not asked in survey'    43        14.3

Remark:
This is not a real survey item, of course ...

================================================================================

income 'Household income'

"All things taken into account, how much do all household members earn in
sum?"

--------------------------------------------------------------------------------

Storage mode: double
Measurement: ratio

Min:   245.000
Max: 13596.000
Mean:  2556.743
Std.Dev.:  2158.757

Remark:
This is not a real survey item, of course ...

On a website, it looks better in HTML:

show_html(codebook(Data))

vote‘Vote intention’

“If a general election would take place next Tuesday, the candidate of which party would you vote for?”

 Storage mode: double Measurement: nominal Missing values: 8, 9, 97, 99

 Values and labels N Valid Total 1 ‘Conservatives’ 32 21 . 1 10 . 7 2 ‘Labour’ 41 27 . 0 13 . 7 3 ‘Liberal Democrats’ 36 23 . 7 12 . 0 4 ‘Other’ 43 28 . 3 14 . 3 8 M ‘Don’t know’ 47 15 . 7 9 M ‘Answer refused’ 29 9 . 7 97 M ‘Not applicable’ 28 9 . 3 99 M ‘Not asked in survey’ 44 14 . 7

Remark:
This is not a real survey item, of course …

region‘Region of residence’

 Storage mode: double Measurement: nominal Missing values: 97, 99

 Values and labels N Valid Total 1 ‘England’ 132 51 . 4 44 . 0 2 ‘Scotland’ 87 33 . 9 29 . 0 3 ‘Wales’ 38 14 . 8 12 . 7 99 M ‘Not asked in survey’ 43 14 . 3

Remark:
This is not a real survey item, of course …

income‘Household income’

“All things taken into account, how much do all household members earn in sum?”

 Storage mode: double Measurement: ratio

 Min: 245 . 000 Max: 13596 . 000 Mean: 2556 . 743 Std.Dev.: 2158 . 757

Remark:
This is not a real survey item, of course …

### 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:

DataFr <- as.data.frame(Data)
## Looking a the data frame structure
str(DataFr)
'data.frame':   300 obs. of  3 variables:
$vote : Factor w/ 4 levels "Conservatives",..: 2 NA 2 NA 1 NA 1 4 3 NA ... ..- attr(*, "label")= chr "Vote intention"$ region: Factor w/ 3 levels "England","Scotland",..: 3 NA 3 NA 1 2 1 1 1 1 ...
..- attr(*, "label")= chr "Region of residence"
\$ income: num  4950 727 1667 2970 2943 ...
..- attr(*, "label")= chr "Household income"
## Looking at the first 25 observations
DataFr[1:25,]
                vote   region income
1             Labour    Wales   4950
2               <NA>     <NA>    727
3             Labour    Wales   1667
4               <NA>     <NA>   2970
5      Conservatives  England   2943
6               <NA> Scotland   1351
7      Conservatives  England   1540
8              Other  England   2270
9  Liberal Democrats  England   2047
10              <NA>  England   6042
11              <NA>     <NA>   1589
12 Liberal Democrats     <NA>   5126
13     Conservatives  England   1206
14              <NA> Scotland   8878
15              <NA>  England   2859
16 Liberal Democrats  England   1038
17            Labour Scotland   1844
18            Labour  England   2928
19              <NA>     <NA>    921
20              <NA>  England   2885
21     Conservatives Scotland   1453
22             Other    Wales   1185
23              <NA> Scotland   3593
24            Labour    Wales   4981
25            Labour Scotland   8243

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

xtabs(~vote+region,data=DataFr)
                   region
vote                England Scotland Wales
Conservatives          16        4     7
Labour                 12       17     7
Liberal Democrats      20        7     3
Other                  24       13     4

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:

xtabs(~vote+region,data=Data)
                   region
vote                England Scotland Wales
Conservatives          16        4     7
Labour                 12       17     7
Liberal Democrats      20        7     3
Other                  24       13     4

Sometimes we do want missing values to be included, and this is possible too:

xtabs(~vote+region,data=within(Data,
vote <- include.missings(vote)))
                      region
vote                   England Scotland Wales
Conservatives             16        4     7
Labour                    12       17     7
Liberal Democrats         20        7     3
Other                     24       13     4
*Don't know               19       19     4
*Not applicable           12        5     6
*Not asked in survey      18       12     4

For convenience, there is also a codebook method for data frames:

show_html(codebook(DataFr))

vote‘Vote intention’

 Storage mode: integer Factor with 4 levels

 Values and labels N Valid Total 1 ‘Conservatives’ 32 21 . 1 10 . 7 2 ‘Labour’ 41 27 . 0 13 . 7 3 ‘Liberal Democrats’ 36 23 . 7 12 . 0 4 ‘Other’ 43 28 . 3 14 . 3 NA 148 49 . 3

region‘Region of residence’

 Storage mode: integer Factor with 3 levels

 Values and labels N Valid Total 1 ‘England’ 132 51 . 4 44 . 0 2 ‘Scotland’ 87 33 . 9 29 . 0 3 ‘Wales’ 38 14 . 8 12 . 7 NA 43 14 . 3

income‘Household income’

 Storage mode: double

 Min: 245 . 000 Max: 13596 . 000 Mean: 2556 . 743 Std.Dev.: 2158 . 757 Skewness: 2 . 288 Kurtosis: 6 . 745

## 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

The function recode() is similar in semantics to the function of the same name in package "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).

Here we load an example data set – a subset of the German Longitudinal Election Study for 20133 – into R’s memory.

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:

with(gles2013work,
show_html(codebook(bula)))

bula‘Bundesland’

 Storage mode: double Measurement: nominal

 Values and labels N Percent 1 ‘Baden-Wuerttemberg’ 333 8 . 5 2 ‘Bayern’ 507 13 . 0 3 ‘Berlin’ 190 4 . 9 4 ‘Brandenburg’ 212 5 . 4 5 ‘Bremen’ 27 0 . 7 6 ‘Hamburg’ 49 1 . 3 7 ‘Hessen’ 232 5 . 9 8 ‘Mecklenburg-Vorpommern’ 160 4 . 1 9 ‘Niedersachsen’ 331 8 . 5 10 ‘Nordrhein-Westfalen’ 619 15 . 8 11 ‘Rheinland-Pfalz’ 150 3 . 8 12 ‘Saarland’ 45 1 . 2 13 ‘Sachsen’ 402 10 . 3 14 ‘Sachsen-Anhalt’ 252 6 . 4 15 ‘Schleswig-Holstein’ 131 3 . 3 16 ‘Thueringen’ 271 6 . 9

We now recode the Bundesländer codes into a new variable:

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:

xtabs(~bula+east.west,data=gles2013work)
                        east.west
bula                     East West
Bayern                    0  507
Berlin                  190    0
Brandenburg             212    0
Bremen                    0   27
Hamburg                   0   49
Hessen                    0  232
Mecklenburg-Vorpommern  160    0
Niedersachsen             0  331
Nordrhein-Westfalen       0  619
Rheinland-Pfalz           0  150
Saarland                  0   45
Sachsen                 402    0
Sachsen-Anhalt          252    0
Schleswig-Holstein        0  131
Thueringen              271    0

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.

### 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:

x <- 1:10
xc <- cases(x <= 3,
x > 3 & x <= 7,
x > 7)
data.frame(x,xc)
    x             xc
1   1         x <= 3
2   2         x <= 3
3   3         x <= 3
4   4 x > 3 & x <= 7
5   5 x > 3 & x <= 7
6   6 x > 3 & x <= 7
7   7 x > 3 & x <= 7
8   8          x > 7
9   9          x > 7
10 10          x > 7

In this example cases() returns a factor. It can also be made to return a numeric value:

xn <- cases(1 <- x <= 3,
2 <- x > 3 & x <= 7,
3 <- x > 7)
data.frame(x,xn)
    x xn
1   1  1
2   2  1
3   3  1
4   4  2
5   5  2
6   6  2
7   7  2
8   8  3
9   9  3
10 10  3

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:

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
)
})
Warning in cases(postal.vote.candidate <- wave == 1 & intent.turnout == : 78 NAs
created
Warning in cases(postal.vote.list <- wave == 1 & intent.turnout == 6, 900 <-
wave == : 78 NAs created

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:

gles2013work <- within(gles2013work,{
candidate.vote <- recode(as.item(candidate.vote),
"CDU/CSU"   =  1 <- 1,
"SPD"       =  2 <- 4,
"FDP"       =  3 <- 5,
"Grüne"     =  4 <- 6,
"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,
"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"
})
Warning in recode(as.item(candidate.vote), CDU/CSU = 1 <- 1, SPD = 2 <- 4, :
recoding created 18 NAs
Warning in recode(as.item(list.vote), CDU/CSU = 1 <- 1, SPD = 2 <- 4, :
recoding created 19 NAs

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:

xtabs(~list.vote+east.west,data=gles2013work)
         east.west
list.vote East West
CDU/CSU  440  714
SPD      268  554
FDP       32   87
Grüne     70  226
NPD       11    6
Piraten   14   34
AfD       27   63
Other      6   21
No Vote  197  318
xtabs(~list.vote+candidate.vote,data=gles2013work)
         candidate.vote
list.vote CDU/CSU  SPD  FDP Grüne Linke  NPD Piraten  AfD Other No Vote
CDU/CSU    1060   29   20     3    12    0       2    0     2       0
SPD          44  700    1    39    14    1       2    1     1       0
FDP          67   13   33     1     0    0       2    0     0       0
Grüne        32  102    4   141     7    0       5    3     0       0
Linke        10   45    2    15   245    2       2    2     1       0
NPD           0    2    0     0     1   12       0    0     1       0
Piraten       3    3    1     8     5    0      25    1     0       0
AfD          20    7    2     2     5    2       5   43     2       0
Other         5    4    0     3     1    1       0    1    11       0
No Vote       0    0    0     0     0    0       0    0     0     515

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.

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.

3. The German Longitudinal Election Study is funded by the German National Science Foundation (DFG) and carried out outin close cooperation with the DGfW, 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.