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) |
Answer refused |
98 |
(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,
"Answer refused" = 98,
"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'
98 'Answer refused'
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",
"Answer refused" = "Refused",
"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 Percent
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 Percent
1 'Cons' 45 25.3 22.5
2 'Lab' 24 13.5 12.0
3 'LibDem' 18 10.1 9.0
4 'Other' 15 8.4 7.5
9 'NoVote' 56 31.5 28.0
97 M 'DK' 6 3.0
98 M 'Refused' 7 3.5
99 M 'N.a.' 9 4.5
(unlab.vld.) 20 11.2 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 and labels N Percent
(unlab.vld.) 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
Skewness: 0.441
Kurtosis: -1.589
Miss.: 24.000
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 | Percent | |||||||
| 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 97 99 882
2 1 1 2174
3 99 2 2826
4 9 1 1382
5 2 2 1289
6 9 1 1810
7 1 1 2504
8 8 1 4195
9 3 2 789
10 3 99 1903
11 2 2 773
12 1 2 1183
13 1 1 1843
14 2 2 2956
15 99 3 1034
16 3 2 2118
17 4 2 4280
18 4 3 9072
19 3 1 1127
20 2 1 4950
21 97 1 727
22 97 1 1667
23 8 2 2970
24 9 99 2943
25 97 1 1351
.. .... ...... ......
(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 97 99 882
2 1 1 2174
3 99 2 2826
4 9 1 1382
5 2 2 1289
. .... ...... ......
(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,
"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)
})
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 *Not applicable *Not asked in survey 882
2 Conservatives England 2174
3 *Not asked in survey Scotland 2826
4 *Answer refused England 1382
5 Labour Scotland 1289
6 *Answer refused England 1810
7 Conservatives England 2504
8 *Don't know England 4195
9 Liberal Democrats Scotland 789
10 Liberal Democrats *Not asked in survey 1903
11 Labour Scotland 773
12 Conservatives Scotland 1183
13 Conservatives England 1843
14 Labour Scotland 2956
15 *Not asked in survey Wales 1034
16 Liberal Democrats Scotland 2118
17 Other Scotland 4280
18 Other Wales 9072
19 Liberal Democrats England 1127
20 Labour England 4950
21 *Not applicable England 727
22 *Not applicable England 1667
23 *Don't know Scotland 2970
24 *Answer refused *Not asked in survey 2943
25 *Not applicable England 1351
.. .................... .................... ......
(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 129 observations and 3 variables
vote region income
1 Conservatives England 2174
2 *Answer refused England 1382
3 *Answer refused England 1810
4 Conservatives England 2504
5 *Don't know England 4195
6 Conservatives England 1843
7 Liberal Democrats England 1127
8 Labour England 4950
9 *Not applicable England 727
10 *Not applicable England 1667
11 *Not applicable England 1351
12 Other England 2047
13 *Not applicable England 6042
14 Conservatives England 1589
15 *Answer refused England 5126
16 Conservatives England 1038
17 Other England 921
18 *Not asked in survey England 4981
19 Conservatives England 8243
20 Conservatives England 2155
21 Conservatives England 1280
22 *Not asked in survey England 3730
23 Conservatives England 689
24 *Not asked in survey England 1142
25 *Don't know England 2713
.. .................... ....... ......
(25 of 129 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 Percent
1 'Conservatives' 39 27.1 13.0
2 'Labour' 43 29.9 14.3
3 'Liberal Democrats' 39 27.1 13.0
4 'Other' 23 16.0 7.7
8 M 'Don't know' 44 14.7
9 M 'Answer refused' 34 11.3
97 M 'Not applicable' 44 14.7
99 M 'Not asked in survey' 34 11.3
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 Percent
1 'England' 129 50.8 43.0
2 'Scotland' 86 33.9 28.7
3 'Wales' 39 15.4 13.0
97 M 'Not applicable' 0 0.0
99 M 'Not asked in survey' 46 15.3
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: 364.000
Max: 13596.000
Mean: 2577.967
Std.Dev.: 2184.240
Skewness: 2.271
Kurtosis: 6.463
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 | Percent | |||||||
| 1 | 'Conservatives' | 39 | 27 | . | 1 | 13 | . | 0 | |
| 2 | 'Labour' | 43 | 29 | . | 9 | 14 | . | 3 | |
| 3 | 'Liberal Democrats' | 39 | 27 | . | 1 | 13 | . | 0 | |
| 4 | 'Other' | 23 | 16 | . | 0 | 7 | . | 7 | |
| 8 | M | 'Don't know' | 44 | 14 | . | 7 | |||
| 9 | M | 'Answer refused' | 34 | 11 | . | 3 | |||
| 97 | M | 'Not applicable' | 44 | 14 | . | 7 | |||
| 99 | M | 'Not asked in survey' | 34 | 11 | . | 3 | |||
- 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 | Percent | |||||||
| 1 | 'England' | 129 | 50 | . | 8 | 43 | . | 0 | |
| 2 | 'Scotland' | 86 | 33 | . | 9 | 28 | . | 7 | |
| 3 | 'Wales' | 39 | 15 | . | 4 | 13 | . | 0 | |
| 97 | M | 'Not applicable' | 0 | 0 | . | 0 | |||
| 99 | M | 'Not asked in survey' | 46 | 15 | . | 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: | 364 | . | 000 |
| Max: | 13596 | . | 000 |
| Mean: | 2577 | . | 967 |
| Std.Dev.: | 2184 | . | 240 |
| Skewness: | 2 | . | 271 |
| Kurtosis: | 6 | . | 463 |
- 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",..: NA 1 NA NA 2 NA 1 NA 3 3 ...
$ region: Factor w/ 3 levels "England","Scotland",..: NA 1 2 1 2 1 1 1 2 NA ...
$ income: num 882 2174 2826 1382 1289 ...
## Looking at the first 25 observations
DataFr[1:25,]
vote region income
1 <NA> <NA> 882
2 Conservatives England 2174
3 <NA> Scotland 2826
4 <NA> England 1382
5 Labour Scotland 1289
6 <NA> England 1810
7 Conservatives England 2504
8 <NA> England 4195
9 Liberal Democrats Scotland 789
10 Liberal Democrats <NA> 1903
11 Labour Scotland 773
12 Conservatives Scotland 1183
13 Conservatives England 1843
14 Labour Scotland 2956
15 <NA> Wales 1034
16 Liberal Democrats Scotland 2118
17 Other Scotland 4280
18 Other Wales 9072
19 Liberal Democrats England 1127
20 Labour England 4950
21 <NA> England 727
22 <NA> England 1667
23 <NA> Scotland 2970
24 <NA> <NA> 2943
25 <NA> England 1351
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 22 9 4
Labour 15 15 2
Liberal Democrats 18 15 3
Other 11 5 6
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 22 9 4
Labour 15 15 2
Liberal Democrats 18 15 3
Other 11 5 6
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 22 9 4
Labour 15 15 2
Liberal Democrats 18 15 3
Other 11 5 6
*Don't know 18 12 8
*Answer refused 15 7 8
*Not applicable 19 8 6
*Not asked in survey 11 15 2
For convenience, there is also a codebook method for data frames:
show_html(codebook(DataFr))
vote
| Storage mode: | integer |
| Factor with | 4 levels |
| Values and labels | N | Percent | |||||||
| 1 | 'Conservatives' | 39 | 27 | . | 1 | 13 | . | 0 | |
| 2 | 'Labour' | 43 | 29 | . | 9 | 14 | . | 3 | |
| 3 | 'Liberal Democrats' | 39 | 27 | . | 1 | 13 | . | 0 | |
| 4 | 'Other' | 23 | 16 | . | 0 | 7 | . | 7 | |
| NA | 156 | 52 | . | 0 | |||||
region
| Storage mode: | integer |
| Factor with | 3 levels |
| Values and labels | N | Percent | |||||||
| 1 | 'England' | 129 | 50 | . | 8 | 43 | . | 0 | |
| 2 | 'Scotland' | 86 | 33 | . | 9 | 28 | . | 7 | |
| 3 | 'Wales' | 39 | 15 | . | 4 | 13 | . | 0 | |
| NA | 46 | 15 | . | 3 | |||||
income
| Storage mode: | double |
| Min.: | 364 | . | 000 |
| 1st Qu.: | 1160 | . | 000 |
| Median: | 1960 | . | 000 |
| Mean: | 2580 | . | 000 |
| 3rd Qu.: | 3110 | . | 000 |
| Max.: | 13600 | . | 000 |
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("gles2013work.RData")
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 | 8 | . | 5 | |
| 2 | 'Bayern' | 507 | 13 | . | 0 | 13 | . | 0 | |
| 3 | 'Berlin' | 190 | 4 | . | 9 | 4 | . | 9 | |
| 4 | 'Brandenburg' | 212 | 5 | . | 4 | 5 | . | 4 | |
| 5 | 'Bremen' | 27 | 0 | . | 7 | 0 | . | 7 | |
| 6 | 'Hamburg' | 49 | 1 | . | 3 | 1 | . | 3 | |
| 7 | 'Hessen' | 232 | 5 | . | 9 | 5 | . | 9 | |
| 8 | 'Mecklenburg-Vorpommern' | 160 | 4 | . | 1 | 4 | . | 1 | |
| 9 | 'Niedersachsen' | 331 | 8 | . | 5 | 8 | . | 5 | |
| 10 | 'Nordrhein-Westfalen' | 619 | 15 | . | 8 | 15 | . | 8 | |
| 11 | 'Rheinland-Pfalz' | 150 | 3 | . | 8 | 3 | . | 8 | |
| 12 | 'Saarland' | 45 | 1 | . | 2 | 1 | . | 2 | |
| 13 | 'Sachsen' | 402 | 10 | . | 3 | 10 | . | 3 | |
| 14 | 'Sachsen-Anhalt' | 252 | 6 | . | 4 | 6 | . | 4 | |
| 15 | 'Schleswig-Holstein' | 131 | 3 | . | 3 | 3 | . | 3 | |
| 16 | 'Thueringen' | 271 | 6 | . | 9 | 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
Baden-Wuerttemberg 0 333
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 == :
conditions are not exhaustive
Warning in cases(postal.vote.list <- wave == 1 & intent.turnout == 6, 900 <-
wave == : conditions are not exhaustive
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.
When the code is run, some warnings are issued, that indicate that the
conditions are not exhaustive, that is, there are some observations for
which none of the conditions in the call cases() are met. The
corresponding elements of resulting vector will contain NA for these
observations. In the present case this occurs with observations that
have missing values in both intent.turnout and turnout.
After their creation, 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,
"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:
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
Linke 227 101
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.
