Introduction to the ‘memisc’ Package¶
This package collects an assortment of tools that are intended to make work with
R easier for the author of this package and are submitted to the public in the hope that they will be also be useful to others.
The tools in this package can be grouped into four major categories:
Data preparation and management
Presentation of analysis results
Data preparation and management¶
memisc provides facilities to work with what users from other packages like SPSS, SAS, or Stata know as ‘variable labels’, ‘value labels’ and ‘user-defined missing values’. In the context of this package these aspects of the data are represented by the
"missing.values" attributes of a data vector. These facilities are useful, for example, if you work with survey data that contain coded items like vote intention that may have the following structure:
Question: ‘’If there was a parliamentary election next tuesday, which party would you vote for?’‘
Liberal Democrat Party
Scottish Nation Party
British National Party
Not allowed to vote
Would not vote
Would vote, do not know yet for which party
A statistical package like SPSS allows to attach labels like ‘Conservative Party’, ‘Labour Party’, etc. to the codes 1,2,3, etc. and to mark mark the codes 96, 97, 98, 99 as ‘missing’ and thus to exclude these variables from statistical analyses.
memisc provides similar facilities. Labels can be attached to codes by calls like
labels(x) <- something and expendanded by calls like
labels(x) <- labels(x) + something, codes can be marked as ‘missing’ by calls like
missing.values(x) <- something and
missing.values(x) <- missing.values(x) + something.
memisc defines a class called “data.set”, which is similar to the class “data.frame”. The main difference is that it is especially geared toward containing survey item data. Transformations of and within “data.set” objects retain the information about value labels, missing values etc. Using
as.data.frame sets the data up for R’s statistical functions, but doing this explicitely is seldom necessary. See
More Convenient Import of External Data¶
Survey data sets are often relative large and contain up to a few thousand variables. For specific analyses one needs however only a relatively small subset of these variables. Although modern computers have enough RAM to load such data sets completely into an R session, this is not very efficient having to drop most of the variables after loading. Also, loading such a large data set completely can be time-consuming, because R has to allocate space for each of the many variables. Loading just the subset of variables really needed for an analysis is more efficient and convenient - it tends to be much quicker. Thus this package provides facilities to load such subsets of variables, without the need to load a complete data set. Further, the loading of data from SPSS files is organized in such a way that all informations about variable labels, value labels, and user-defined missing values are retained. This is made possible by the definition of
importer objects, for which a
subset method exists.
importer objects contain only the information about the variables in the external data set but not the data. The data itself is loaded into memory when the functions
as.data.set are used.
memisc also contains facilities for recoding survey items. Simple recodings, for example collapsing answer categories, can be done using the function
recode. More complex recodings, for example the construction of indices from multiple items, and complex case distinctions, can be done using the function
cases. This function may also be useful for programming, in so far as it is a generalization of
There is a function
codebook which produces a code book of an external data set or an internal “data.set” object. A codebook contains in a conveniently formatted way concise information about every variable in a data set, such as which value labels and missing values are defined and some univariate statistics.
An extended example of all these facilities is contained in the vignette “anes48”, and in
Tables and Data Frames of Descriptive Statistics¶
genTable is a generalization of
xtabs: Instead of counts, also descriptive statistics like means or variances can be reported conditional on levels of factors. Also conditional percentages of a factor can be obtained using this function.
In addition an
Aggregate function is provided, which has the same syntax as
genTable, but gives a data frame of descriptive statistics instead of a
By is a variant of the standard function
by: Conditioning factors are specified by a formula and are obtained from the data frame the subsets of which are to be analysed. Therefore there is no need to
attach the data frame or to use the dollar operator.
Presentation of Results of Statistical Analysis¶
Publication-Ready Tables of Coefficients¶
Journals of the Political and Social Sciences usually require that estimates of regression models are presented in the following form:
================================================== Model 1 Model 2 Model 3 -------------------------------------------------- Coefficients (Intercept) 30.628*** 6.360*** 28.566*** (7.409) (1.252) (7.355) pop15 -0.471** -0.461** (0.147) (0.145) pop75 -1.934 -1.691 (1.041) (1.084) dpi 0.001 -0.000 (0.001) (0.001) ddpi 0.529* 0.410* (0.210) (0.196) -------------------------------------------------- Summaries R-squared 0.262 0.162 0.338 adj. R-squared 0.230 0.126 0.280 N 50 50 50 ==================================================
Such tables of coefficient estimates can be produced by
mtable. To see some of the possibilities of this function, use
LaTeX Representation of R Objects¶
Output produced by
mtable can be transformed into LaTeX tables by an appropriate method of the generic function
toLatex which is defined in the package
utils. In addition,
toLatex methods for matrices and
ftable objects. Note that results produced by
genTable can be coerced into
ftable objects. Also, a default method for the
toLatex function is defined which coerces its argument to a matrix and applies the matrix method of
Looping over Variables¶
Sometimes users want to contruct loops that run over variables rather than values. For example, if one wants to set the missing values of a battery of items. For this purpose, the package contains the function
foreach. To set 8 and 9 as missing values for the items
knowledge3, one can use
foreach(x=c(knowledge1,knowledge2,knowledge3), missing.values(x) <- 8:9)
Changing Names of Objects and Labels of Factors¶
R already makes it possible to change the names of an object. Substituting the
dimnames can be done with some programming tricks. This package defines the function
rowrename that implement these tricks in a convenient way, so that programmers (like the author of this package) need not reinvent the weel in every instance of changing names of an object.
Dimension-Preserving Versions of
If a function that is involved in a call to
sapply returns a result an array or a matrix, the dimensional information gets lost. Also, if a list object to which
sapply are applied have a dimension attribute, the result looses this information. The functions
Sapply defined in this package preserve such dimensional information.
Combining Vectors and Arrays by Names¶
The generic function
collect collects several objects of the same mode into one object, using their names,
dimnames. There are methods for atomic vectors, arrays (including matrices), and data frames. For example
a <- c(a=1,b=2) b <- c(a=10,c=30) collect(a,b)
x y a 1 10 b 2 NA c NA 30
Reordering of Matrices and Arrays¶
memisc package includes a
reorder method for arrays and matrices. For example, the matrix method by default reorders the rows of a matrix according the results of a function.