# Introduction to the ‘memisc’ Package¶

## Description¶

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
- Data analysis
- Presentation of analysis results
- Programming

## Data preparation and management¶

### Survey Items¶

`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
`"description"`

, `"labels"`

, and `"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?’’

1 |
Conservative Party |

2 |
Labour Party |

3 |
Liberal Democrat Party |

4 |
Scottish Nation Party |

5 |
Plaid Cymru |

6 |
Green Party |

7 |
British National Party |

8 |
Other party |

96 |
Not allowed to vote |

97 |
Would not vote |

98 |
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 `data.set`

.

### 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 `subset`

or
`as.data.set`

are used.

### Recoding¶

`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 `ifelse`

.

### Code Books¶

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
`demo(anes48)`

## Data Analysis¶

### 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 `table`

object.

### Per-Subset Analysis¶

`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 `example(mtable)`

.

### 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, `memisc`

defines `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 `toLatex`

.

## Programming¶

### 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 `knowledge1`

, `knowledge2`

, `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
`names`

or `dimnames`

can be done with some programming tricks. This package defines
the function `rename`

, `dimrename`

, `colrename`

, and `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 `lapply`

and `sapply`

¶

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 `lapply`

or `sapply`

are applied have a dimension attribute, the result looses this information.
The functions `Lapply`

and `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, `rownames`

, `colnames`

and/or `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)
```

leads to

```
x y
a 1 10
b 2 NA
c NA 30
```

### Reordering of Matrices and Arrays¶

The `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.