The R system has several different flavours of graphics. These
include: Base or traditional graphics; Lattice's highly stylized
graphics; Grid graphics on which lattice is built; and the ggplot2
implementation of Wilkinson's Grammar of Graphics. For
three-dimensional rotational graphics, note rgl and the
dynamical graphical abilities of the rggobi interface to GGobi. Supplement and summary notes (28pp.) Code for the examples. Overheads - lattice
Rattle (the R Analytical Tool To Learn Easily)
is a data mining toolkit used to analyse very large collections of
data. Rattle presents statistical and visual summaries of data,
transforms data into forms that can be readily modelled, builds both
unsupervised and supervised models from the data, presents the
performance of models graphically, and scores new datasets. Overheads
Many real data sets have a hierarchical multi-level structure of
variation; for example multiple measurements within trees within
stands within forests. The modeling approaches and that have been
developed for such data provide a rich source of insights and
challenges. We will showcase the nlme and lme4 packages, both of
which provide extensive infrastructure for the analysis of multi-level
data. The lme4 package is under very active continuing development,
with new features and improvements appearing at regular intervals.
Overheads
Topics include:
i. graphic presentation of meta analysis results
with, ii. coin package for use with non-parametric testing and
power computations, with comparisons with bootstrap procedures. Notes Overheads
Sweave' provides a flexible framework for mixing text and S code [R
implements the S language] for automatic report generation (for
example, to enable reproducible research). The basic idea is to
place R code into the LaTeX/Office document, and ask R to replace
the code with its output, such that the final document only
contains the text and the output of the statistical analysis.
Currently, there is provision for incorporating S code, with markup,
into either LaTeX or Open Office documents. The S code gets
replaced by its output (text, tables and/or graphs) in the final
markup file. This makes it possible to re-generate a report if the
input data changes. It documents code that can reproduce the
analysis in the same file that also produces the report.
Where published papers report statistical analyses and/or summaries,
it is too often hard to be sure just what analysis was done.
Reference to an Sweave version (typically on a web page) documents
the analysis to a standard and with a completeness that is not
otherwise possible. Notes Overheads
The BRugs package facilitates Bayesian statistical analyses
through the use of scripts; i.e. without the need for menus and
mouse-clicks. Scripting in both R and the BUGS (Bayesian inference
Using Gibbs Sampling) languages is required. Other than time, there is
no firm limit on the complexity of Bayesian models that can be handled
with BRugs. Because R is used at the front-end and back-end of the
analysis one can take advantage of R's functionality for data input
and pre-processing, as well as summary and graphical display. This
component of the short course will provide illustrations at both
introductory and advanced levels.
There are three main kinds of spatial data:
geostatistical data, where the response variable is recorded at a point
location (e.g. daily temperature records at a set of weather stations);
regional data, where the response variable is obtained from a spatial
region (e.g. number of HIV notifications in each health authority area);
and spatial point patterns, where the response is the location of an event
(e.g. locations of petty crimes in Chicago). The R packages 'geoR',
'spdep' and 'spatstat' (respectively) provide functionality for these
types of data. Overheads
Much of the power and flexibility of R derives from
the large variety of powerful packages that are available to add on
to the base system. Putting code into R packages is surprisingly
straightforward, for a user who is careful to follow the rules.
Notes