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Using Views in R and comparing functions across multiple packages
R has almost 2923 available packages
This makes the task of searching among these packages and comparing functions for the same analytical task across different packages a bit tedious and prone to manual searching (of reading multiple Pdfs of help /vignette of packages) or sending an email to the R help list.
However using R Views is a slightly better way of managing all your analytical requirements for software rather than the large number of packages (see Graphics view below).
CRAN Task Views allow you to browse packages by topic and provide tools to automatically install all packages for special areas of interest. Currently, 28 views are available. http://cran.rproject.org/web/views/
Bayesian Bayesian Inference ChemPhys Chemometrics and Computational Physics ClinicalTrials Clinical Trial Design, Monitoring, and Analysis Cluster Cluster Analysis & Finite Mixture Models Distributions Probability Distributions Econometrics Computational Econometrics Environmetrics Analysis of Ecological and Environmental Data ExperimentalDesign Design of Experiments (DoE) & Analysis of Experimental Data Finance Empirical Finance Genetics Statistical Genetics Graphics Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization gR gRaphical Models in R HighPerformanceComputing HighPerformance and Parallel Computing with R MachineLearning Machine Learning & Statistical Learning MedicalImaging Medical Image Analysis Multivariate Multivariate Statistics NaturalLanguageProcessing Natural Language Processing OfficialStatistics Official Statistics & Survey Methodology Optimization Optimization and Mathematical Programming Pharmacokinetics Analysis of Pharmacokinetic Data Phylogenetics Phylogenetics, Especially Comparative Methods Psychometrics Psychometric Models and Methods ReproducibleResearch Reproducible Research Robust Robust Statistical Methods SocialSciences Statistics for the Social Sciences Spatial Analysis of Spatial Data Survival Survival Analysis TimeSeries Time Series Analysis To automatically install these views, the ctv package needs to be installed, e.g., via
install.packages("ctv") library("ctv")Created by Pretty R at insideR.org
and then the views can be installed via install.views or update.views (which first assesses which of the packages are already installed and uptodate), e.g.,install.views("Econometrics") update.views("Econometrics") Created by Pretty R at insideR.org
CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
Maintainer:  Nicholas LewinKoh 
Contact:  nikko at hailmail.net 
Version:  20091028 
R is rich with facilities for creating and developing interesting graphics. Base R contains functionality for many plot types including coplots, mosaic plots, biplots, and the list goes on. There are devices such as postscript, png, jpeg and pdf for outputting graphics as well as device drivers for all platforms running R. lattice and grid are supplied with R’s recommended packages and are included in every binary distribution. lattice is an R implementation of William Cleveland’s trellis graphics, while grid defines a much more flexible graphics environment than the base R graphics.
R’s base graphics are implemented in the same way as in the S3 system developed by Becker, Chambers, and Wilks. There is a static device, which is treated as a static canvas and objects are drawn on the device through R plotting commands. The device has a set of global parameters such as margins and layouts which can be manipulated by the user using par() commands. The R graphics engine does not maintain a user visible graphics list, and there is no system of double buffering, so objects cannot be easily edited without redrawing a whole plot. This situation may change in R 2.7.x, where developers are working on double buffering for R devices. Even so, the base R graphics can produce many plots with extremely fine graphics in many specialized instances.
One can quickly run into trouble with R’s base graphic system if one wants to design complex layouts where scaling is maintained properly on resizing, nested graphs are desired or more interactivity is needed. grid was designed by Paul Murrell to overcome some of these limitations and as a result packages like lattice, ggplot2, vcd or hexbin (on Bioconductor ) use grid for the underlying primitives. When using plots designed with grid one needs to keep in mind that grid is based on a system of viewports and graphic objects. To add objects one needs to use grid commands, e.g., grid.polygon() rather than polygon(). Also grid maintains a stack of viewports from the device and one needs to make sure the desired viewport is at the top of the stack. There is a great deal of explanatory documentation included with grid as vignettes.
The graphics packages in R can be organized roughly into the following topics, which range from the more user oriented at the top to the more developer oriented at the bottom. The categories are not mutually exclusive but are for the convenience of presentation:
 Plotting : Enhancements for specialized plots can be found in plotrix, for polar plotting, vcd for categorical data, hexbin (on Bioconductor ) for hexagon binning, gclus for ordering plots and gplots for some plotting enhancements. Some specialized graphs, like Chernoff faces are implemented in aplpack, which also has a nice implementation of Tukey’s bag plot. For 3D plots lattice, scatterplot3d and misc3d provide a selection of plots for different kinds of 3D plotting. scatterplot3d is based on R’s base graphics system, while misc3d is based on rgl. The package onion for visualizing quaternions and octonions is well suited to display 3D graphics based on derived meshes.
 Graphic Applications : This area is not much different from the plotting section except that these packages have tools that may not for display, but can aid in creating effective displays. Also included are packages with more esoteric plotting methods. For specific subject areas, like maps, or clustering the excellent task views contributed by other dedicated useRs is an excellent place to start.
 Effect ordering : The gclus package focuses on the ordering of graphs to accentuate cluster structure or natural ordering in the data. While not for graphics directly cba and seriation have functions for creating 1 dimensional orderings from higher dimensional criteria. For ordering an array of displays, biclust can be useful.
 Large Data Sets : Large data sets can present very different challenges from moderate and small datasets. Aside from overplotting, rendering 1,000,000 points can tax even modern GPU’s. For univariate datalvplot produces letter value boxplots which alleviate some of the problems that standard boxplots exhibit for large data sets. For bivariate data ash can produce a bivariate smoothed histogram very quickly, and hexbin, on Bioconductor , can bin bivariate data onto a hexagonal lattice, the advantage being that the irregular lines and orientation of hexagons do not create linear artifacts. For multivariate data, hexbin can be used to create a scatterplot matrix, combined with lattice. An alternative is to use scagnostics to produce a scaterplot matrix of “data about the data”, and look for interesting combinations of variables.
 Trees and Graphs : ape and ade4 have functions for plotting phylogenetic trees, which can be used for plotting dendrograms from clustering procedures. While these packages produce decent graphics, they do not use sophisticated algorithms for node placement, so may not be useful for very large trees. igraph has the TilfordRheingold algorithm implementead and is useful for plotting larger trees. diagram as facilities for flow diagrams and simple graphs. For more sophisticated graphs Rgraphviz and igraph have functions for plotting and layout, especially useful for representing large networks.
 Graphics Systems : lattice is built on top of the grid graphics system and is an R implementation of William Cleveland’s trellis system for SPLUS. lattice allows for building many types of plots with sophisticated layouts based on conditioning. ggplot2 is an R implementation of the system described in “A Grammar of Graphics” by Leland Wilkinson. Like lattice, ggplot (also built on top of grid) assists in trellislike graphics, but allows for much more. Since it is built on the idea of a semantics for graphics there is much more emphasis on reshaping data, transformation, and assembling the elements of a plot.
 Devices : Whereas grid is built on top of the R graphics engine, many in the R community have found the R graphics engine somewhat inflexible and have written separate device drivers that either emphasize interactivity or plotting in various graphics formats. R base supplies devices for PostScript, PDF, JPEG and other formats. Devices on CRAN include cairoDevice which is a device based libcairo, which can actually render to many device types. The cairo device is desgned to work with RGTK2, which is an interface to the Gimp Tool Kit, similar to pyGTK2. GDD provides device drivers for several bitmap formats, including GIF and BMP. RSvgDevice is an SVG device driver and interfaces well with with vector drawing programs, or R web development packages, such as Rpad. When SVG devices are for web display developers should be aware that internet explorer does not support SVG, but has their own standard. Trust Microsoft. rgl provides a device driver based on OpenGL, and is good for 3D and interactive development. Lastly, the Augsburg group supplies a set of packages that includes a Javabased device, JavaGD.
 Colors : The package colorspace provides a set of functions for transforming between color spaces and mixcolor() for mixing colors within a color space. Based on the HCL colors provided in colorspace, vcdprovides a set of functions for choosing color palettes suitable for coding categorical variables ( rainbow_hcl()) and numerical information ( sequential_hcl(), diverge_hcl()). Similar types of palettes are provided in RColorBrewer and dichromat is focused on palettes for colorimpaired viewers.
 Interactive Graphics : There are several efforts to implement interactive graphics systems that interface well with R. In an interactive system the user can interactively query the graphics on the screen with the mouse, or a moveable brush to zoom, pan and query on the device as well as link with other views of the data. rggobi embeds the GGobi interactive graphics system within R, so that one can display a data frame or several in GGobi directly from R. The package has functions to support longitudinal data, and graphs using GGobi’s edge set functionality. The RoSuDA repository maintained and developed by the University of Augsburg group has two packages, iplots and iwidgets as well as their Java development environment including a Java device, JavaGD. Their interactive graphics tools contain functions for alpha blending, which produces darker shading around areas with more data. This is exceptionally useful for parallel coordinate plots where many lines can quickly obscure patterns. playwith has facilities for building interactive versions of R graphics using the cairoDevice and RGtk2. Lastly, the rgl package has mechanisms for interactive manipulation of plots, especially 3D rotations and surfaces.
 Development : For development of specialized graphics packages in R, grid should probably be the first consideration for any new plot type. rgl has better tools for 3D graphics, since the device is interactive, though it can be slow. An alternative is to use Java and the Java device in the RoSuDA packages, though Java has its own drawbacks. For porting plotting code to grid, using the package gridBase presents a nice intermediate step to embed base graphics in grid graphics and vice versa.
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R Node and other Web Interfaces to R
R Node is a great web interface to R.
http://squirelove.net/rnode/doku.php
Features

Access to a R server backend via a web browser UI

The web browser UI works in all modern browsers, including IE 7 and 8 (excluding SVG based graphs).

Username/password login (both from the browser to the RNode server, and from the RNode server to Rserve and R).

Peruser R sessions. Each user can have their own R workspace, or they can share.


Support for most R commands that perform statistical analysis and provide textual feedback.

Support for most standard R commands that provide graphical feedback via server side generation of the graphs. Some graphs (e.g. plot() can be plotted via SVG clientside as well).

Downloading of generated graphs.

Accessing R help files using help() and ? commands (Note R v2.10 altered how help is provided, so this currently is not working in R v2.10)

Uploading files to work with their data in R.

Many commands will work. Try a command, if it does not work, use the feedback button in the application to let us know.
Limitations

Various R functions are not supported. These include:

Installation of new R packages.

Searching of help via ??.
 Example calls (via example()).

 First and now not so updated Rweb: Webbased Statistical Analysis Last Modified: 25Jun1999 JSS Paper (http://www.jstatsoft.org/v04/i01/
ROnline https://user.cs.tuberlin.de/~ulfi/cgibin/ronline/ronline.cgi(The official FAQ seems outdated )
 Rcgi (it is not clear if the project is still active as per official FQ) http://www.ms.uky.edu/~statweb/testR3.html
Rphp
RWui
http://sysbio.mrcbsu.cam.ac.uk/Rwui/
R.Rsp
http://cran.rproject.org/web/packages/R.rsp/index.html
RServe
http://www.rforge.net/doc/packages/Rserve/00Index.html
RPad
http://rpad.googlecode.com/svnhistory/r76/Rpad_homepage/index.html
CGIwithR
JSS paper Citation. CGIwithR: Facilities for processing Web forms using R. Journal of Statistical Software, 8(10), pp. 18, 2003.
A lecture on aspects of using CGI
R Apache
http://biostat.mc.vanderbilt.edu/rapache/
 Open Infrastructure for Outcomes with a live reporting module using RSessionDA
 Free statistics software Wessa server using R (see http://www.wessa.net/rwasp_arimaforecasting.wasp)
Wessa, P. (2011), Free Statistics Software, Office for Research Development and Education,
version 1.1.23r6, URL http://www.wessa.net/
 An impressive implementation of time series analysis based on R and Javascript. This web server creates separate browser windows for data entry, graphics, and procedure selection. These windows are dynamic. For example, after entering data there is no
submit
button to submit the data. The procedure selection window is used to start the analysis, which uses the current values in the data window.
 Online multivariate analysis and graphical displays from PBIL, Lyon
 An R web server for robust rankbased linear models
To make an interactive GUI in gWidgets can be as easy as creating the following script:
w < gwindow(’simple interactive GUI with one button’, visible=FALSE)
g < ggroup(cont=w)
b < gbutton(’click me’, cont=g, handler=function(h,...) {
gmessage(’hello world’, parent=b)
})
visible(w) < TRUE
A big and slightly outdated resource page from (which I used for some find and seek of resources)
http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse
AND
The famous site at http://www.yeroon.net/ggplot2/ (but no sharing of this site’s source code ,sigh!)
Thats all for now but watch this space its exciting (to watch AND code) 
Code Enhancers for R
This page lists code editors (or IDE)
https://rforanalytics.wordpress.com/codeenhancersforr/
Graphical User Interfaces for R
https://rforanalytics.wordpress.com/graphicaluserinterfacesforr/
ODBC /Databases for R
https://rforanalytics.wordpress.com/odbcdatabasesforr/
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 Rserve – TCP/IP interface to R – RoSuDa – Lehrstuhl für Rechnerorientierte Statistik und Datenanalyse – Universität Augsburg (stats.math.uniaugsburg.de)