Info for R users (Links, Manuals, Books, etc.)

[Tart]

Note: This original post has been modified to make it easier to find the resources in the thread and to compile them into one location. If you want to view the post as Tart left it click on the spoiler at the end of the thread

Hello All,
Please add to this post useful links, recommendation for books and so on. Thank you all.

Editors:

• RStudio - One of the best IDEs for working with R today. It is being developed at an amazing rate and new features are being added all the time.
• Emacs + ESS - If you're an Emacs user then using the ESS (Emacs Speaks Statistics) plugin is a natural choice. Emacs is a very powerful editor and ESS adds support for R syntax and easily allows you to create a buffer for an R session.
• Rkward - It is available in the repositories for most Linux distros. You can run it on Windows and/or Macs but takes a little bit more work.
• Tinn-R (Windows) - The former king. It seems to be out of fashion now a days.

There is more to add but I haven't got around to updating this post yet. View the spoiler for more.

Spoiler:

Hello All,
Please add to this post useful links, recommendation for books and so on. Thank you all.

These are some links if you are just starting with R I hope they will help you, others may find them useful too.

EDITORS:
http://www.sciviews.org/Tinn-R/ - Tinn-R (Windows)
http://rkward.sourceforge.net/ - Rkward (Linux). It is available in repositories if you are running Ubuntu.


MANUALS

http://cran.r-project.org/manuals.html - The Official R Manuals
http://cran.r-project.org/other-docs.html - Contributed Documentation. Contains wealth of documentation. This is the second step after official introduction.

http://www.rpad.org/Rpad/R-refcard.pdf - very nice reference card, slightly updated version from the link in contributed documentation.

Following two links are stolen from TheEcologist post :-)
http://www.splusbook.com/Rintro/RCourse.pdf
http://people.su.se/~ma/R_intro/

LINKS:
http://zoonek2.free.fr/UNIX/48_R/all.html - statistics with R. This is extremely useful site, I learned many useful tricks there.
http://addictedtor.free.fr/graphiques/ - R Graph Gallery. Very nice collection of graphs.
http://cged.genes.nig.ac.jp/RGM2/index.php – R Graphical Manuals. A collection of R graphics from all packages.
http://www.rseek.org/ - search engine for R functions, lists and more

http://news.gmane.org/index.php?pref...ne.comp.lang.r - Mailing list in web format

The R Inferno
http://www.burns-stat.com/pages/Tutor/R_inferno.pdf
"If you are using R and you think you're in hell, this is a map for you.". Nice manual.

[vinux]

Nice one.
Couple of links for beginners..
1. http://www.unt.edu/rss/Teaching-with-R.pdf ("Using the R Statistical Computing Environment to Teach Social Statistics Courses" : from Wiki)
2. http://en.wikipedia.org/wiki/R_programming ( See the reference )

3. http://cran.r-project.org/doc/contri...ni-SimpleR.pdf ( Introductory Statistics: Nice One)

[mp83]

I'd recommend “An Introduction to R: Software for Statistical Modelling & Computing” by Petra Kuhnert and Bill Venables

> http://cran.r-project.org/doc/contri...urse_Notes.zip

(including scripts to reproduce the notes!)

and of course “Practical Regression and Anova using R” by Julian Faraway

> http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf

I'll be back with a really good book on time series with R (not like the Springer's)

[TheEcologist]

Excellent Idea! But lets not forget:

Dan Bailey and Matt Nunes at the University of Bristol and their "idiot’s guide" to builing packages in R.

http://www.maths.bris.ac.uk/~maman/c...Rpackages.html

EDIT link is dead: use this link for the above:
http://web.archive.org/web/200806191...Rpackages.html

A webpage to search the R-Help archives (so no need to keep the digests anymore like I still do).

http://finzi.psych.upenn.edu/search.html

And Ofcourse

Last but not least... the one, the only...

The R Graph Gallery!!

http://addictedtor.free.fr/graphiques/

(edit: Oeps Tart already added it I missed that, but its worth the emphasis anyway )

[mp83]

So we have

"Analysis of Financial Market Data (Financial Econometrics)"
> http://www.math.uncc.edu/~zcai/econ6219.html

Playing around his teching page you'll find good stuff (math. stats mostly)
> http://www.math.uncc.edu/~zcai/teach.html

By the way Rkward seems really cool! Sad for us win-users..

EDIT

http://zoonek2.free.fr/UNIX/48_R/all.html - statistics with R. It has BMA in chapter 11!!!?****ing awesome!

[nelsonpa]

Getting started in R, I've found Michael J. Crawley's "Statistical Computing, An Introduction to Data Analysis using S-Plus" very helpful. Crawley's newer "The R Book" may be better for R users, though I haven't seen it. Cheers, Pete

[Tart]

Colors in R.
I struggled with finding right colors for my charts. I found this wonderful "Chart of R Colors" and it was a tremendous help.

http://research.stowers-institute.or...hart/index.htm

http://research.stowers-institute.or...ColorChart.pdf - this is PDF version.

[Tart]

Some books.

"Modern Applied Statistics with S" by Venables and Ripley - classic, must have for everyone.

If you are doing linear modeling then I think that flowing two books are a must have.
"Linear Models with R" and "Extending the Linear Models with R" by Faraway. Books very well written, full of examples, advices, recommendations and tricks.

"Data Analysis using Regression and Multilevel/Hierarchical Models" by Gelman and Hill. I just started reading this book, this is a book about multilevel modeling, but all examples are in R. Authors show you how to apply theory in R. Really nice.

If you are working with graphics, then this book will help you a lot
"R Graphics" by Murrell. It doesn't tell you how to create certain plot. It describes how graphics works. Especially tricky lattice/Trellis graphics.

"Statistical Computing with R" by Rizzo this book covers statistical methods using R quite well. It is not specialized, just gives overview of essential methods.

"Data Manipulation with R (Use R)" by Spector - this one on my wish list. It looks like another must have :-). UPDATE: I have this book now, and it is very useful. Definitely mast have.

[TheEcologist]

Resources to help you learn R
http://www.ats.ucla.edu/stat/r/

Whole Page full of R-tips
http://pj.freefaculty.org/R/statsRus.html

Need mathematical notation/mathematical expressions in your graphs? try

help(plotmath)

Example

x <- seq(-4, 4, len = 101)
y <- cbind(sin(x), cos(x))
matplot(x, y, type = "l", xaxt = "n",
        main = expression(paste(plain(sin) * phi, "  and  ",
                                plain(cos) * phi)),
        ylab = expression("sin" * phi, "cos" * phi), # only 1st is taken
        xlab = expression(paste("Phase Angle ", phi)),
        col.main = "blue")
axis(1, at = c(-pi, -pi/2, 0, pi/2, pi),
     labels = expression(-pi, -pi/2, 0, pi/2, pi))

[mp83]

A nice exposition article on using R

"Use of R as a Toolbox for Mathematical Statistics Exploration"

[Tart]

I just discovered very interesting blog dedicated to R. Strongly recommend to anyone.

http://blog.revolution-computing.com/

[bugman]

Interesting. Thanks Tart

[Tart]

http://www.jstatsoft.org/v27/i03/paper - link to paper dedicated to Automatic Time series forecasting using Rob Hyndman's "forecasting" package

[Tart]

http://rattle.togaware.com/ - Rattle: Gnome Cross Platform GUI for Data Mining using R. Didn't play with it yet, but it looks interesting.

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.

Through a simple and logical graphical user interface based on Gnome, Rattle can be used by itself to deliver data mining projects. Rattle also provides an entry into sophisticated data mining using the open source and free statistical language R.

Rattle runs under GNU/Linux, Macintosh OS/X, and MS/Windows. The aim is to provide an intuitive interface that takes you through the basic steps of data mining, as well as illustrating the R code that is used to achieve this. Whilst the tool itself may be sufficient for all of a user's needs, it also provides a stepping stone to more sophisticated processing and modelling in R itself, for sophisticated and unconstrained data mining.

[mp83]

Soooo good!

> onertipaday.blog-spot.com
> addictedtor.free.fr/graphiques
> statisticsr.blog-spot.com
> statisticsr.blog-spot.com

Note : Delete the "-" at the blog-spots...