1. R Programming

2. What is R?
R is a language and environment for statistical computing and graphics
R is based on the S language originally developed by John Chambers and colleagues at AT&T Bell Labs in the late 1970s and early 1980s
R is the most recent and full-featured implementation of the S language
R (sometimes called “GNU S" ) is free open source software licensed under the GNU general public license (GPL 2)
R development was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand
R is formally known as The R Project for Statistical Computing
GNU is an extensive collection of computer software that can be used to build a Unix-like operating system. GNU is composed wholly of free software. GNU is an acronym for "GNU's Not Unix!", chosen because GNU's design is Unix-like, but differs from Unix by being free software and containing no Unix code.

3. R Foundation
The R Foundation is the non-profit organization located in Vienna, Austria which is responsible for developing and maintaining R
Hold and administer the copyright of R software and documentation
Support continued development of R
Organize meetings and conferences related to statistical computing

4. What is R great at? Data manipulation Data analysis
Statistical modeling
Data visualization
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. 

5. R Environment
Our introduction to the R environment did not mention statistics, yet many people use R as a statistics system. We prefer to think of it of an environment within which many classical and modern statistical techniques have been implemented.
A few of these are built into the base R environment, but many are supplied as packages.
There are about 25 packages supplied with R (called “standard” and “recommended” packages) and many more are available through the CRAN family of Internet sites (via and elsewhere.

6. R Packages All R functions are stored in packages
The standard R distribution includes core packages and recommended packages:
Core R packages base, utils, stats, methods, graphics, grDevices, datasets
Recommended packages boot, rpart, foreign, MASS, cluster, Matrix, etc.
Additional packages can be downloaded through the R GUI or via the install.packages function
When R is initially loaded, only core R packages are loaded by default
Additional packages are loaded via the library command
Packages datasets are made accessible via the data command

7. Official web-site: www.rstudio.com
R Studio is a free and open source integrated development environment (IDE) for R, a programming language for statistical computing and graphics.  
R Studio is written in the C++  programming language.
Work on R Studio started at around December 2010, and the first public BETA version (v0.92) was officially announced in February 2011.
Official web-site:
RStudio is available in open source and commercial editions and runs on the desktop (Windows, Mac, and Linux) or in a browser connected to RStudio Server or RStudio Server Pro (Debian/Ubuntu, RedHat/CentOS, and SUSE Linux).

11. R Installation: for Windows
Check that User Name is in English. If it is in Arabic, create a new User Name in Latin letters. Make installation, having logged in Latin.
Install classical R 
Install R-studio
Open R-studio. In menu Tools --- Global Options --- Sweave --- "Weave .Rnw files using" choose knitr.
Install all packages necessary for our course. Download file: install_all.R. Open it in R-studio (File --- Open file). Run file by choosing Code --- Source with Echo. You need Internet connection.
Note, red lines do not mean error. Errors are only when you evidently see a message Error. Wait until the red sign in the upper right corner in console disappear.

12. Notes for Windows
When you install R, R-studio for Windows it is recommended to switch off antivirus.
If you have questions, consult your Course Instructor.