Faraway, J. Linear Models with R. Fox, J. Muenchen, R. Springer Series in Statistics and Computing. Pinheiro, J. Venables, W. Modern Applied Statistics with S. Fourth Edition. Zuur, A. A Beginner's Guide to R. Use R. Ihaka, R. R: A language for data analysis and graphics.
Other newer graphics systems, like lattice and ggplot2 allow for complex and sophisticated visualizations of high-dimensional data. R has maintained the original S philosophy, which is that it provides a language that is both useful for interactive work, but contains a powerful programming language for developing new tools.
This allows the user, who takes existing tools and applies them to data, to slowly but surely become a developer who is creating new tools. Finally, one of the joys of using R has nothing to do with the language itself, but rather with the active and vibrant user community. In many ways, a language is successful inasmuch as it creates a platform with which many people can create new things.
R is that platform and thousands of people around the world have come together to make contributions to R, to develop packages, and help each other use R for all kinds of applications. The R-help and R-devel mailing lists have been highly active for over a decade now and there is considerable activity on web sites like Stack Overflow. According to the Free Software Foundation, with free software , you are granted the following four freedoms.
The freedom to study how the program works, and adapt it to your needs freedom 1. Access to the source code is a precondition for this.
The freedom to improve the program, and release your improvements to the public, so that the whole community benefits freedom 3. CRAN also hosts many add-on packages that can be used to extend the functionality of R. When you download a fresh installation of R from CRAN, you get all of the above, which represents a substantial amount of functionality.
However, there are many other packages available:. There are over packages on CRAN that have been developed by users and programmers around the world. There are also many packages associated with the Bioconductor project. People often make packages available on their personal websites; there is no reliable way to keep track of how many packages are available in this fashion.
There are a number of packages being developed on repositories like GitHub and BitBucket but there is no reliable listing of all these packages. No programming language or statistical analysis system is perfect. R certainly has a number of drawbacks. For starters, R is essentially based on almost 50 year old technology, going back to the original S system developed at Bell Labs. Another commonly cited limitation of R is that objects must generally be stored in physical memory.
This is in part due to the scoping rules of the language, but R generally is more of a memory hog than other statistical packages.
However, there have been a number of advancements to deal with this, both in the R core and also in a number of packages developed by contributors. Also, computing power and capacity has continued to grow over time and amount of physical memory that can be installed on even a consumer-level laptop is substantial. While we will likely never have enough physical memory on a computer to handle the increasingly large datasets that are being generated, the situation has gotten quite a bit easier over time.
The capabilities of the R system generally reflect the interests of the R user community. As the community has ballooned in size over the past 10 years, the capabilities have similarly increased. When I first started using R, there was very little in the way of functionality for the physical sciences physics, astronomy, etc. However, now some of those communities have adopted R and we are seeing more code being written for those kinds of applications.
If you want to know my general views on the usefulness of R, you can see them here in the following exchange on the R-help mailing list with Douglas Bates and Brian Ripley in June Roger D. Before proceeding with this tutorial, you should have a basic understanding of Computer Programming terminologies.
A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track. Previous Page Print Page.
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