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Résine
R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R. My goal is to help you quickly access this language in your work.
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I assume that you are already familiar with the statistical methods covered and instead provide you with a roadmap and the code necessary to get started quickly, and orient yourself for future learning. I designed this web site to be an easily accessible reference. Look at the sitemap to get an overview.
If you prefer an online interactive environment to learn R, this free R tutorial by DataCamp is a great way to get started. If you're already somewhat advanced and interested in machine learning, try this Kaggle tutorial on who survived the Titanic.
R/gachalifecringe
What's New
A link to the new resource The R Graph Gallery has been added.
A number of new sections have been added. These include:
- A new section on time series analysis.
- A new section on ggplot2 graphics.
- For old friends, please note that I've renamed the section on trellis graphs to lattice graphs. Since both the lattice and ggplot2 packages can be used to create trellis graphs, changing the name makes the distinction between these two sections clearer.
Why Use R?
If you currently use another statistical package, why learn R?
- It's free! If you are a teacher or a student, the benefits are obvious.
- It runs on a variety of platforms including Windows, Unix and MacOS.
- It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner.
- It contains advanced statistical routines not yet available in other packages.
- It has state-of-the-art graphics capabilities.
Obtaining R
R is available for Linux, MacOS, and Windows (95 or later) platforms. Software can be downloaded from one of the Comprehensive R Archive Network (CRAN) mirror sites.
Feedback
I constantly strive to improve these pages. Feedback and suggestions are always welcome!
- Rob Kabacoff
Introduction to R
R/memes
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
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. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.
The R environment
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
- an effective data handling and storage facility,
- a suite of operators for calculations on arrays, in particular matrices,
- a large, coherent, integrated collection of intermediate tools for data analysis,
- graphical facilities for data analysis and display either on-screen or on hardcopy, and
- a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
R/wallstreetbets
Many users think of R as a statistics system. We prefer to think of it as an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.
R&b Music
R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.