Without a doubt, R is nibbling other programming languages that popularly used by data scientists or statisticians. Recently, most of statisticians use R to analyze data, fit models, do research. Moreover, a lot of practitioners also tend to use R due to its free, open source and active community full of great researchers. Researchers and many other soft engineers help R community to be organicaly developed. All those cooperations are helping R to become one of the greatest statistical programming language in the world. R Studio, REvolution, GGplot2, Shiny App etc. are a few great tools in this community which play big role to make this language great.
For people who want to study frontier statistical methodologies, R helps them to be able to focus on the implementation of statistical methods. One can easily implement new estimation method or algorithms for simulation or comparison purpose, meanwhile, one can easily develop R packages for the methods or models they proposed, which can be used by practionor, maybe right after the method is published. This cannot happen before when statistician know few about software developing and engineers also care less about statistical theories. However, by learning a few of simple R package developing knowledge, one can develop statistical package for their new methods so that people in industry can quickly apply research fruits to accelare their business or improve their decision making, just as simple as clicking button in SPSS. On the other side, practitioners can also develop package for their own projects, which makes their intellectual property reproducible for further using (they don’t necessarily need to publish their package to the public). Although open source may face some issues like infrequent maintenance or security, it makes the flow of knowledge seamless.
And it is easy to learn as learning how to use a package in R. Here is the tutorial: https://shiny.rstudio.com/tutorial/
R Studio is a very active community to enrich functions of R. There are several great features I want to emphisis and recommend.
Markdown is an elegant syntax for writers, no matter he or she is a novelist or even in social science or nature science fields, which need more sophisticated notations or graphical and numerical results. Its simple syntax allows writers to be able to focus on the essential part of knowledge sharing: the content and idea itself. Although LaTex is a great tool to generate beautiful document, researchers, unlike publisher, doesn’t really need to know much about a lot of tedious things like: formating, typesetting, referencing, and perfectly positioning statistical tables or figures. Because smooth writing process leads to fluent communication with readers, the advantage of Markdown language is obvious.
Here is Markdown Basics: http://rmarkdown.rstudio.com/authoring_basics.html And many other tutorials are available in the same website.
New version of R studio provides R Notebook feature that is similar to R markdown. Both of them provide an easy environment to test and iterate when writing article with code. You can run programs in the article and display whatever results you want to insert.
Shiny Web App provide a very important pipeline for statisticians and data scientists to interact with audience or customers. With this simple and user-friendly tool, those who are playing with data can directly interpret what they’ve found to those who are interested in. This process was used to be complicated and need the involvement of professionals in software developing or web developing. The Shiny App, however, simplify the process and provide a intuitive way to build interactive application upon statistical results. Therefore, it is a nice tool that is worthy to learn so that you can smoothly tranfer your idea into real product.
R
R Studio
Shiny App
R Markdown
The download and installation should be straightforward, in case you encounter problems you can check the following video tutorials.
Install R: http://www.youtube.com/watch?v=SJ9sVyqWJn8&hd=1
Install R Studio: http://www.youtube.com/watch?v=6aTRbo7kdGk&hd=1
RStudio is running based on R. It is an IDE (Intergrated Development Environment) with many advanced features. This lab notes is created based R Markdown, a very nice and useful tool from RStudio.
After you open RStudio, it should look like this: