Introduction to Data Science Using R Runestone Version 1

If you are new to computers, programming, and/or data science, welcome to an exciting chapter that will open the door to the most powerful free data analytics tool ever created anywhere in the universe, no joke. On the other hand, if you are experienced with spreadsheets, statistical analysis, or accounting software you are probably thinking that this book has now gone off the deep end, never to return to sanity and all that is good and right in user interface design. Both perspectives are reasonable. The "R" open source data analysis programming language is immensely powerful, flexible, and especially "extensible" (meaning that people can create new capabilities for it quite easily).

At the same time, R is "commandline" oriented, meaning that most of the work that one needs to perform is done through carefully crafted text instructions, many of which have tricky syntax (the punctuation, grammar, and related rules for making a command that works). In addition, R is not especially good at giving feedback or error messages that help the user to repair mistakes or figure out what is wrong when results look funny.

But there is a method to the madness here. One of the virtues of R as a teaching tool is that it hides very little. The successful user must fully understand what the "data situation" is or else the R commands will not work. With a spreadsheet, it is easy to type in a lot of numbers and a formula like =FORECAST() and a result pops into a cell like magic, whether it makes any sense or not. With R you have to know your data, know what you can do with it, know how it has to be transformed, and know how to check for problems. Because R is a programming language, it forces users to think about problems in terms of data objects, methods that can be applied to those objects, and procedures for applying those methods. These are important metaphors used in modern programming languages, and no data scientist can succeed without having at least a rudimentary understanding of how software is programmed, tested, and integrated into working systems. The extensibility of R means that new modules are being added all the time by volunteers: R was among the first analysis programs to integrate capabilities for drawing data directly from the Twitter® social media platform. So you can be sure that whatever the next big development is in the world of data, that someone in the R community will start to develop a new "package" for R that will make use of it. Finally, the lessons one learns in working with R are almost universally applicable to other programs and environments. If one has mastered R, it is a relatively small step to get the hang of other statistical languages like SAS® statistical programming language or SPSS®. (SAS and SPSS are two of the most widely used commercial statistical analysis programs). So with no need for any licensing fees paid by school, student, or teacher it is possible to learn the most powerful data analysis system in the universe and take those lessons with you no matter where you go. It will take a bit of patience though, so please hang in there!

Joseph J. Allaire is an entrepreneur, software engineer, and the originator of some remarkable software products including "ColdFusion," which was later sold to the web media tools giant Macromedia and Windows Live Writer, a Microsoft blogging tool. Starting in 2009, Allaire began working with a small team to develop an open source program that enhances the usability and power of R. It is called R-Studio.