Advantages of R Programming

R includes an effective data handling and storage facility, a developed and effective programming language which includes input and output facilities, conditional loops, a suite of operators for calculations for different arrays such as matrices. But most important it has a large and integrated collection of intermediate tools for data analysis, which is a plus in comparison to other software that provide the same characteristics or better options as some reviewers think( for example Python- also an open source language to perform data science tasks).

After I have done my research and read articles or passages in books and users’ reviews on blogs, I have discovered that the R has a lot of advantages according to reviewers’ opinions, but it has disadvantages also. I have never used it, as at the University we have done statistical analysis in Excel, which I think it is a good option considering the teachers’ preferences for the level of our routine statistical analysis. R is for another level of analysis, it is used by economists, scientists, but it can be used by students. It is more difficult to use than Excel because we don’t have a framework on it, it is not studied in High Scholl, but there are books for novices such as “Data mining with rattle and R” by Graham Williams. As many reviews find using R rather easy than difficult, I think that it can be approached by teachers in academic institutions and it will be a good start for us to learn to use a software that companies all over the world find efficient.

Further on I will continue to present the advantages of using the R statistics course, which I find very attractive. R is free and an open source software which allows anyone to use it and respectively to modify it. R it is licensed under the GNU General Public License, with no license restrictions so that anyone can use it. R can be installed on many operating systems like: UNIX and derivatives including Darwin, Mac OS X, Linux, FreeBSD, Solaris and Microsoft Windows. R can work on objects with no limitations in size and complexity, offering a clear vision and statistical reference using a clear language. It has the advantage of being the product of international collaboration between top computational statisticians and all source code is published, consequently any user has access to the exact algorithms used, also the code can be corrected by statisticians, experts in their field. It is fully programmable, which means that any repetitive procedure can be automated by user written scripts. The user has the chance to create its own functions, or to personalize the existing ones.

Another advantage of using R, which I find important, is that it allows the user to do statistical analysis and have visualization for any sophisticated computation. The user is not restricted to a small set of methods to make analysis, as R has a lot of contributed packages from CRAN (The Comprehensive R Archive Network).

In comparison with Excel, R has a lot of advantages, mainly the ones written above. But from my point of view, the main plus that R has is about graphics and data visualization. As a result, it has excellent tools for creating graphics, from staples like bar charts and scatterplots to multi-panel Lattice charts to brand new graphics of your own devising. For example R's graphical system is influenced by thought leaders in data visualization like Bill Cleveland and Edward Tufte, therefore these graphics appear regularly in venues like the New York Times and the Economist. Moreover it makes users to develop critical thinking about problem- solving rather than “push the button mentality”. (D. G. Rossiter)
R provides a wide variety of statistical linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, graphical techniques, being 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.

Many users think of R as a statistics system, but the correct word that defines it is environment. R is an environment where statistical formulae and techniques can be implemented and enhanced over time by any user. R has its own LaTex- like documentation format which is used to generate comprehensive documentation, online in a number of formats and hardcopy.