R Programming – Help, How-To’s, and Examples

Help with R Programming

Finding R programming frustrating? RProgramming.net can help! Go from learning R to using R with examples, tips, code, help, and how-to’s.

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R Programming

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Wondering if You Should Learn R?

Here’s a video from Courtney Brown that may convince you to try R programming:

What is R?

R is a free, open source statistical programming language that is available for Windows, Mac and Linux. It consists of the base or core R software as well as add-on packages that extend functionality. R is based on the language S which was developed in the 1970s primarily by John Chambers, Rick Becker and Allan Wilks. In the 1990s Ross Ihaka and Robert Gentleman created R and shortly after made it freely available under the GNU General Public License. R continues to be maintained by the R Development Core Team and major releases are published about twice per year.

What Software is Similar to R?

R is comparable to SAS, SPSS and Stata.

What is R Used For?

R can be used for statistical analysis, graphics, and reporting. R can be used to manipulate data, run statistical analyses such as descriptive statistics, t-tests, regressions, and produce charts. R can even be used to make maps and play minesweeper.

Why You Should Use R

R has a number of advantages over comparable commercial software packages. The first and most obvious advantage is that it is free. For students and anyone on a budget this can be a major advantage.

Another related advantage is that you can install it on as many computers as you want to. Want to install R on two computers in the office, your home desktop and your laptop? No problem, R is free! No complicated and expensive multi-computer license is needed.

A third advantage is that R is being used at many universities and businesses. This means that R users are able to collaborate and learn from other R users and that there are many opportunities for those who can program in R.

One final advantage of R is that, because anyone can write an add-on package for it, R has the most advanced analyses and is always being added to. If the function you want isn’t available you can write your own package and share it with the world.

Why You Should Not Use R

R has a steep learning curve if you don’t have computer programming experience. This is because R has a very limited graphical user interface. R programming is done almost exclusively through code. R’s dependence on programming can be both a benefit and a drawback. It is a benefit because methods are very repeatable with little extra effort. As you program in R the code produced is both documentation and a method for easily repeating the process. It is a drawback because learning the code can be very time consuming and frustrating. In the early stages of R programming many users have difficulties figuring out why their code won’t work the way they want it to. Like any computer program, R requires the code to be free of errors or it won’t work (and won’t tell you why not!).

Another problem with R is that there are few resources for learning the basics. There are many websites devoted to cutting edge analyses and graphics in R but very few that show how to manipulate data and conduct basic analyses. RProgramming.net hopes to help address this problem.

What Support is Available for R Programming?

R is supported by an enthusiastic group of users and developers. If you provide a well-formed question and a reproducible example, you are likely to get a solution to your R programming problem. There are many websites devoted to R programming, like this one, as well as R mailing lists with thousands of subscribers. Stackoverflow is another place to look for code and ask R questions. Finally, there are many books on R programming.

R and Reproducible Research

One benefit to R is its capacity for reproducible research. If you aren’t familiar with reproducible research, it’s the concept of providing your data and analysis (code) to an interested party so that they can reproduce your results. R is ideal for this purpose because an R programmer can easily write code that pulls data, analyzes it, and produces a PDF, HTML, or slideshow file for reporting all in one. When another party is interested, the original author can simply share the data and code.

Another benefit to creating reproducible research is that projects can easily be repeated when new data is available. If you have created a report based on online survey data you can run the report at any time to see updated results. If you have created a slideshow presentation that management wants to see every quarter you can easily run the code again at that time.

Alternatives to R

Looking for an easier free statistical software package? Check out FreeStatisticalSoftware.com for information on PSPP, a free, open-source competitor to SPSS.

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