Let’s begin by examining …

Background on the Origin of R: What is R?

R is an ever-expanding set of analytical packages that originated from a small group of programmers into a an ever-increasing programming, analytical, and graphical environment. Quoting from the r-project.org site, “The name [R] is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language ‘S’.”

R itself is freeware. On the positive side, it has wonderful analytical and plotting capabilities, and moderate data management capabilities. On the negative side, you get what you pay for, and as a consequence there are many curious idiosyncrasies found in R you will experience.

How Does R Relate To You and Your Research?

R has become the primary tool for data analysis in the broadly defined fields of ecology and conservation, as well as others. For better or worse, there are in excess of 8,750 R packages as of 15 August 2016 designed around particular analytical tools or processes. Many of these have direct relevance to the data you analyze in ecology and conservation. Most, unfortunately, do not, or are so narrow in scope they simply are not useful to your research. The trick to R is finding those packages that are useful.

I cannot, by any stretch of imagination, instruct you in even a small subset of the available R packages in this course. You have to develop a personal, web-based search pattern to ferret out a package that is relevant to the analyses you wish to perform. Or better yet, learn how to write your own code to solve your analytical problems.

Nonetheless, you will need to invest some measure of personal time above and beyond this web-based primer to learn R. In baseR I will instruct you in some of the more common R packages as they relate to data management and manipulation.

So How Will You Learn R?

baseR is one (the first) of 11 different workshops I have developed to help you analyze ecological and conservation data within the R environment. baseR consists of 4 modules that introduce you to the basics of R, ranging from how to find and load R onto your personal computer, to data management and manipulation. There are 18 exercises associated with these modules.

If you work through the baseR modules, and do the exercises, you will have an excellent start on how to use R in your research. I am, of course, available for consultation on sticky R issues, and you will encounter many a sticky issue. I myself do quite regularly. What I have done personally is develop a “Zen and the Art of R” sense that allows me to rapidly find clues for how to resolve a data management or statistical issue in R. This can only be learned by experimentation on your part; there is no “spoon-fed” answer to any aspect of R.

Where to Start - I am a Registered Student

If you are taking this as a registered course at Utah State University, you must start by sending an email to: Course Contact. Please provide your name, your student status (MS/PhD, degree program), and your university email address. I will return information for accessing the baseR.zip file.

Once the .zip file is downloaded, unpack the .zip file at any location you prefer on your CPU. Navigate to the powerpoints folder where you will find baseR_V201X.X.pdf. This is your self-learning set of modules for baseR. The .pdf of the powerpoints provides a step-by-step process for learning baseR. The .pdf is bookmarked by modules and sub-modules, and is searchable as well. You can also do the entire course from this website.

Information on the exercises and course evaluation procedures will also be returned to you via email.

Note that Module #2 includes instructions on how to download and begin R.

I Am Not a Registered Student, and All I Know About R Is That R Is the 18th Letter of the Alphabet …

It would be silly to pretend that learning R is simple. Most of us have been brought up in a MS Excel world. That is not the world of scientific analysis. You can, of course, try to analyze your research data within MS EXCEL, and there are many statistics books based on the use MS Excel for simple analyses. Several books link R with MS Excel. However, most of us will eventually be doing complex analyses well beyond the capabilities of MS EXCEL.

But given R has become the default platform for most analyses in ecological study, it is to your advantage to learn R. You can use this website as a means of learning R. If, as noted above, you work through the baseR modules, and do the exercises, you will have an excellent start on how to use R in your research.

I Am an Experienced R User …

Great! Then feel free to skim the exercises, code and examples in the website, as you see fit based on your personal level of experience. Hopefully parts of this site will refresh your memory. There may also be some tricks you can learn if you look the exercises over.

And if you have some R tricks you wish to share with me, please feel free to do so. I make no claim to be the Guru of R. You may also find yourself in the role of helping others.

Working Your through baseR

I am available for consultation about R questions, and prefer answering any questions via: Course Contact. I will try answer questions posed as soon as possible, but my work precludes any guarantee of timely response except to students who officially enrolled in the course.

END MODULE 1.1