Posts order

I recommend you read the posts in the following order:

• [Intro 01 - R and RStudio]
• [Intro 02 - R Studio]
• [Intro 03 - save your data]
• [Intro 04 - scripts and libraries/packages]
  and only after that go into the other posts. For those posts the order is not set in stone, and most of all are written so that they can be read independently from each other.

While the posts above are titled very creatively so that you cannot mistake their order, it is a measure of good principle when doing things the first time to provide as many iterations as possible. Here is me, again, advising your to follow the suggested posts order, unless you’re already a Master JediR.

These few posts are a bit of time consuming (albeit not that much) but this type of introductory information is so very important! A surgeon, a soldier, a farmer, you name it, is only as proficient as his tools allows it. We, the scientists, are no different. We may understand the principles, but our results are made possible also by the tools we use. And that’s why is so important to allot just a bit of time to your tools.

How to read this blog

First, this is not a real blog. It’s just a collection of very brief, very basic, and most likely, very incomplete posts that cover (scarcely) various and apparently disparate aspects of R, R Studio, R libraries/packages, actions for data manipulation, statistical procedures, and data processing for learning analytics.

About the posts being incomplete. Then why posting anything in the first place?

The main purpose of this collection of posts is to support a 3-5 hours workshop on learning analytics. The participants are expected to have some computer literacy, including some familiarity with computational tools (not unlike R, for instance). However, it is not unreasonable that someone is well-versed in statistics, but has little or no experience with R or R Studio.

It is these participants, with little or no experience with R and/or R Studio, that this website tries to accomodate and guide along our steps through the learning analytics seminary. For the R literate ones, this website is more or less a mere outline of the steps we need to take while progressing through our seminary. If you are familiar with R and R Studio, you can safely skip the introductory tutorials, but if not, I highly recommend you try to understand them.

About the exercises in the posts

Applying hands on a certain procedure (say, an algorithm) is certain to enhance a person’s familiarity and use of said procedure (algorithm). However, it is common sense in education that applying it in a meaningul context enhances even more the retention and the meaningul integration of that procedure in the learner’s skill set (procedural knowledge).

The limitations above being acknowledged, I’m trying my best to provide the minimaly sufficient information to make things work, i.e., to empower the user with the minimally required know-how to practice and apply on their own the actions depicted in the posts.

Finally…
If I were to make an analogy between a real-life scenario and what this collection of tutorials tries to accomplish, I would say it is akin to me trying to explain you how to navigate a very narrow path on a mountain ridge. If you do everything correctly, you should be able not only to walk the path and reach our objective, but also be able to stop from time to time and observe the greatness of what you’ve achieved. So, mind your footing, and everything will be great!