Reddit mentions of The Art of R Programming: A Tour of Statistical Software Design

The Art of R Programming is, hands down, one of the best books on R. It is simply an excellent, well-written description of how R works. That’s all you need to know.

You wouldn’t go read a book on Pandas to learn python, so don’t read a book on data science in R to learn R, as many people here have suggested you do.

Right now I'm reading The Art of R Programming. It seems like it has a lot of good knowledge but also seems really disorganized. The author uses control statements without explanation in the 2nd chapter about vectors to demonstrate their ability, and then doesn't get back to control statements until chapter 7. But being a seasoned programmer I don't think things like that will bother you too much. This is the only R book I've used, so my opinion isn't very broad based. The reviews for R Cookbook seem pretty good and I'm a little sorry I didn't start with that instead.

Hopefully someone else can chime in.

just did coursera course, I think that it is pretty poor, its pretty fast, assumes alot and is not very friendly for beginner's. Try "The Art of R Programming" by Norman Matloff. It is better worded and you can take it at your own pace.

http://www.amazon.com/The-Art-Programming-Statistical-Software/dp/1593273843

From my coursework, I found these resources helpful:

http://www-bcf.usc.edu/~gareth/ISL/

https://www.amazon.com/Art-Programming-Statistical-Software-Design/dp/1593273843/ref=sr_1_3?ie=UTF8&qid=1527726606&sr=8-3&keywords=programming+with+R

https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_9?ie=UTF8&qid=1527726606&sr=8-9&keywords=programming+with+R

https://www.amazon.com/Mastering-RStudio-Develop-Communicate-Collaborate-ebook/dp/B0123RVFZG/ref=sr_1_1_sspa?s=books&ie=UTF8&qid=1527726659&sr=1-1-spons&keywords=Rstudio&psc=1

As you already have programming experience I strongly recommend you try "The Art of R Programming" sooner or later. The majority of other books discuss R from a statistical aspect. This book, however, approaches it as a programming language. One of the few R books I own ("R graphics" and "ggplot2" might be others, but that's a bit advanced.)

This site is a great resource for all those simple little R-isms that I forget from time to time. "The R Cookbook" is another resource, much like the above, but with a bit more meat.

There are LOADS of other resources out there. If you ever have a question, just google it + "R stats" and you'll usually find what you need.

You might also want to subscript to "R Bloggers", it's a planet with loads of sources. It's inspiring and educational to see all the things people put R to use for.

For a complete noobie, I'd also grab http://www.amazon.com/The-Art-Programming-Statistical-Software/dp/1593273843

I use the Art of R Programming as the starter text, then go to the Applied Spatial Analysis text -- although I'd skip its parts on raster processing and instead use the vignette is the "raster" package.

I used this book when I got started, but there are numerous online tutorials as well.

I was advised to start with basic R and after move to tidyverse and I think it was a good advice. For basic R I recommend https://www.amazon.com/Art-Programming-Statistical-Software-Design/dp/1593273843 , book is little old but I really like it.

> would you be able to point me in any resources about methods more suited to psychology or other discussions about this issue?

To my knowledge, this discussion really took of witht the proclamation of cognitive architectures, although I'm sure already William James said something about that. For a quick introduction, read You can't play 20 questions with nature and win. I also admire the work of the ACT-R guys, even if I don't agree with all assumptions of ACT-R (modularity comes first to mind).

I honestly believe modelling is the way to go. It provides us with quantifiable theories that have a well defined scope and can be unambiguously communicated. This book gives an excellent introduction in theory and techniques, alas it's written horribly. It took me half a year to get really through it.

Learn statistics and programming. The bigger your toolbox, the better your work. Cognition is insanely complicated, which reflects on the level of competence a skilled cognitive scientist is required to possess. Learn R, it's the future. I'm mad at myself for having wasted years with SPSS. R also gives you insights into statistics you'd never have otherwise. Once you see the code you suddenly understand the actual meaning of the equations and what they mean for your research question. I now demand all my students to learn R, for their and my own good. That also opens up a myriad of career paths. Learning to program takes you one year, the reward is with you a lifetime. Here's a good introductory book.

The brainfuck here is that we actually know how to do it. Experiments are an invaluable methodology to nail down the relationship between variables, but they are also dangerous. It's asinine to rejoice over a result which stems only from the experimenter trying to retrieve that exact result. We want to understand human behavior, and that occurs in the real world. So we have first to establish a frame for behavior, being the task at hand. What do people want to achieve? What's their goal? What are their means? These questions lead you to a better research question, which in turn gives you better ideas about what you are looking for. On the research question depends everything! Worry not so much about causal relationships at the beginning. I understand why it's such a big thing during studies, but it's only half the story. We often only need correlation, causation comes later.