# Statistics resources for R and JASP

In my lab we use R and JASP whenever possible.

Why R? It's free, extremely flexible, and easy to script, all of which promotes accurate, reproducible, and open science (open because it's easy to share non-proprietary datasets and analysis scripts). R has a lot of available statistical packages, including for linear mixed effects modeling and Bayesian analysis approaches. And R produces great graphics, both for data visualization and publication.

Why JASP? It's free and makes it easy to implement Bayesian analyses, and also produces good figures. (We're new to JASP but liking it a lot so far.)

Here are some links for learning and using these programs. As with most things, the ultimate key is to spend a significant amount of time using the program, ideally with some data in which you're invested.

## Learning R

- tryr.codeschool.com is an online introduction to R in 8 lessons
- DataCamp has a course for R (and for Python)
- swirl lets you learn R interactively, in R
- stat545.com: Data wrangling, exploration, and analysis with R
- R for data science, by Garrett Grolemund and Hadley Wickham
- http://varianceexplained.org/RData/
- Johns Hopkins coursera cource
- R Programming for Data Science by Roger Peng
- OpenIntro Statistics also includes some labs
- Winston Chang's Cookbook for R
- Discovering statistics using R (book)
- Programiz R tutorial

## Learning JASP

- The JASP team provide a short user guide and previous slides from JASP workshops