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Here you can access links to R learning resources for different levels and the schedule of next meetings.
- An Introduction to R: A nice staring point.
- Good practices in R coding: There are many ways to write your code, but when you want to share it, it is better to follow the standars that you can find in this link.
- An introduction to R by Longhow Lam: if you want to start with R, here you will find the basic commands. I would recommend Chapters 2 to 4, Data objects, Importing data and Data manipulation.
- Visualizing Data by William S. Cleveland: This book was suggested by Ron Wehrens during his presentation
- Advanced R by Hadley Wickham: it covers from basic to advanced level of code.
- Quick-R: fast access to functions with examples (plots, modelling functions, etc.)
- r-bloggers: A blog of blogs. This link gathers posts from ~600 R blogs, so it has about 5-10 post per day. For all levels.
- Using R for Digital Soil Mapping by B. Malone et al.: short review soon.
You should keep in mind that most R problems have been already asked and solved somewhere. Thus, if you need to solve a problem in R the first thing to do is just to google it: copy&paste the error that you get, use keywords, avoid names that belong only to your case, etc. However, it is not always very easy to find the solution. There are also many specialised mailing list where you could find the solution. For this reason MarkLogic is a nice tool. It allows to search millions of emails across thousands of mailing lists, search using keywords as well as from:, subject:, extension:, and list: constraints, among other features. Check it out here.
- MMAT-50303: R for Statistics: The aim of this course is to provide an introduction to R, a computer language and environment for statistics and graphics.
- Basic Statistics
- Introduction to R for Statistical Analysis
- Linear Models
- Dynamic Models in R: Programming, parameter estimation and model selection. Register NOW!
Nowadays there are many sources of massive open online courses (MOOCs) that deliver courses on R. The most popular sites are edX, Coursera, Udacity, Udemy, among others. Some of them are free and others you have to pay. We would like to give a list of R courses here with a review. Thus, we would ask you to share your experience here.
In this link you can access the previous meeting scripts and presentations, as well as the schedule for the next meetings.
To bear in mind
This piece of text is the Zen of Python, but applicable to R as well:
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!