Material for the DePaul Data Science Course
To start with, I'd like to point to my collection of "getting started in R" resources. There are a lot of great tools, tutorials, and resources out there, and I'm not going to try to reinvent them. However I will try to point you in the right direction.
Download and install R. Yes you can google it, google knows all about R already.
Unless you're already in love with another IDE, use R Studio for your development. You can download desktop version HERE. R Studio is also a great resource in general.
Unless you're forced to do something else, use git for your version control. In Windows the git tool is amazing, install it wtih the extra command line tools. You may have heard of github.com (oh, look you're here now!). Github uses git, and is also a great resource.
You will also need a text editor for taking a quick look at files. I like Sublime Text, and I gladly pay for it. http://www.sublimetext.com/ I also use SciTE (Scientific Text Editor), because it's really fast, easy and lightweight. I also have a version of SciTE on my personal website with some R specific customizations, just unzip it and use it without installing anything.
You can get help the old way by subscribing to r-help, which is perfect if you're a masochist. For everyone else there's Stackoverflow. Always search before you ask, and do follow posting guidelines. You can also search old r-help archives using something called markmail.
For high level "how tos", search for your topic in r-bloggers, which is an amazing collection of R related blogs.
I'm putting both versions of the mother of all R cheatsheets.
- R cheat sheet v1: http://cran.r-project.org/doc/contrib/Short-refcard.pdf
- R cheat sheet v2: http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf
- Shiny cheat sheet: http://shiny.rstudio.com/articles/cheatsheet.html
- Markdown / Knitr cheat sheet: http://shiny.rstudio.com/articles/rm-cheatsheet.html
These are two of my favorite quick references for learning R topics and finding examples.
Also R's built in documentation is incredibly valuable, but it's also dense for beginners
Once you've done analysis in R, you need to get it out of R. Here's how
Use Knitr to make reports knitr tutorial Use Shiny to make interactive reports (i.e. applications) Shiny tutorial
I rely heavily on the data.table package. You can install it with install.packages("devtools"). You can learn more in the data.table FAQ, which is also available within R once you download the package. For help, check out Stackoverflow again, but with "questions tagged data.table". Also, their github repo has an (overwhelming) amount of info!
Probably the single best thing in R is the data.frame. However, I use data.table which is an "enhanced" data.frame. The data.table uses a very different mindset, and it's a little hard to get used to, but it's worth it. The performance is vastly superior and the syntax is much easier once you've gotten the hang of it. Now I never switch back to data.frame just so that I don't get confused / muddled with the old way of doing things.
Lastly, you can download my package using devtools, which can install R libraries directly from github (amazing, right?). I use a lot of utilities from my library, so you might as well get it.
Start R and type:
install.packages("devtools") ## Get devtools from CRAN
library(devtools) ## Load devtools into R
install_github("geneorama/geneorama")If it doesn't install, open an issue on this project (see issues on the right) and I'll update the instructions.