The great thing of R, is that the number of available resources on the web is increasing dramatically. If you cannot afford expensive books, or you are looking straight questions or some tutorials you will find almost anything you need. Here's my short list of best place to learn and explore R other than the CRAN website:

R Seek: You probably use google while searching for specific commands. But this search engine is much more powerful. It looks for R related websites, and you can also filter to "functions", "blogs", "books" and etc. Awesome!

R Graph Gallery : This is unfortunately not updated anymore, but you can find fancy graphs and codes which can inspire you for something even better.

R Graphical Manual: Visual learning is always the best. This is probably the principle of this website, which allows you to search for functions and packages from the standpoint of their graphical outputs.

R Reference Card : This is actually from CRAN, but it's an extremely handy reference card that you can print and put on your office wall to show people how geeky you are. Most of them are daily uses functions that you'll probably learn soon anyway, but it's extremely useful if you've just got started.

R Bloggers: New research method, elaborated codes, inspiring plots, novel ideas on everything related to R cannot be found in books. The cutting edge is in the blogosphere, where hundreds of peer R users are perhaps tackling the same issue you are working one. This websites put together almost 200 bloggers from around the world. Just put in you're RSS feed, and you'll get about 10 daily posts with new ideas. Inspiring.

Quick-R. Nice and intuitive website on R, covering wide range of topics from analysis to plotting

Quantitative Archaeology Wiki . This is still under development but has some sections on R specifically designed for archaeologists.

There are of course plenty of other tutorials around the web. If you want use GRASS and R together you can find a nice introductory tutorial on geostats here. A number of academics also share their courses online so you can sneak in and learn some great stuff like this one. My favourite course isRichard McElreath's Statistical Thinking in Evolutionary Ecology Course. This is simply mind-opening.

**Searching, Exploring and Procrastinating**R Seek: You probably use google while searching for specific commands. But this search engine is much more powerful. It looks for R related websites, and you can also filter to "functions", "blogs", "books" and etc. Awesome!

R Graph Gallery : This is unfortunately not updated anymore, but you can find fancy graphs and codes which can inspire you for something even better.

R Graphical Manual: Visual learning is always the best. This is probably the principle of this website, which allows you to search for functions and packages from the standpoint of their graphical outputs.

R Reference Card : This is actually from CRAN, but it's an extremely handy reference card that you can print and put on your office wall to show people how geeky you are. Most of them are daily uses functions that you'll probably learn soon anyway, but it's extremely useful if you've just got started.

R Bloggers: New research method, elaborated codes, inspiring plots, novel ideas on everything related to R cannot be found in books. The cutting edge is in the blogosphere, where hundreds of peer R users are perhaps tackling the same issue you are working one. This websites put together almost 200 bloggers from around the world. Just put in you're RSS feed, and you'll get about 10 daily posts with new ideas. Inspiring.

**Learning and Tutorials**Quick-R. Nice and intuitive website on R, covering wide range of topics from analysis to plotting

Quantitative Archaeology Wiki . This is still under development but has some sections on R specifically designed for archaeologists.

There are of course plenty of other tutorials around the web. If you want use GRASS and R together you can find a nice introductory tutorial on geostats here. A number of academics also share their courses online so you can sneak in and learn some great stuff like this one. My favourite course isRichard McElreath's Statistical Thinking in Evolutionary Ecology Course. This is simply mind-opening.