Welcome to R-bloggers!

Hooray! I am now part of the R-bloggers community!

R-bloggers is a blog aggregator for content created by the Rstats community. It was founded by Tal Galili towards the end of 2009, in order to help connect R bloggers with R users (and each other).

I came across R-bloggers relatively soon after it got going and have benefitted so greatly from it over the years. It has been quite something to see how it has grown and flourished in this time. Just take a look at Tal’s annual retrospectives between 2010 and 2012 which highlight the early growth of R-bloggers and you’ll see what I mean:

• 2009: R-bloggers gets up and running, 17 contributing blogs

• 2010: 143 contributing blogs, 3000 posts, 600 000 visits from over 200 countries

• 2011: almost 300 contributing blogs, over 1.4 million visits

• 2012: over 400 contributing blogs, 2.7 million visits by over 1.1 million people

These days R-bloggers continues to serve a large global audience with over 600 contributing bloggers. It is the go-to place for R news and tutorials and one of the core spokes of the R community.

Tal, I’m sure I speak for many in the R community when I say thank you for continuing to invest in and develop this resource which we all benefit from on a daily basis. Having appreciated content from R-bloggers for so many years I now look forward to being able to contribute some of my own and hope that I will be able to add value for others in the process.

Eduardo from Data Science LA did a really nice interview with Tal at the useR conference back in 2014. They discussed how he came to start R-bloggers, his thoughts on and involvement with R and the community, as well as another of his projects, R-Users: “A job board for people and companies looking to hire R users”.

Listening to it again now, two years later, I was struck by how much of their discussion is still so relevant, and Tal has some great insight and advice to share (e.g. don’t get too caught up with newer tech developments before they’re ready, unless they really solve a problem that you have).

Two of the things he mentions in particular I would like to highlight:

• learn to create R packages as soon as possible
• supplement your R and stats knowledge with some C++ (i.e. learn to use Rcpp)

These are two areas which have been really pivotal for me in terms of improving my knowledge and understanding of R, and empowering myself to dig deeper and do more with it. If you’re looking for good learning resources in either of these areas, I highly recommend Hadley Wickham’s books R packages and Advanced R (which has a great chapter on Rcpp).

When asked what his advice would be to an R user back in 2007, I had to chuckle at Tal’s remark not to invest in Tinn-R because it won’t be around much longer 🙂

But jokes aside, whether you are a new or long time R user, it’s a great interview that is worth a listen.