Rajashri Venkatesh
R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years.
Here is a list of blogs you cannot miss if you are R programmer!
R-Bloggers.com is a central hub (e.g: A blog aggregator) of content collected from bloggers who write about R (in English). The site will help R bloggers and users to connect and follow the “R blogosphere”
Revolutions is a blog dedicated to news and information of interest to members of the R community. This blog is updated every US workday, with contributions from various authors. Revolutions was founded in 2008 by Revolution Analytics. Today, Revolutions is hosted, and maintained by Microsoft, which acquired Revolution Analytics in 2015. They welcome contributions to this blog.
R had tricky and less intuitive syntax than languages I was used to, and it took a while to get accustomed to the nuances. It wasn't immediately clear to me that the power of the language was bound up with the community and the diverse packages available.
Here are 10 great R packages that we love and use every day!
R is more than a programming language. It is an interactive environment for doing statistics. I find it more helpful to think of R as having a programming language than being a programming language. The R language is the scripting language for the R environment, just as VBA is the scripting language for Microsoft Excel. Some of the more unusual features of the R language begin to make sense when viewed from this perspective.
If you're more familiar with SQL than R, it can be difficult to figure out how to do basic data tasks such as subsetting your data. Statistics consultant Patrick Burns shows how to do common data slicing in both SQL and R, making it easier for experienced database users to add R to their toolkit.
This is a good resource of a lot of graphs and visualisations with heir codes.
R and Data Mining written by Yanchang Zhao is a very useful source to keep yourself updated with latest in data mining and R
If you are a sports enthusiast this is a post you don't want to miss!
This post uses pro-football (American) boxscore data from 1966 through 2013 and generates few interactive charts using rCharts, ggplot2 and shiny. It also provided a first time exposure to the power of dplyr. Data for these charts were scraped from the excellent reference site, pro-football-reference.com, using a function written in R
