R Resources (maybe more)

In the past, I lost track of reality trying to track a gazillion links covering every data-science-friendly programming language under the sun. **shakes head** Bad idea. Since I program in R daily, I like to keep track of R and RStudio developments. I’m mostly going to share R resources and some other miscellaneous resources that I find useful for analytics, statistical programming, machine learning, data science workflows, and web app development. I’m enjoying the Julia programming language recently, so I’ll share resources that I find bookmark worthy as well.

R and Julia are open-source programming languages for statistical computing and graphics. R has an open and friendly community devoted to using R for data science and making business analytics easy to attain. One of the things that I like most about R is the thousands of packages available making almost everything in R a little easier from ETL, to method chaining, to developing interactive web apps. Julia is a newer programming language (newer to me) recommended by a coworker. Unlike interpreter languages such as R and Python, Julia has significant performance advantages for numerical computations. Julia is a multi-threaded programming language (native parallel processing and designed for distributed computing), it compiles (it doesn’t interpret lower-level languages like C or C++), and there is a growing body of scientists and developers finding unique ways to use the language. I certainly welcome any suggestions that you might have for the lists below!

R Classics

R Applied Resources

R Packages (my favorites)