We've talked about its use in stats and in graphing. Yet another benefit is R's extensive functionality. Every aspect of R is free to use, unlike some other stats packages you may have heard of EG, SAS or SPSS. Additionally, as you can see in this graph, knowing R is one of the top five languages asked for in data scientist's job postings. This makes R a great language to learn as the more popular software is, the quicker new functionality is developed, the more powerful it becomes and the better this support there is.

R is quickly becoming the standard language for statistical analysis. Outside of this course, you may be asking yourself, 'Why should I use R?' One reason to want to use R it's popularity. While this might be your first brush with it, we will be returning to CRAN time and time again when we install packages, so keep an eye out. R is downloaded from the Comprehensive R Archive Network or CRAN. It will be one of the main tools you use in this and following courses. R is both a programming language in an environment focused mainly on statistical analysis and graphics. First, let's remind ourselves exactly what R is and why we might want to use it. Now that we've got a handle on what a data scientist is, how to find answers, and then spend some time going over data science example, it's time to get you set up to start exploring on your own.

Installing r and rstudio on mac