Beginning Data Science in R

In the book, we cover basic data manipulation filtering and selecting relevant data; transforming data into shapes readily analyzable; summarizing data; visualizing data in informative ways both for exploring data and presenting results; and model building. These are the key aspects of doing analysis in data science. After this we will cover how to develop R code that is reusable and works well with existing packages, and that is easy to extend, and we will see how to build new R packages that other people will be able to use in their projects. These are the essential skills you will need to develop your own methods and share them with the world. R is one of the most popular (and open source) data analysis programming languages around at the moment. Of course, popularity doesnt imply quality, but because R is so popular it has a rich ecosystem of extensions (called packages in R) for just about any kind of analysis you could be interested in. People who develop statistical methods often implement them as R packages, so you can quite often get the state of the art techniques very easily in R. The popularity also means that there is a large community of people who can help if you have problems. Most problems you run into can be solved with a few minutes on Google because you are unlikely to be the first to run into any particular issue.