About

This book covers basics to learn R for Data Science. It is designed for MBAN students.

We also have a companion R package named RBootcamp, containing the data sets used as well as interactive exercises for each chapter. Each chapter covers fundamentals with few basic applications. The interactive lessons (see the last chapter Lessons & Answers) provide more detailed applications on each subject. Install RBootcamp by running the following lines on your console:

#install.packages("remotes")
#remotes::install_github("yaydede/RBootcamp")

Why R?

R is both a programming language and software environment for statistical computing, which is free and open-source.

With ever increasing availability of large amounts of data, it is critical to have the ability to analyze the data and learn from it for making informed decisions. Familiarity with software such as R allows users to visualize data, run statistical tests, and apply machine learning algorithms. Even if you already know other software, there are still good reasons to learn R:

  1. R is free. If your future employer does not already have R installed, you can always download it for free, unlike other proprietary software packages that require expensive licenses. You can always have access to R on your computer.

  2. R gives you access to cutting-edge technology. Top researchers develop statistical learning methods in R, and new algorithms are constantly added to the list of packages you can download.

  3. R is a useful skill. Employers that value analytics recognize R as useful and important. If for no other reason, learning R is worthwhile to help boost your resume.

Here is a very good article about R and Programming that everybody should read: 7 Reasons for policy professionals to get into R programming in 2019 (Jones_2019?).

Sources

There are many sources for learning R. But there is one source that compiles all possible sources in R: Big Book of R

The other source is LOST, Library of Statistical Techniques. It provides all possible data analytics tools in multiple languages including Python, R, SAS, Stata etc …