Introduction to R Programming [Video]
Data is everywhere, and statisticians and analysts everywhere need to handle this data efficiently and tactfully. In comes R, a powerful programming language, arming developers with the tools to cater to their needs. This course will give you everything you need to start making software that can unlock your statistics and data.
The course is broken down into three parts. The first part will introduce R Studio and the basics of R—using packages and teaching you programming concepts such as variables, vectors, arrays, loops, and matrices. By solving coding challenges, you will gain a strong foundation for data munging.
With the basics mastered, we will take you through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, writing functions, debugging, error handling, and writing an apply family of functions. When you’ve mastered data munging, we’ll focus on visualizing data using base graphics.
Naturally, the next step is to learn how to make statistical inferences. We walk you through the fundamentals of univariate and bivariate analysis, computing confidence intervals, interpreting p values, and working with statistical significance. You’ll see how and when to use some of the commonly used statistical tests. With that, you will be ready for your first full-scale data analysis project to test the skills you’ve learned.
Finally, you will glimpse two powerful packages for data munging, the dplyr and data.table, which have both seen a rise in the R community. It is imperative to learn about both of these packages because much modern R code has been written using them.
With the help of interesting examples and coding challenges, this course will ensure that you have all the hacks and tricks you need to get started with R.Style and Approach
You’ll learn all of R fundamentals and establish a strong understanding of the concepts behind data analysis. The course follows a pragmatic approach where you’ll find step-by-step instructions of the functions, tools, and concepts, and the reason you’re learning about them. Most of the videos on the course close with coding challenges, putting your newly learned skills into practical use immediately.
|Course Length||3 hours 46 minutes|
|Date Of Publication||24 Jul 2016|
|Project 2 – Visualization with Base Graphics|
|Project 3 – Statistical Inference|