Go to Course: https://www.coursera.org/learn/r-programming
Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler
Week 1: Background, Getting Started, and Nuts & Bolts
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
Week 2: Programming with RWelcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
Week 3: Loop Functions and DebuggingWe have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
Week 4: Simulation & ProfilingThis week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code,
For anyone planning to set their foot in the vast field of data analytics and R, this course is what will set the momentum and actually make you fond of programming in R by the end of the course.
A great introduction to slightly more complicated R programming. Basic concepts covered well and it builds nicely to the point where you feel like you can apply your knowledge to real world examples
Great course for people who work with data a lot. This course actually helps in looking at data in its basic forms, helps understand transformations better, and gives ideas about playing with it.
It was a great course with an excellent teacher (Prof.Peng) who teaches all of the contents with extra details. However, I think it would have been better to bring more examples during teaching time.
This is course is probably my favorite out of the Data Science: Foundations using R specialization. There was plenty of opportunity to practice and further develop by burgeoning R programming skill.