Athlete Monitoring: Data Analysis and Visualization Hello World in R As the tradition demands, when learning a new programming language, one needs to create the Hello World program. In this version of the Hello World, I am explaining the atomic vectors classes in R, as well some of the specific of the language (such as vectorized variables, vectors recycling...

Athlete Monitoring: Data Analysis and Visualization Aggregating data in Excel We are going to use R and R-Studio thorough the rest of this course (as well as a little bit of Microsoft Excel), so in this lecture, I will show you how to install them on your computer. About Mladen Jovanovic Mladen Jovanovic is a physical preparation coach from...

We are going to use R and R-Studio thorough the rest of this course (as well as little bit of Microsoft Excel), so in this lecture I will show you how to install them on you computer.

When we have multiple individuals sharing the same context we can check what is happening to the group but more importantly to check how is a given individual differing from a group.

In this lecture I will explain the few techniques that can be used to make the comparison more “fair”, particularly for the purpose of figuring out the change in individual monitoring.

In this lecture, I continue with the solution to the problem of dealing with nominal variables using rolling functions. The presented solution is to use dummy coding and aggregating session using means, which gives us rolling proportions.

Rolling functions are useful in describing trends over time. These can be any type of descriptive functions, like means, medians, standard deviations, and so forth.

In this lecture, I explain the basics of data aggregation and a few issues that you might stumble upon, as well as their solution.

Tags are simple descriptors that can help you in your analysis, and can also potentially expand the number of questions you can answer. In this video, I will introduce session descriptors (i.e., type, location, attendance) and athlete descriptors (i.e., health status, training status).

In this module, we are going to cover foundational topics that are needed to progress this course to more practical data wrangling, analysis and visualization.