Posts tagged with ‘Course’

Athlete Monitoring: Data Analysis and Visualization Basic Visualizations In this video I will show you the basics of visualizations using ggplot2 package in R. GGplot2 package is the implementation of the Grammar of Graphics – a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. I am demonstrating the very basic...

Athlete Monitoring: Data Analysis and Visualization Data Wrangling in R – Part 2 In this video I am continuing the data wrangling in R using tidyverse (actually the dplyr) package. With few basic functions (i.e.,group_by(), mutate(), summarize(), filter(), select()), you can pretty much do 80% of data wrangling for the athlete monitoring purposes. For more informations, please refer to...

Athlete Monitoring: Data Analysis and Visualization Data Wrangling in R – Part 1 In this video we are beginning to do some data wrangling in R. I am explaining how to convert wide format to long format using pivot_longer() and vice versa using pivot_wider(). These data wrangling techniques are very important, but very hard to do in Excel (although...

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.