Course - Complementary Training - Page 4

• # Athlete Monitoring: Data Analysis and Visualization – Body Diagram Analysis

By Mladen Jovanovic on 20/05/2021

In this video, I will teach you how you can use the AthleteSR body diagram to visualize and analyze the monitoring data. I want to thank Jose Fernandez for making this possible by creating polygon data for the AthleteSR body diagram and providing a sample code for the visualization.

• # Athlete Monitoring: Data Analysis and Visualization – The Dorem Package

By Mladen Jovanovic on 17/03/2021

In this video, I am explaining how the dorem package works and I am also preparing our data sets (i.e., training load and wellness) to be used in mapping training load to sleep quality.

• # Athlete Monitoring: Data Analysis and Visualization – Shiny AthleteSR Dashboard

By Mladen Jovanovic on 12/03/2021

Athlete Monitoring: Data Analysis and Visualization Shiny AthleteSR Dashboard In this video, I am walking you through the Shiny AthleteSR Dashboard. As explained in this and the previous video, this course and the athletemonitoring package were the result of my attempt to build this mockup dashboard for the AthleteSR developers. Everything covered in this course is implemented in this...

• # Athlete Monitoring: Data Analysis and Visualization – AthleteSR

By Mladen Jovanovic on 12/03/2021

Athlete Monitoring: Data Analysis and Visualization AthleteSR The idea for this course started when I developed an athlete monitoring Shiny dashboard for the AthleteSR, which is our app for athlete management (FREE for the Complementary Training members). The goal was to have a formalized way to analyze and visualize the data for our developers, so they can implement it...

• # Athlete Monitoring: Data Analysis and Visualization – Nominal Analysis

By Mladen Jovanovic on 12/03/2021

Athlete Monitoring: Data Analysis and Visualization Nominal Analysis To remind ourselves, nominal analysis is the same as the continuous (i.e., ratio scale) analysis, but with dummy coding, where each level is represented as an additional variable with 0 or 1. Thus, (rolling) proportions and counts are the methods used to analyze and visualize the nominal variables. In this video,...

• # Athlete Monitoring: Data Analysis and Visualization – Continuous Analysis

By Mladen Jovanovic on 12/03/2021

Athlete Monitoring: Data Analysis and Visualization Continuous Analysis In this video, I will explain how to use the prepare() function from the athletemonitoring package. This function/package allows us to implement everything we have covered so far in this course: Dealing with missing entries Dealing with missing days Acute and Chronic rolling windows for smoothing and trends Rolling functions Group...

• # Athlete Monitoring: Data Analysis and Visualization – Introducing athletemonitoring R package

By Mladen Jovanovic on 12/03/2021

In this module, I am going to introduce athletemonitoring package by demonstrating how easy is to perform common data analysis and deal with issues explained in the previous modules. I will also demonstrate the AthleteSR software, how to extract/sync the monitoring data from it and how to analyze it.

• # Athlete Monitoring: Data Analysis and Visualization – Basic Visualizations

By Mladen Jovanovic on 19/02/2021

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

By Mladen Jovanovic on 19/02/2021

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

By Mladen Jovanovic on 19/02/2021

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...