Posts tagged with ‘Course’

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 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 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 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 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 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...