Athlete Monitoring: Data Analysis and Visualization – Continuous Analysis

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 summaries and trends

The prepare() function is very flexible and allows you to implement your own estimators very easily. It provides neat data summaries (i.e., wide and long formats, missing data, group summaries) that you can use for further analysis.

The plotting functions of the athletemonitoring package allows you very simple, yet powerful plotting functionalities that will cover pretty much all of the most common athlete monitoring data visualizations. The plotting functionalities allow you to:

  • Create sparkline HTML table
  • Create bar plots
  • Create line plots

The filtering parameters in the plot() function allows you to design the graph you are looking for very easily.

In this video, I am covering the analysis of the continuous data (i.e., numerical) and in the next video, I will explain the analysis of the nominal data types.

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I am a physical preparation coach from Belgrade, Serbia, grew up in Pula, Croatia (which I consider my home town). I was involved in physical preparation of professional, amateur and recreational athletes of various ages in sports such as basketball, soccer, volleyball, martial arts and tennis. Read More »

Welcome to Complementary Training Community! Forums Athlete Monitoring: Data Analysis and Visualization – Continuous Analysis


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