Learning R

A collection of 53 posts

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Sports Science Featured

Statistical Methods for Detecting Performance Changes in Athletes

Single-subject statistical methods are crucial for evaluating performance changes in individual athletes, especially when working with smaller groups or limited data. Practitioners should move away from relying solely on group-level statistics or subjective assessments.

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Learning R Featured

A Follow-Up Guide to Building an Athlete Monitoring Dashboard

In this guide, we'll follow on from my last post on the process of creating a Shiny application to visualize sample GPS data. All the necessary files can be found at the end of this article.

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A Beginners Guide to Building a Comprehensive Shiny Dashboard for GPS Data Visualization

In this guide, we'll walk through the process of creating a Shiny application to visualize sample GPS data. This app will include interactive charts and a styled data table. By following these detailed steps, you'll learn how to set up and deploy a Shiny app on Shinyapps.io.

Learning R Featured

Player Performance in the League of Ireland Premier Division 2022-2024

Ranking the top 20 players based on performance metrics in the League of Ireland Premier Division 2022-2024.

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Player Performance in the League of Ireland Premier Division 2024

Ranking the top 25 players based on performance metrics in the League of Ireland Premier Division 2024.

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`worldfootballR` Tutorial: Analysing League of Ireland with Fotmob Data

The Greatest League in the World!

Learning R Featured

StatsBombR Tutorial: Comparing Player Shots and Goals and Shots and Key Passes per 90 in the Champions League and UEFA Euros

Stay Curious!

Learning R Featured

StatsBombR: A tutorial on how to use the StatsBombR package to analyse football data."

All credit for this tutorial goes to StatsBomb and the webinar they recently hosted which went through this code. This tutorial is a step-by-step guide on how to use the StatsBombR package to analyse the 2024 Copa America data.

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EURO 24 Web Scraping: A tutorial of scraping EURO 24 Stats from FBRef by a complete beginner!

I've no doubt that this can be done more efficiently than I have done it but this is what I have learned so far. I hope this helps in some way and if you are a more experienced R user, I would love to hear your feedback on how I can improve this process - feel free to reach out to me.

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R for Data Science Exercises: Quarto Formats

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R for Data Science Exercises: Quarto

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R for Data Science Exercises: Base R

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R for Data Science Exercises: Iteration

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R for Data Science Exercises: Functions

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R for Data Science Exercises: Web Scraping

Web scraping involves programmatically extracting data from websites.

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R for Data Science Exercises: Hierarchical Data

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R for Data Science Exercises: Arrow

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R for Data Science Exercises: Databases

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R for Data Science Exercises: Spreadsheets

Learning R Featured

R for Data Science Exercises: Joining Data Frames

Joining data frames is a crucial data manipulation technique used to combine two or more data frames based on common columns or keys. This process allows you to merge datasets to create a more comprehensive and detailed dataset that can be used for further analysis.

Learning R Featured

R for Data Science Exercises: Missing Values

Missing values can occur for various reasons, such as data entry errors, equipment malfunctions, or respondents choosing not to answer certain survey questions. Properly handling these missing values is crucial to ensure the integrity and accuracy of your analyses.

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R for Data Science Exercises: Dates and Times

Dates and times are hard because they have to reconcile two physical phenomena (the rotation of the Earth and its orbit around the sun) with a whole raft of geopolitical phenomena including months, time zones, and DST.

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R for Data Science Exercises: Factors

Factors are a type of variable in R that are used to store categorical data. Factors can be ordered or unordered. Factors are stored as integers, and have labels associated with these unique integers.

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R for Data Science Exercises: Regular Expressions

Regular Expressions, A.K.A., "regex" or "regexp" are a concise and powerful language for describing patterns within strings. With every punctuation character potentially overloaded with meaning, regular expressions are one of the most compact languages out there.

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R for Data Science Exercises: Strings

Strings are not glamorous, high-profile components of R, but they do play a big role in many data cleaning and preparation tasks. R provides a solid set of string operations, but because they have grown organically over time, they can be inconsistent and a little hard to learn.