Lorcán Mason

Sports Science Featured

Match Running Performance Preceding Scoring and Conceding a Goal in Men’s Professional Soccer

Within the 5 min period before scoring a goal, teams covered significantly greater distance at low- and medium-intensity when compared to before conceding a goal.

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Learning R Featured

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

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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.

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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.

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R for Data Science Exercises: Numbers

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R for Data Science Exercises: Logical Vectors

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R for Data Science Exercises: Communication

Now that we understand your data, we need to communicate this understanding to others. To help others quickly build up a good mental model of the data, you will need to be as self-explanatory as possible. This chapter focuses on the tools you need to create good graphics.

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R for Data Science Exercises: Exploratory Data Analysis

The goal during EDA is to develop an understanding of your data using questions as tools to guide your exploration: 1. What type of variation occurs within my variables?; 2. What type of covariation occurs between my variables?

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R for Data Science Exercises: Visualise Layers

This section is based on the layered grammar of visualisations, facets, statistics, position adjustments, and coordinate systems to give you a fundamental understanding of plotting your data.

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R for Data Science Exercises: Workflow Scripts and Projects and Data Import

The final installment of exercises from the "Whole game" section of the R4DS 2nd edition. These exercises focus on becoming more familiar and efficient in R studio.

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R for Data Science Exercises: Data Transformation Case Study

In this case study, we will be comparing how often a player successfully hits the ball (H) to the total number of attempts they made to hit the ball (AB). Including a count ensures our analysis is based on a reasonable amount of data and not just a few instances.

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R for Data Science Exercises: Workflow Code Style and Data Tidying

These exercises are focussed on furthering your workflow coding style and tidying messy data to make analysis easier.