Manuel Chevalier

Tour de France meets Data Science: Merging datasets

1. Tour de France Legends: A Data Dive into Records What We Learned In The Previous Posts What We’re Tackling Next 2. Setting The Scene 2.1 Installing / Loading Your Packages 2.2 Loading The Data 2.3 Cleaning The Data 3. Tour de France Trivia 3.1 How Many Riders Won the Tour de France Without Winning […]

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dirty data, combining datasets, merge, real data, string manipulation, subsetting, summary statistics, tapply, tour-de-france, useful functions

Tour de France Meets Data Science: Manipulating Strings

1. Tour de France Records: Data Challenges and Cycling Trivia Facts 1.1 Flashbash: What We Discovered Last Time 1.2 What We’re Tackling Next 2. Setting The Scene 2.1 Installing / Loading Your Packages 2.2 Loading The Data 2.3 Cleaning The Data 3. Now the Fun Begins: Exploring Tour de France Records 3.1 Which City Has

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string manipulation, dirty data, real data, setdiff, subsetting, tapply, tour-de-france, useful functions, which.max, which.min

Tour de France Meets Data Science: A Beginner’s Case Study

1. Start Here: Building Data Confidence 2. Meet the Data: The Tour de France Dataset 3. Let’s Get to Work 3.1 Installing / Loading Your Packages 4. Where We All Begin: Loading The Data 5. What Can We Learn From These Data? 5.1 How Many Stage Winners in History? 5.2 How Many Unique Tour de

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loading data, dirty data, real data, setdiff, subsetting, summary statistics, tour-de-france, useful functions

Welcome

Curious about data? So am I. In this blog, I will share practical tips, real-world examples, and thoughtful reflections to help you work more confidently with data. Whether you’re just getting started or refining your skills, my goal is to keep things clear, honest, and genuinely useful. With these articles, you will learn to structure

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