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R for Data Science

A practical, hands-on guide to doing data science in R using the tidyverse, walking the reader through the complete workflow of importing, tidying, transforming, visualizing, modeling, and communicating data.

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What it’s about

R for Data Science teaches you how to turn raw data into understanding, insight, and knowledge using R and the tidyverse collection of packages. Rather than starting with the boring parts (data ingest and cleaning), the book begins with visualization and transformation of clean data so your motivation stays high, then progressively layers in programming skills, data wrangling, modeling, and communication. Written by Hadley Wickham (creator of much of the tidyverse) and Garrett Grolemund, the book unabashedly focuses on the most important 80% of data science tasks—hypothesis generation and exploratory data analysis on rectangular, in-memory datasets—giving you a coherent, opinionated toolkit (ggplot2, dplyr, tidyr, readr, purrr, and more) that share a common philosophy and work together naturally. By the end you'll have a reusable mental model of the data science process and the concrete R skills to execute it, plus pointers to deeper resources for the remaining 20%.

The through-line

Who it’s for: An aspiring or working data analyst who wants to turn raw data into understanding, insight, and knowledge and tackle a wide variety of data science challenges.
The problem: They have data but lack a coherent, efficient toolkit and workflow to import, clean, explore, model, and communicate it in R. They feel frustrated and overwhelmed by R's pickiness and the sprawling, inconsistent landscape of tools and techniques.
The plan: Install R, RStudio, and the tidyverse.
Start with visualization and transformation of clean data to build momentum.
Learn exploratory data analysis to ask and answer questions about data.
Wrangle messy data into tidy form using import and tidying tools.
Acquire programming skills (functions, vectors, iteration) to tackle harder problems.
The payoff: You can tackle about 80% of any data science project with the tools you've learned. · You generate many promising leads through rapid, iterative data exploration. · You produce elegant, informative plots and reproducible reports.

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Additional reading

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Sources of Power: How People Make Decisions · Gary Klein
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The Black Swan: The Impact of the Highly Improbable · Nassim Nicholas Taleb
Argues that statistical analysis is limited because it cannot predict rare, high-impact 'black swan' events, serving as a cautionary note on the limits of analytics.
Moneyball: The Art of Winning an Unfair Game · Michael Lewis
A popular case study of how the Oakland A's baseball team used an analytical approach to player selection to compete with richer teams, illustrating the power of competing on analytics.
Why Great Leaders Don't Take Yes for an Answer · Michael Roberto
Describes how to foster a culture of constructive conflict and debate in decision-making processes, which is essential for an analytical culture where assumptions are tested and merit triumphs over politics.
The Visual Display of Quantitative Information · Edward Tufte
A foundational work on how to create clear visual representations of data, a key skill for communicating analytical findings effectively.
Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart · Ian Ayres
The book discusses how statistical analyses are replacing human intuition and expert judgment in decision-making, a core theme related to the discussion of 'Moneyball' and the demise of the expert.
Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed · James Scott
Documents how governments' fetish for quantification and data can lead to misguided and harmful policies, providing a deep historical context for the book's warnings about the 'dictatorship of data'.
The War Managers · Douglas Kinnard
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Thinking, Fast and Slow · Daniel Kahneman
Explains the cognitive biases that lead humans to see illusory causal links, which the author's argue big data correlations can challenge and disprove.