Leading With Data· a Bicycle Guide

Partial book profile

Learning R

This book provides a hands-on guide to learning the R programming language for data analysis, starting with the fundamentals and progressing through the entire data analysis workflow to advanced topics like package creation.

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What it’s about

Learn how to perform data analysis with the R language, even if you have little or no programming experience. This hands-on guide walks you through the essential tools and concepts you need to know, from basic data types and programming principles to the complete data analysis workflow. You'll learn how to retrieve, clean, explore, visualize, and model data, as well as how to leverage R's vast ecosystem of add-on packages. The book finishes by teaching you more advanced topics, including how to create your own R packages to share your work with others. This is not a reference book, but a practical, tutorial-based introduction designed to get you analyzing data with R quickly and confidently.

The through-line

Who it’s for
The reader is an aspiring data analyst, student, or professional who wants to learn the R programming language to analyze data effectively, but has little to no prior programming experience and feels overwhelmed by the complexity of the language and the data analysis process.
The problem
The reader needs to perform data analysis tasks but lacks the programming skills in R to do so, struggling with importing, cleaning, visualizing, and modeling data. They feel intimidated by programming, confused by R's syntax, and uncertain about how to approach a data analysis project from start to finish.
The plan
  1. Master the R Language Fundamentals: Learn the core data types (vectors, lists, data frames), concepts (functions, loops), and syntax of R.
  2. Execute the Complete Data Analysis Workflow: Follow a guided process from importing and cleaning data to exploring, visualizing, modeling, and presenting results.
  3. Extend R's Power with Packages: Learn how to find, install, use, and even create R's add-on packages.
The payoff
Become a competent R programmer, capable of confidently handling the entire data analysis workflow. · Transform raw data into clean, insightful visualizations and models. · Feel empowered to answer questions with data and effectively communicate your findings.

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