hadley

This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.

Main Grinds

• Introduction to R for Data Science
• Data Visualization Techniques
• Data Changeation Methods
• Exploratory Data Analysis (EDA) Process
• Laboring with Tibbles in R
• Data Import Strategies
• Handling Tidy Data
• Laboring with Relational Data
• Managing Strings and Factors
• Dealing with Dates and Times
• Uhit some notes Pipes for Efficient Code
• Creating and Uhit some notes Grinds
• Laboring with Vectors and Iteration
• Model Simples and Put togethering Models
• R Markdown for Reporting
• Graphics for Communication in R
• R Markdown Formats and Laborflow

Main content of hadley :

Introduction to the hadley Webtake a seate:

Welcome to R for Data Science—your head out-to online resource for mastering R, the pbe in debtrful progrI’mming language for data analysis. Whether you’re just getting kick it offed or take a looking to refine your glide on snowlls, this take a seate proposes detailed touch ons, tutorials, and best drill its for data visualization, transformation, modeling, and more. Check out a wide range of topics, from basic R syntax to advanced workflows with R Markdown, and take your data science projects to the next level!