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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, visualizing, and exploring data.
- Preface to the second edition
10 Exploratory data analysis. 11 Communication. Transform....
- 1 Introduction
If you run the same code in your local console, it will look...
- Whole game
Our goal in this part of the book is to give you a rapid...
- Visualize
In 10 Exploratory data analysis, you’ll combine...
- Transform
10 Exploratory data analysis. 11 Communication. Transform....
- Import
In this part of the book you’ll learn how to access data...
- Program
As you read more code written by others, you’ll see more...
- Communicate
So far, you’ve learned the tools to get your data into R,...
- Preface to the second edition
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.
Quarto provides a unified authoring framework for data science, combining your code, its results, and your prose. Quarto documents are fully reproducible and support dozens of output formats, like PDFs, Word files, presentations, and more. Quarto files are designed to be used in three ways:
Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models.
Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of “R for Data Science” is to help you learn the most important tools in R that will allow you to do data science.
R4DS teaches 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.
This chapter will show you how to use visualization and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle.
