Автор: Mark Gardener
Издательство: Pelagic Publishing
Год: 2023
Страниц: 384
Язык: английский
Формат: epub (true)
Размер: 10.2 MB
The modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science.
Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.
Who this book is for:
This book is an introduction to using R: The Statistical Programming Language. Regardless of your background or specialty – science, business, engineering, or social science – you’ll find this book a starting point for learning about Data Science, that is, Data Analysis, Data Visualization, and Data Management. This book is not aimed at any particular educational level and should be accessible to anyone who wants to learn R, a powerful and flexible analytical toolbox.
What you will learn:
In this book you will learn how to use the R program. You’ll learn how to get started with R, in particular:
• making and importing data items
• exporting data
• managing and manipulating data objects
• summarizing and aggregating data
• visualizing data
• the basics of Data Analysis, including:
• differences tests
• correlation
• association
• regression
• R programming functions
This book is intended as a foundation course in using R for Data Science and a springboard for further exploration into Data Analysis and Data Visualization. Not every R command is shown (there are so many!), and the emphasis is on those that the author deemed most useful. I apologize in advance if you are an R user already, and your favorite function is missing.
Contents:
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