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The objective of this book is to introduce participants to the use of R for data manipulation and analysis. It is intended for individuals with little or no prior experience in R. The topics that are covered are those that author thinks are the most important for getting started with R. By the end of the book, you should be able to complete all steps reguired for data analysis and visualization using R, including the use of some relatively sophisticated methods.
An Essential Reference for Intermediate and Advanced R Programmers
Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.
The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn:
The fundamentals of R, including standard data types and functions
Functional programming as a useful framework for solving wide classes of problems...
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.
The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate...
The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.
Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you're a beginner, this book will help get you started. If you're an intermediate user, this book will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.
There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively—until now. This hands-on book teaches novices and experienced R users how to write efficient R code.
Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf. Academics...
This book offers solutions to all 284 exercises in Advanced R, Second Edition. All the solutions have been carefully documented and made to be as clear and accessible as possible. Working through the exercises and their solutions will give you a deeper understanding of a variety of programming challenges, many of which are relevant to everyday work. This will expand your set of tools on a technical and conceptual level. You will be able to transfer many of the specific programming schemes directly and will discover far more elegant solutions to everyday problems.
Features:
When R creates copies, and how it affects memory...
Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special.
This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising
your code.
By reading this book, you will learn:
The difference between an object and its name, and why the distinction is important
The aim of this book is to introduce you to using R, a powerful and flexible interactive environment for statistical computing and research. R in itself is not difficult to learn, but as with learning any new language (spoken or computer) the initial learning curve can be a little steep and somewhat daunting.
This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.
Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.
Use R’s default graphics for quick exploration of data
Create a variety of bar graphs, line graphs, and scatter plots
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.
Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this...
Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to...
Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickhamâ??s package development philosophy. In the process, youâ??ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.
Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your...
Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.
RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.
Work hands-on with three practical data analysis projects...
Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.
RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it...
The world of R has evolved substantially since its early days as a statistical computing language. As the field of data science has rocketed to the forefront of all areas of scientific and industry work, R has become the centerpiece language for doing data science. Through the contributions of a vibrant and highly active developer community, R has evolved to the point where it can be considered a software development language for developing robust, modular, and highly reusable software tools.
We begin by providing a rigorous introduction to the R language, and quickly move on to more advanced aspects like functional programming...
Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more.
Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with...
"[This book] contains an excellent blend of both Shiny-specific topics … and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters…."
Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)
"[This] book is a gradual and pleasant invitation to the production-ready shiny apps world. It …exposes a comprehensive and robust workflow powered by the {golem} package. [It] fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one...
Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverseâ??a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.
You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the...
People Analytics (also known as HR Analytics or Workforce Analytics) refers to the analysis of data from human resources (human capital) in conjunction with other company data. People Analytics is based on research fields such as social psychology, motivational psychology and behavioral science as well as business intelligence, predictive analytics and big data.
This open access book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous...
The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data.
This book is an introduction to geographic data science using R, covers the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses.
Census data is widely used by practitioners to understand demographic change, allocate resources, address inequalities, and make sound business decisions. Until recently, projects using US Census data have required proficiency with multiple web interfaces and software platforms to prepare, map, and present data products. This book introduces readers to tools in the R programming language for accessing and analyzing Census data and shows how to carry out demographic analyses in a single computing environment.
Chapters in this book cover the following key topics:
• Rapidly acquiring data from the decennial US Census and American...
This short publication attempts to provide practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R code organized in custom-built functions and packages.
If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.
Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users.
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