The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin writing programs in R. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing tests and modeling. You’ll even learn how to create impressive data visualizations with R’s graphics tools and contributed packages, like ggplot2, ggvis, and rgl. Dozens of hands-on exercises take you from theory to practice as you learn: The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling and how to execute them in R How to access R’s thousands of functions, libraries, and datasets How to draw valid and useful conclusions from your data and create publication-quality graphics of your results The Book of R brings both statistics and R to life. With clear explanations, practical examples, and hands-on exercises, this book opens the door to the evolving world of data analysis. New to this edition: The entire book has been revised and expanded, with nearly 100 pages of new content and exercises. You’ll find greater coverage of data plots and R graphics, guidance on using pipes to string together commands, and new ways to read and write external files, among many other lessons.

Simplify stats and learn how to graph, analyze, and interpret data the easy way Statistical Analysis with R For Dummies makes stats approachable by combining clear explanations with practical applications. You'll learn how to download and use R and RStudio—two free, open-source tools—to learn statistics concepts, create graphs, test hypotheses, and draw meaningful conclusions. Get started by learning the basics of statistics and R, calculate descriptive statistics, and use inferential statistics to test hypotheses. Then, visualize it all with graphs and charts. This Dummies guide is your well-marked path to sailing through statistics. Get clear explanations of the basics of statistics and data analysis Learn how to analyze and visualize data with R, step by step Create charts, graphs, and summaries to interpret results Explore hypothesis testing, and prediction techniques This is the perfect introduction to R for students, professionals, and the stat-curious.

A practical guide to reproducible and high impact mass spectrometry data analysis R Programming for Mass Spectrometry teaches a rigorous and detailed approach to analyzing mass spectrometry data using the R programming language. It emphasizes reproducible research practices and transparent data workflows and is designed for analytical chemists, biostatisticians, and data scientists working with mass spectrometry. Readers will find specific algorithms and reproducible examples that address common challenges in mass spectrometry alongside example code and outputs. Each chapter provides practical guidance on statistical summaries, spectral search, chromatographic data processing, and machine learning for mass spectrometry. Key topics include: Comprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data Processing chromatographic peaks, peak detection, and quality control in mass spectrometry data Applying machine learning techniques, using Tidymodels for supervised and unsupervised learning, as well as for feature engineering and selection, providing modern approaches to data-driven insights Methods for producing reproducible, publication-ready reports and web pages using RMarkdown R Programming for Mass Spectrometry is an indispensable guide for researchers, instructors, and students. It provides modern tools and methodologies for comprehensive data analysis. With a companion website that includes code and example datasets, it serves as both a practical guide and a valuable resource for promoting reproducible research in mass spectrometry.

This book is an essential guide designed to equip you with the vital tools and knowledge needed to excel in data science. Master the end-to-end process of data collection, processing, validation, and imputation using R, and understand fundamental theories to achieve transparency with literate programming, renv, and Git--and much more. Each chapter is concise and focused, rendering complex topics accessible and easy to understand. Data Insight Foundations caters to a diverse audience, including web developers, mathematicians, data analysts, and economists, and its flexible structure allows enables you to explore chapters in sequence or navigate directly to the topics most relevant to you. While examples are primarily in R, a basic understanding of the language is advantageous but not essential. Many chapters, especially those focusing on theory, require no programming knowledge at all. Dive in and discover how to manipulate data, ensure reproducibility, conduct thorough literature reviews, collect data effectively, and present your findings with clarity. What You Will Learn Data Management: Master the end-to-end process of data collection, processing, validation, and imputation using R. Reproducible Research: Understand fundamental theories and achieve transparency with literate programming, renv, and Git. Academic Writing: Conduct scientific literature reviews and write structured papers and reports with Quarto. Survey Design: Design well-structured surveys and manage data collection effectively. Data Visualization: Understand data visualization theory and create well-designed and captivating graphics using ggplot2. Who this Book is For Career professionals such as research and data analysts transitioning from academia to a professional setting where production quality significantly impacts career progression. Some familiarity with data analytics processes and an interest in learning R or Python are ideal.

Causal Inference in R is a comprehensive guide that introduces you to the methods and practices of determining causality in data through the lens of R programming. By navigating its pages and examples, you'll master the application of causal models and statistical approaches to real-world problems, enabling more informed data-driven decisions. What this Book will help me do Understand the principles and foundations of causal inference to identify causality in data. Apply methods like propensity score matching and instrumental variables using R. Leverage real-world case studies to analyze and resolve confounding factors and make better data claims. Harness statistical methods and R tools to address real-world data challenges innovatively. Develop a strategy for integrating causal models into decision-making workflows with confidence. Author(s) Subhajit Das, the author of Causal Inference in R, is an accomplished applied scientist with over a decade of experience in causal inference methodologies and data analysis. Subhajit is passionate about empowering learners by breaking down complex concepts into manageable, clear explanations. His expertise ensures that readers not only understand the theory behind causal inference but are also able to apply it effectively using R. Who is it for? This book is ideal for data analysts, statisticians, and researchers looking to deepen their understanding of causal inference techniques using R. Whether you're a practitioner aiming to enhance your data-driven decision-making skills or a student aspiring to tackle advanced causal analysis, this book provides pathbreaking insights. It's suitable for individuals at beginner to intermediate skill levels in data analysis, especially those in public policy, economics, and the social sciences who utilize R regularly.

Understand how to use R and R Studio to analyse HR data and deliver insights that drive workforce and business performance.

Analyzing Baseball Data with R Third Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps.

An accessible learning resource that develops data analysis skills for natural science students in an efficient style using the R programming language R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists is a compact, example-based, and user-friendly statistics textbook without unnecessary frills, but instead filled with engaging, relatable examples, practical tips, online exercises, resources, and references to extensions, all on a level that follows contemporary curricula taught in large parts of the world. The content structure is unique in the sense that statistical skills are introduced at the same time as software (programming) skills in R. This is by far the best way of teaching from the authors’ experience. Readers of this introductory text will find: Explanations of statistical concepts in simple, easy-to-understand language A variety of approaches to problem solving using both base R and tidyverse Boxes dedicated to specific topics and margin text that summarizes key points A clearly outlined schedule organized into 12 chapters corresponding to the 12 semester weeks of most universities While at its core a traditional printed book, R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists comes with a wealth of online teaching material, making it an ideal and efficient reference for students who wish to gain a thorough understanding of the subject, as well as for instructors teaching related courses.

The R programming language is a remarkably powerful tool for data analysis and visualization, but its steep learning curve can be intimidating for some. If you just want to automate repetitive tasks or visualize your data, without the need for complex math, R for the Rest of Us is for you. Inside you’ll find a crash course in R, a quick tour of the RStudio programming environment, and a collection of real-word applications that you can put to use right away. You’ll learn how to create informative visualizations, streamline report generation, and develop interactive websites—whether you’re a seasoned R user or have never written a line of R code. You’ll also learn how to: Manipulate, clean, and parse your data with tidyverse packages like dplyr and tidyr to make data science operations more user-friendly Create stunning and customized plots, graphs, and charts with ggplot2 to effectively communicate your data insights Import geospatial data and write code to produce visually appealing maps automatically Generate dynamic reports, presentations, and interactive websites with R Markdown and Quarto that seamlessly integrate code, text, and graphics Develop custom functions and packages tailored to your specific needs, allowing you to extend R’s functionality and automate complex tasks Unlock a treasure trove of techniques to transform the way you work. With R for the Rest of Us, you’ll discover the power of R to get stuff done. No advanced statistics degree required.

Confidently use predictive analytic and statistical techniques to identify key relationships and trends in HR-related data to aid strategic organizational decision-making.

The easy way to get started coding and analyzing data in the R programming language Statistical Analysis with R Essentials For Dummies is your reference to all the core concepts about R—the widely used, open-source programming language and data analysis tool. This no-nonsense book gets right to the point, eliminating review material, wordy explanations, and fluff. Understand all you need to know about the foundations of R, swiftly and clearly. Perfect for a brush-up on the basics or as an everyday desk reference on the job, this is the reliable little book you can always turn to for answers. Get a quick and thorough intro to the basic concepts of coding for data analysis in R Review what you've already learned or pick up essential new skills Perform statistical analysis for school, business, and beyond with R programming Keep this concise reference book handy for jogging your memory as you work This book is to the point, focusing on the key topics readers need to know about this popular programming language. Great for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job.

Near Extensions and Alignment of Data in Rn Comprehensive resource illustrating the mathematical richness of Whitney Extension Problems, enabling readers to develop new insights, tools, and mathematical techniques Near Extensions and Alignment of Data in Rn demonstrates a range of hitherto unknown connections between current research problems in engineering, mathematics, and data science, exploring the mathematical richness of near Whitney Extension Problems, and presenting a new nexus of applied, pure and computational harmonic analysis, approximation theory, data science, and real algebraic geometry. For example, the book uncovers connections between near Whitney Extension Problems and the problem of alignment of data in Euclidean space, an area of considerable interest in computer vision. Written by a highly qualified author, Near Extensions and Alignment of Data in Rn includes information on: Areas of mathematics and statistics, such as harmonic analysis, functional analysis, and approximation theory, that have driven significant advances in the field Development of algorithms to enable the processing and analysis of huge amounts of data and data sets Why and how the mathematical underpinning of many current data science tools needs to be better developed to be useful New insights, potential tools, and mathematical techniques to solve problems in Whitney extensions, signal processing, shortest paths, clustering, computer vision, optimal transport, manifold learning, minimal energy, and equidistribution Providing comprehensive coverage of several subjects, Near Extensions and Alignment of Data in Rn is an essential resource for mathematicians, applied mathematicians, and engineers working on problems related to data science, signal processing, computer vision, manifold learning, and optimal transport.

This book guides readers through the essentials of applied statistics and machine learning using the R programming language. By delving into robust data processing techniques, visualization, and statistical modeling with R, you will develop skills to effectively analyze data and design predictive models. Each chapter includes hands-on exercises to reinforce the concepts in a practical, intuitive way. What this Book will help me do Understand and apply key statistical concepts such as probability distributions and hypothesis testing to analyze data. Master foundational mathematical principles like linear algebra and calculus relevant to data science and machine learning. Develop proficiency in data manipulation and visualization using robust R libraries such as dplyr and ggplot2. Build predictive models through practical exercises and learn advanced concepts like Bayesian statistics and linear regression. Gain the practical knowledge needed to apply statistical and machine learning methodologies in real-world scenarios. Author(s) Liu Peng is an accomplished author with a strong academic and practical background in statistics and data science. Armed with extensive experience in applying R to real-world problems, he brings a blend of technical mastery and teaching expertise. His commitment is to transform complex concepts into accessible, enriching learning experiences for readers. Who is it for? This book is ideal for data scientists and analysts ranging from beginners to those at an intermediate level. It caters especially to those interested in practicing statistical modeling and learning R in depth. If you have basic familiarity with statistics and are looking to expand your data science capabilities using R, this book is well-suited for you.

A companion to Remote Sensing and Digital Image Processing with R, this lab manual covers examples of natural resource data analysis applications including practical, problem-solving exercises and case studies that use the free and open-source platform R.

Turn your R code into packages that others can easily install and use. With this fully updated edition, developers and data scientists will learn how to bundle reusable R functions, sample data, and documentation together by applying the package development philosophy used by the team that maintains the "tidyverse" suite of packages. In the process, you'll learn how to automate common development tasks using a set of R packages, including devtools, usethis, testthat, and roxygen2. Authors Hadley Wickham and Jennifer Bryan from Posit (formerly known as RStudio) help you create packages quickly, then teach you how to get better over time. You'll be able to focus on what you want your package to do as you progressively develop greater mastery of the structure of a package. With this book, you will: Learn the key components of an R package, including code, documentation, and tests Streamline your development process with devtools and the RStudio IDE Get tips on effective habits such as organizing functions into files Get caught up on important new features in the devtools ecosystem Learn about the art and science of unit testing, using features in the third edition of testthat Turn your existing documentation into a beautiful and user friendly website with pkgdown Gain an appreciation of the benefits of modern code hosting platforms, such as GitHub

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 tidyverse—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 latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto

TWO-DIMENSIONAL (2D) NMR METHODS Practical guide explaining the fundamentals of 2D-NMR for experienced scientists as well as relevant for advanced students Two-Dimensional (2D) NMR Methods is a focused work presenting an overview of 2D-NMR concepts and techniques, including basic principles, practical applications, and how NMR pulse sequences work. Contributed to by global experts with extensive experience in the field, Two-Dimensional (2D) NMR Methods provides in-depth coverage of sample topics such as: Basics of 2D-NMR, data processing methods (Fourier and beyond), product operator formalism, basics of spin relaxation, and coherence transfer pathways Multidimensional methods (single- and multiple-quantum spectroscopy), NOESY (principles and applications), and DOSY methods Multiple acquisition strategies, anisotropic NMR in molecular analysis, ultrafast 2D methods, and multidimensional methods in bio-NMR TROSY (principles and applications), field-cycling and 2D NMR, multidimensional methods and paramagnetic NMR, and relaxation dispersion experiments This text is a highly useful resource for NMR specialists and advanced students studying NMR, along with users in research, academic and commercial laboratories that study or conduct experiments in NMR.

This book illustrates how data can be useful in solving business problems. It explores various analytics techniques for using data to discover hidden patterns and relationships, predict future outcomes, optimize efficiency and improve the performance of organizations. You’ll learn how to analyze data by applying concepts of statistics, probability theory, and linear algebra. In this new edition, both R and Python are used to demonstrate these analyses. Practical Business Analytics Using R and Python also features new chapters covering databases, SQL, Neural networks, Text Analytics, and Natural Language Processing.Part one begins with an introduction to analytics, the foundations required to perform data analytics, and explains different analytics terms and concepts such as databases and SQL, basic statistics, probability theory, and data exploration. Part two introduces predictive models using statistical machine learning and discusses concepts like regression, classification, and neural networks. Part three covers two of the most popular unsupervised learning techniques, clustering and association mining, as well as text mining and natural language processing (NLP). The book concludes with an overview of big data analytics, R and Python essentials for analytics including libraries such as pandas and NumPy. Upon completing this book, you will understand how to improve business outcomes by leveraging R and Python for data analytics. What You Will Learn Master the mathematical foundations required for business analytics Understand various analytics models and data mining techniques such as regression, supervised machine learning algorithms for modeling, unsupervised modeling techniques, and how to choose the correct algorithm for analysis in any given task Use R and Python to develop descriptive models, predictive models, and optimize models Interpret and recommend actions based on analytical model outcomes Who This Book Is For Software professionals and developers, managers, and executives who want to understand and learn the fundamentals of analytics using R and Python.

Data Wrangling with R guides you through mastering data preparation in the R programming language using tidyverse libraries. You will learn techniques to load, explore, transform, and visualize data effectively, gaining the skills needed for data modeling and insights extraction. What this Book will help me do Understand how to use R and tidyverse libraries to handle data wrangling tasks. Learn methods to work with diverse data types like numbers, strings, and dates. Gain proficiency in building visual representations of data using ggplot2. Build and validate your first predictive model for useful insights. Create an interactive web application with Shiny in R. Author(s) Gustavo Santos is an experienced data scientist specializing in R programming and data visualization. With a background in statistics and several years of professional experience in industry and academia, Gustavo excels at translating complex data analytics concepts into practical skills. His approach to teaching is hands-on and example-driven, aiming to empower readers to excel in real-world applications. Who is it for? If you are a data scientist, data analyst, or even a beginner programmer who wants to enhance their data manipulation and visualization skills, this book is perfect for you. Familiarity with R or a general understanding of programming concepts is suggested but not mandatory. It caters to professionals looking to refine their data wrangling workflow and to students aspiring to break into data-centered fields. By the end, you'll be ready to apply data wrangling and visualization tools in your projects.

A deep dive into the programming language of choice for statistics and data With R All-in-One For Dummies, you get five mini-books in one, offering a complete and thorough resource on the R programming language and a road map for making sense of the sea of data we're all swimming in. Maybe you're pursuing a career in data science, maybe you're looking to infuse a little statistics know-how into your existing career, or maybe you're just R-curious. This book has your back. Along with providing an overview of coding in R and how to work with the language, this book delves into the types of projects and applications R programmers tend to tackle the most. You'll find coverage of statistical analysis, machine learning, and data management with R. Grasp the basics of the R programming language and write your first lines of code Understand how R programmers use code to analyze data and perform statistical analysis Use R to create data visualizations and machine learning programs Work through sample projects to hone your R coding skill This is an excellent all-in-one resource for beginning coders who'd like to move into the data space by knowing more about R.