Search results for "R"
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R-ticulate
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.
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Data Wrangling with R
Take your data wrangling skills to the next level by gaining a deep understanding of tidyverse libraries and effectively prepare your data for impressive analysisPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore state-of-the-art libraries for data wrangling in R and learn to prepare your data for analysisFind out how to work with different data types such as strings, numbers, date, and timeBuild your first model and visualize data with ease through advanced plot types and with ggplot2Book DescriptionIn this information era, where large volumes of data are being generated every day, companies want to get a better grip on it to perform more efficiently than before. This is where skillful data analysts and data scientists come into play, wrangling and exploring data to generate valuable business insights. In order to do that, you'll need plenty of tools that enable you to extract the most useful knowledge from data.Data Wrangling with R will help you to gain a deep understanding of ways to wrangle and prepare datasets for exploration, analysis, and modeling. This data book enables you to get your data ready for more optimized analyses, develop your first data model, and perform effective data visualization.The book begins by teaching you how to load and explore datasets. Then, you'll get to grips with the modern concepts and tools of data wrangling. As data wrangling and visualization are intrinsically connected, you'll go over best practices to plot data and extract insights from it. The chapters are designed in a way to help you learn all about modeling, as you will go through the construction of a data science project from end to end, and become familiar with the built-in RStudio, including an application built with Shiny dashboards.By the end of this book, you'll have learned how to create your first data model and build an application with Shiny in R.What you will learnDiscover how to load datasets and explore data in RWork with different types of variables in datasetsCreate basic and advanced visualizationsFind out how to build your first data modelCreate graphics using ggplot2 in a step-by-step way in Microsoft Power BIGet familiarized with building an application in R with ShinyWho this book is forIf you are a professional data analyst, data scientist, or beginner who wants to learn more about data wrangling, this book is for you. Familiarity with the basic concepts of R programming or any other object-oriented programming language will help you to grasp the concepts taught in this book. Data analysts looking to improve their data manipulation and visualization skills will also benefit immensely from this book.
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Predictive Analytics For Business Using R
Predictive Analytics for Business using R by Russell R Barton equips business professionals and students with the tools to forecast trends, classify outcomes, and analyze time-dependent data using R programming. Focused on real-world business applications, the book covers essential topics such as regression analysis, classification models, time series forecasting, and simulation. It emphasizes how to assess uncertainty and interpret model results with confidence.Designed for case-based learning, each chapter is paired with real data and accompanying R code — making it ideal for hands-on practice in academic or professional settings. Whether you're analyzing customer behavior, forecasting sales, or modeling system performance, this book offers the analytical foundation to make smarter, data-driven decisions.
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Understanding and Applying Basic Statistical Methods Using R
Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining dataDetailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming.Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox’ Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.
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Optimization Modelling Using R
This book covers using R for doing optimization, a key area of operations research, which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R.With the rapid rise of interest in data analytics, a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools. Philosophically, the book emphasizes creating formulations before going intoimplementation. Algebraic representation allows for clear understanding and generalizationof large applications, and writing formulations is necessary to explain and convey the modeling decisions made.Appendix A introduces R. Mathematics is used at the level of subscripts and summations Refreshers are provided in Appendix B.This book: • Provides and explains code so examples are relatively clear and self-contained.• Emphasizes creating algebraic formulations before implementing.• Focuses on application rather than algorithmic details.• Embodies the philosophy of reproducible research.• Uses open-source tools to ensure access to powerful optimization tools.• Promotes open-source: all materials are available on the author’s github repository.• Demonstrates common debugging practices with a troubleshooting emphasis specific to optimization modeling using R.• Provides code readers can adapt to their own applications.This book can be used for graduate and undergraduate courses for students without a background in optimization and with varying mathematical backgrounds.
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R Companion to Epidemiology: Study Design and Data Analysis
R Companion to Epidemiology: Study Design and Data Analysis is a companion volume to the classic textbook by Mark Woodward, Epidemiology: Study Design and Data Analysis, Third Edition. It aims to equip the reader with sufficient knowledge to use R for practising epidemiology. Towards this aim, it reworks the examples in the textbook, presenting the code followed by an explanation and its result.Key Features:Almost all of the numerical examples in the textbook are reworked in RR code is introduced in small portions and explained thoroughlyComplexity of introduced code is increased only graduallyMore than 300 commands spanning more than 40 libraries are introducedThe book is intended primarily to be used as a supplement to the textbook by undergraduate and graduate students in the fields of epidemiology and statistics. It will also serve practitioners and researchers in epidemiology who want to learn R for use in their work.
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R Companion to Epidemiology: Study Design and Data Analysis
R Companion to Epidemiology: Study Design and Data Analysis is a companion volume to the classic textbook by Mark Woodward, Epidemiology: Study Design and Data Analysis, Third Edition. It aims to equip the reader with sufficient knowledge to use R for practising epidemiology. Towards this aim, it reworks the examples in the textbook, presenting the code followed by an explanation and its result.Key Features:Almost all of the numerical examples in the textbook are reworked in RR code is introduced in small portions and explained thoroughlyComplexity of introduced code is increased only graduallyMore than 300 commands spanning more than 40 libraries are introducedThe book is intended primarily to be used as a supplement to the textbook by undergraduate and graduate students in the fields of epidemiology and statistics. It will also serve practitioners and researchers in epidemiology who want to learn R for use in their work.
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Introduction to R and Python for Data Analysis
An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/randpythonbook/tree/master/data. Instructors can access the solutions manual via the book's website.Key features: - Teaches R and Python in a "side-by-side" way. - Examples are tailored to aspiring data scientists and statisticians, not software engineers. - Designed for introductory graduate students.- Does not assume any mathematical background.
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First R
This study looks into how children learn about the 'first R'—race—and challenges the current assumptions with case-study examples from three child-care centers.Parents and teachers will find this remarkable study reveals that the answer to how children learn about race might be more startling than could be imagined.
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Analyzing Linguistic Data
Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.
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Mastering Parallel Programming with R
Master the robust features of R parallel programming to accelerate your data science computationsAbout This Book• Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest• Become an expert in writing the most efficient and highest performance parallel algorithms in R• Get to grips with the concept of parallelism to accelerate your existing R programsWho This Book Is ForThis book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks.What You Will Learn• Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package• Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS)• Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code• Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages• Build and extend a parallel R package (SPRINT) with your own MPI-based routines• Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL• Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them• Build a task farm master-worker, spatial grid, and hybrid parallel R programsIn DetailR is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources.Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running
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Omic Association Studies with R and Bioconductor
After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions
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