Deep Learning And Scientific Computing With R Torch

torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++. Though still "young" as a project, R torch already has a vibrant community of users and developers. Experience shows that torch users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold: - Provide a thorough introduction to torch basics – both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch. - Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classification. - Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by torch code you can play with. Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.

-Written by the authors of key spatial R packages -Makes spatial data analysis more robust -Integrates with the tidyverse and comparable approaches -Includes many easily reproducible examples

Learning a computer language like R can be either frustrating, fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward for overcoming them. The book is designed so that it includes smaller and bigger challenges, in what I call playgrounds, in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. For students and professionals in the biological sciences, humanities and many applied fields, recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide, encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2024 and beyond. What is new in the second edition? Text expanded by more than 25% to include additional R features and gentler and more detailed explanations Contains 24 new diagrams and flowcharts, seven new tables, and revised text and code examples for clarity All three indexes were expanded, and answers to 28 frequently asked questions added What will you find in this book? Programming concepts explained as they apply to current R Emphasis on the role of abstractions in programming Few prescriptive rules—mostly the author’s preferences together with alternatives Presentation of the R language emphasizing the “R way of doing things” Tutoring for “programming in the small” using scripts for data analysis Explanation of the differences between R proper and extensions for data wrangling The grammar of graphics is described as a language for the construction of data visualisations Examples of data exchange between R and the foreign world using common file formats Coaching to become an independent R user, capable of writing original scripts and solving future challenges

This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. We then provide the code to prepare common open source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques. Highlights 1. Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. 2. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copy-pasting the code we provide. 3. A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. 4. Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets in the field of financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most relevant data characteristics. 5. Each chapter provides exercises that are based on established lectures and exercise classes and which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises.

Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.

The integration of geology with data science disciplines, such as spatial statistics, remote sensing, and geographic information systems (GIS), has given rise to a shift in many natural sciences schools, pushing the boundaries of knowledge and enabling new discoveries in geological processes and earth systems. Spatial analysis of geological data can be used to identify patterns and trends in data, to map spatial relationships, and to model spatial processes. R is a consolidated and yet growing statistical programming language with increasing value in spatial analysis often replacing, with advantage, GIS tools. By providing a comprehensive guide for geologists to harness the power of spatial analysis in R, Spatial Analysis in Geology Using R serves as a tool in addressing real-world problems, such as natural resource management, environmental conservation, and hazard prediction and mitigation. Features: Provides a practical and accessible overview of spatial analysis in geology using R Organised in three independent and complementary parts: Introduction to R, Spatial Analysis with R, and Spatial Statistics and Modelling Applied approach with many detailed examples and case studies using real geological data Presents a collection of R packages that are useful in many geological situations Does not assume any prior knowledge of R; all code are explained in detail Supplemented by a website with all data, code, and examples Spatial Analysis in Geology Using R will be useful to any geological researcher who has acquired basic spatial analysis skills, often using GIS, and is interested in deepening those skills through the use of R. It could be used as a reference by applied researchers and analysts in public, private, or third-sector industries. It could also be used to teach a course on the topic to graduate students or for self-study.

“Our community has continued to grow exponentially, thanks to those who inspire the next generation. And inspiring the next generation is what the authors of Analyzing Baseball Data with R are doing. They are setting the career path for still thousands more. We all need some sort of kickstart to take that first or second step. You may be a beginner R coder, but you need access to baseball data. How do you access this data, how do you manipulate it, how do you analyze it? This is what this book does for you. But it does more, by doing what sabermetrics does best: it asks baseball questions. Throughout the book, baseball questions are asked, some straightforward, and others more thought-provoking.” - From the Foreword by Tom Tango 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, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available for download online. New to the third edition is the revised R code to make use of new functions made available through the tidyverse. The third edition introduces three chapters of new material, focusing on communicating results via presentations using the Quarto publishing system, web applications using the Shiny package, and working with large data files. An online version of this book is hosted at https://beanumber.github.io/abdwr3e/.

Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the mapping of geographic data. Both static and interactive graphics are described and the use of color, shape, shading, grouping, annotation, and animations are covered in detail. The book moves from a default look and feel for graphs, to graphs with customized colors, fonts, legends, annotations, and organizational themes. Features Contains a wide breadth of graph types including newer and less well-known approaches Connects each graph type to the characteristics of the data and the goals of the analysis Moves the reader from simple graphs describing one variable to building visualizations that describe complex relationships among many variables Provides newer approaches to creating interactive web graphics via JavaScript libraries Details how to customize each graph type to meet users’ needs and those of their audiences Gives methods for creating visualizations that are publication ready for print (in color or black and white) and the web Suggests best practices Offers examples from a wide variety of fields The book is written for those new to data analysis as well as the seasoned data scientist. It can be used for both teaching and research, and will particularly appeal to anyone who needs to describe data visually and wants to find and emulate the most appropriate method quickly. The reader should have some basic coding experience, but expertise in R is not required. Some of the later chapters (e.g., visualizing statistical models) assume exposure to statistical inference at the level of analysis of variance and regression.