This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

This book is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities.

This book 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.

This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly - without having to comb through all the details of R's graphing systems.

The main aim of this book is to help you climb the initial learning curve and provide you with the basic skills and experience (and confidence!) to enable you to further your experience in using R.

This book helps you understand how R works at a fundamental level. Designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages to understand what makes R different and special.

This book offers solutions to the exercises from Advanced R, 2nd Edition by Hadley Wickham. It is work in progress and under active development. The 2nd edition of Advanced R is in print now and we hope to provide most of the answers.

This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

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's package development philosophy.

This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

Introduces concepts and skills that can help tackling real-world data analysis challenges. Covers concepts from probability, statistical inference, linear regression, and machine learning. Helps developing skills such as R programming, data wrangling, etc.

The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

This book is about increasing the amount of work you can do with R in a given amount of time. It's about both computational and programmer efficiency. This book is for anyone who wants to make their use of R more reproducible, scalable, and faster.

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.

This book aims at giving a wide understanding of many aspects of R. Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R's functionality.

This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.

It provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

It provides an introduction to Bayesian Inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R Programming for anyone interested in learning about Bayesian statistics.

An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, it brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. Integrates R code, including RStan modeling tools, bayesrules package.

This book provides a self-contained introduction to computational Bayesian statistics using R programming language. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability.

This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. All examples implemented using R.

This is an introduction to statistics, with R, without calculus, for students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.

This book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.

This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.

This book takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required.

You'll explore text-mining techniques with tidytext, a package that authors 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.

This book presents useful tools and techniques for attacking many types of R programming problems. It not only helps current R users become R programmers but also shows existing programmers what's special about R.

The objective of this book is to introduce participants to the use of R for data manipulation and analysis. It is intented for individuals with little or no prior experience in R. The topics that are covered are the most important for getting started with R.

Learn R programming for data analysis in a single day. The book aims to teach data analysis using R within a single day to anyone who already knows some programming in any other language.

This book is an essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. If you are using spreadsheets to understand data, switch to R.

A guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

This book has a two-fold aim: to learn the basics of R and to acquire basic skills for programming efficiently in R. Emphasis is on converting ideas about analysing data into useful R programs.

A straightforward introduction to the statistical analysis of language, designed for those with a non-mathematical background. Using the leading statistics programme 'R'. Suitable for all those working with quantitative language data.

This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics.

This is a textbook for an undergraduate course in probability and statistics. Calculus and some linear algebra knowledge is required.

Begin developing your mastery of the powerful R programming language. Become comfortable with the R environment and learn how to find ways for R to fulfill your data needs.

This book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data.

This tutorial manual provides a comprehensive introduction to R, an open source software package for statistical computing and graphics

It presents R packages, functions, classes and methods for handling spatial data, and showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping.

This book is the perfect guide for newcomers as well as seasoned users of R who want concrete, step-by-step guidance on how to use the software easily and effectively for nearly any statistical analysis.

This book lays the foundation for further study and development in statistics using R - an ideal text for integrating the study of statistics with a powerful computational tool.

This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.