Thomas Mailund Livres

Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software. After reading Advanced Object-Oriented Programming in R, you'll come away with a practical project that you can reuse in your own analytics coding endeavors. You’ll then be able to visualize your data as objects that have state and then manipulate those objects with polymorphic or generic methods. Your projects will benefit from the high degree of flexibility provided by polymorphism, where the choice of concrete method to execute depends on the type of data being manipulated. What You'll Learn Define and use classes and generic functions using R Work with the R class hierarchies Benefit from implementation reuse Handle operator overloading Apply the S4 and R6 classes Who This Book Is For Experienced programmers and for those with at least some prior experience with R programming language. /div

Learn how to manipulate functions and expressions to modify how the R language interprets itself. This book is an introduction to metaprogramming in the R language, so you will write programs to manipulate other programs. Metaprogramming in R shows you how to treat code as data that you can generate, analyze, or modify. R is a very high-level language where all operations are functions and all functions are data that can be manipulated. This book shows you how to leverage R's natural flexibility in how function calls and expressions are evaluated, to create small domain-specific languages to extend R within the R language itself. What You'll Learn Find out about the anatomy of a function in R Look inside a function call Work with R expressions and environments Manipulate expressions in R Use substitutions Who This Book Is For Those with at least some experience with R and certainly for those with experience in other programming languages.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

Focusing on the intricacies of pointers in C programming, this contemporary guide offers a comprehensive exploration from fundamental concepts to advanced applications. It serves as an essential resource for professionals and advanced students, featuring hands-on coverage of pointer mechanics at the machine level. The book incorporates the latest versions of the C language, including C20, C17, and C14, ensuring readers are equipped with up-to-date knowledge and practical skills.

The book guides readers through the process of creating hash tables in C, beginning with basic implementations that lack collision resolution. It progressively explores various design strategies and enhancements, demonstrating how to refine these structures. Through practical experiments, readers validate their design choices, making it a comprehensive resource for understanding both the theory and application of hash tables in programming.

You have heard about git and GitHub and want to know what the buzz is about. That is what I am here to tell you. Or, at least, I am here to give you a quick overview of what you can do with git and GitHub. I won't be able, in the space here, to give you an exhaustive list of features-in all honesty, I don't know enough myself to be able to claim expertise with these tools. I am only a frequent user, but I can get you started and give you some pointers for where to learn more. That is what this booklet is for.

This quick reference guide introduces various R data science packages, offering concise explanations and practical examples for each. Readers will explore essential APIs such as readr, lubridate, dplyr, and ggplot2, among others. The book emphasizes clarity and accessibility, making it a valuable resource for both beginners and experienced users looking to enhance their R programming skills through illustrative examples.

The book delves into computational thinking and the principles of algorithm design, emphasizing their practical applications in software development. Readers will explore various algorithms that are foundational to nearly all computer programs, providing a comprehensive understanding of how these algorithms function and their significance in the tech landscape.

The book focuses on mastering functions in R, particularly in the context of R 4. It teaches readers how to create pure functions that avoid side effects, manipulate functions within other functions, and build complex functions using simpler ones as foundational elements. This practical approach equips programmers with the skills to enhance their functional programming techniques in R.