Thomas Mailund Book order

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.

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.

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 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.

Focusing on practical applications, this guide delves into data structures and algorithms specifically for text search, illustrating their integration into larger systems. It provides an in-depth exploration of string algorithms, all implemented in the C programming language, making it a valuable resource for understanding foundational concepts in computer science.

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.

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.

Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. Along the way, you’ll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context. Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. After reading and using this book, you’ll understand how to write DSLs in R and have skills you can extrapolate to other programming languages. What You'll Learn Program with domain-specific languages using R Discover the components of DSLs Carry out large matrix expressions and multiplications Implement metaprogramming with DSLs Parse and manipulate expressions Define text strings to search for or specify rules for modifying text using regular expressions Use continuous time Markov chains (CTMCs) Who This Book Is For Those with prior programming experience. R knowledge is helpful but not required.

Master functions and discover how to write functional programs in R. In this book, you'll make your functions pure by avoiding side-effects; you'll write functions that manipulate other functions, and you'll construct complex functions using simpler functions as building blocks. In Functional Programming in R, you'll see how we can replace loops, which can have side-effects, with recursive functions that can more easily avoid them. In addition, the book covers why you shouldn't use recursion when loops are more efficient and how you can get the best of both worlds. Functional programming is a style of programming, like object-oriented programming, but one that focuses on data transformations and calculations rather than objects and state. Where in object-oriented programming you model your programs by describing which states an object can be in and how methods will reveal or modify that state, in functional programming you model programs by describing how functions translate input data to output data. Functions themselves are considered to be data you can manipulate and much of the strength of functional programming comes from manipulating functions; that is, building more complex functions by combining simpler functions. You will: Write functions in R including infix operators and replacement functions Create higher order functions Pass functions to other functions and start using functions as data you can manipulate Use Filer, Map and Reduce functions to express the intent behind code clearly and safely Build new functions from existing functions without necessarily writing any new functions, using point-free programming Create functions that carry data along with them