Bøger af Thomas Mailund

Build working implementations of hash tables, written in the C programming language. The new and revised Second Edition has been enhanced with simplified code and more comprehensive explanations, aimed at improving clarity and comprehension while exploiting the latest features in C programming.This book starts with simple first attempts devoid of collision resolution strategies. It then progresses through improvements and extensions illustrating different design ideas and approaches, followed by experiments to validate the choices. You will learn how hash tables, when implemented and used appropriately, are exceptionally efficient data structures for representing sets and lookup tables, providing low overhead, constant time, insertion, deletion, and lookup operations. The Joys of Hashing walks you through the implementation of efficient hash tables and the pros and cons of different design choices when building tables. The source code used in the book is available on GitHub for your re-use and experiments.What You Will Learn Master the basic ideas behind hash tables Carry out collision resolution, including strategies for handling collisions and their consequences for performance Resize or grow and shrink tables as needed Store values by handling when values must be stored with keys to make general sets and maps

Master functions and discover how to write functional programs in R. In this book, updated for R 4, you'll learn to make your functions pure by avoiding side effects, write functions that manipulate other functions, and construct complex functions using simpler functions as building blocks.In Functional Programming in R 4, yoüll see how to 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.What You'll LearnWrite functions in R 4, including infix operators and replacement functions Create higher order functionsPass functions to other functions and start using functions as data you can manipulateUse Filer, Map and Reduce functions to express the intent behind code clearly and safelyBuild new functions from existing functions without necessarily writing any new functions, using point-free programmingCreate functions that carry data along with themWho This Book Is ForThose with at least some experience with programming in R.

In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. All source code used in the book is freely available on GitHub..  What You'll LearnImplement applicable R 4 programming language specification featuresImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize data with ggplot2 and fit data to models using modelrWho This Book Is ForProgrammers new to R's data science, data mining, and analytics packages.  Some prior coding experience with R in general is recommended.  

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, 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 4, Second Edition 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. Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications.  Source code will be available to support your next projects as well.Source code is available at github.com/Apress/beg-data-science-r4.What You Will LearnPerform data science and analytics using statistics and the R programming languageVisualize and explore data, including working with large data sets found in big dataBuild an R packageTest and check your codePractice version controlProfile and optimize your codeWho This Book Is ForThose with some data science or analytics background, but not necessarily experience with the R programming language.

Discover how to write manuscripts in Markdown and translate them with Pandoc into different output formats.

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.