Bøker av 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

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.