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. 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.

本書の真髄は、単なるコードの羅列ではない、データを「語らせる」ための美学にあります。著者マイランドは、統計学と計算機科学が交差する地平を、緻密な物語を編むように描き出しました。Rという言語を通じて無機質なデータから意味あるパターンを抽出するプロセスは、知の探究における一種の叙事詩とも呼べる深みを湛えています。 解析に留まらず、パッケージ開発の規律を重んじる姿勢は、読者を単なる使い手から、持続可能な知を構築する「表現者」へと昇華させます。洗練されたコードこそが最強の武器であることを示す本書は、データサイエンスという広大な荒野を往く者にとって、情熱に満ちた究極の羅針盤となるはずです。