Modern Statistics with R, 2nd Ed by Måns Thulin (.PDF)

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File Size: 10 MB

Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling: Second Edition by Måns Thulin
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Overview: The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. R is not like other statistical software packages. It is free, versatile, fast, and modern. It has a large and friendly community of users that help answer questions and develop new R tools. With more than 17,000 add-on packages available, R offers more functions for data analysis than any other statistical software. This includes specialised tools for disciplines as varied as political science, environmental chemistry, and astronomy, and new methods come to R long before they come to other programs. R makes it easy to construct reproducible analyses and workflows that allow you to easily repeat the same analysis more than once. R is not like other programming languages. It was developed by statisticians as a tool for data analysis and not by software engineers as a tool for other programming tasks. Some books on R focus entirely on Data Science – data wrangling and exploratory data analysis – ignoring the many great tools R has to offer for deeper data analyses. Many introductory books on statistical methods put too little focus on recent advances in computational statistics and advocate methods that have become obsolete. Far too few books contain discussions of ethical issues in statistical practice. This book aims to cover all of these topics and show you the state-of-the-art tools for all these tasks. It covers data science and (modern!) classical statistics as well as predictive modelling and machine learning, and deals with important topics that rarely appear in other introductory texts, such as simulation. It is written for R 4.3 or later and will teach you powerful add-on packages like data.table, dplyr, ggplot2, and caret. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices.
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