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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. It is designed from the ground up to handle data, which is evident. But it is also flexible enough to be used to create interactive web pages, automated reports, and application programming interfaces (APIs). Simply put, R is currently the best tool there is for data analysis.
This book was born out of lecture notes and materials that I created for courses at the University of Edinburgh, Uppsala University, Dalarna University, the Swedish University of Agricultural Sciences, and Karolinska Institutet. It can be used as a textbook, for self-study, or as a reference manual for R. No background in programming is assumed. This is not a book that has been written with the intention that you should read it back-to-back. Rather, it is intended to serve as a guide to what to do next as you explore R. Think of it as a conversation, where you and I discuss different topics related to data analysis and data wrangling. At times I’ll do the talking, introduce concepts and pose questions. At times you’ll do the talking, working with exercises and discovering all that R has to offer. The best way to learn R is to use R. You should strive for active learning, meaning that you should spend more time with R and less time stuck with your nose in a book. Together we will strive for an exploratory approach, where the text guides you to discoveries and the exercises challenge you to go further. This is how I’ve been teaching R since 2008, and I hope that it’s a way that you will find works well for you. The book contains more than 200 exercises. Apart from a number of open-ended questions about ethical issues, all exercises involve R code. These exercises all have worked solutions available on the book’s webpage. It is highly recommended that you actually work with all the exercises, as they are central to the approach to learning that this book seeks to support: using R to solve problems is a much better way to learn the language than to just read about how to use R to solve problems.
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. Others focus on predictive modelling or classical statistics but ignore data-handling, which is a vital part of modern statistical work. 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.
The expanded second edition includes new and updated examples throughout the book, and new material on, among other things, fundamental statistical concepts, survival analysis, and structural equation models.
It teaches you:
Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.
Exploratory data analysis - using visualisations and multivariate techniques to explore datasets.
Statistical inference - modern methods for testing hypotheses and computing confidence intervals.
Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting.
Simulation - using simulation techniques for sample size computations and evaluations of statistical methods.
Ethics in statistics - ethical issues and good statistical practice.
R programming - writing code that is fast, readable, and (hopefully!) free from bugs.
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. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book