E-raamat: R for Non-Programmers

The book introduces interactive elements, including chapter exercises in the accompanying R package, facilitating readers internalising this new programming language and statistical techniques. This interactive approach, particularly beneficial for novices, enhances the overall learning experience and distinguishes it as a valuable resource.

Unlock the Power of Data Analysis with R
Whether you are a researcher, student, or professional new to programming, this book provides a step-by-step guide to mastering R for quantitative and mixed-methods analysis. Designed for those who still need to gain programming experience or wish to learn a new one, it demystifies data analysis, helping you tackle challenges from data wrangling to statistical modelling. Packed with practical examples, engaging explanations, and real-world applications, this book equips you with the tools to analyse data confidently, identify trends, and uncover meaningful insights.

Transform Your Approach to Research
Through clear instructions and hands-on exercises, you will learn to prepare datasets, explore patterns with descriptive statistics, and create impactful visualisations. You will also gain confidence in performing statistical tests such as comparing groups, and building predictive models using regression techniques. This book provides strategies and tools to streamline your workflow, whether handling large datasets, managing missing data, or conducting mixed-methods research.
Each chapter builds your expertise incrementally, supported by accessible examples and interactive online training. The accompanying training modules, available through the book’s companion package, offer engaging exercises and extended examples to reinforce learning. These features enable you to practise skills and retain knowledge more effectively.
This book, which strongly focuses on reproducible research, is an indispensable guide for anyone looking to enhance their analytical toolkit and unlock R's full potential for data analysis and statistical modelling.