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14.8 MB
Info Hash:
51748AE9A3C914FF15B03E7F7B6A7064B69A0C4C
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June 13, 2025, 11:30 a.m.
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(Last updated: June 17, 2025, 6:57 a.m.)
| File | Size |
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| ['Dauber D. R for Non-Programmers 2025.pdf'] | 0 bytes |
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14.8 MB
[23
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2025-06-13
| Uploaded by andryold1 | Size 14.8 MB | Health [ 23 /27 ] | Added 2025-06-13 |
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89.7 MB
[40
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2025-09-06
| Uploaded by andryold1 | Size 89.7 MB | Health [ 40 /13 ] | Added 2025-09-06 |
NOTE
SOURCE: Dauber D. R for Non-Programmers 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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 program- ming 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. Preface Readme. before you get started Why learn a programming language as a non-programmer? Setting up R and RStudio The RStudio Interface R Basics: The very fundamentals Starting your R projects Data Wrangling Descriptive Statistics Sources of Bias: Outliers, Normality and other 'Conundrums' Correlations Power: You either have it or you don't Comparing Groups Regression: Creating Models to Predict Future Observations Mixed-Methods Research: Analysing Qualitative Data in R Where to go from here: The next steps in your R journey Epilogue Appendix References
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