| Description |
1 online resource (327 pages) |
| Contents |
Cover; Half Title; Title; Copyright; Contents; Acknowledgments; 0. Preface: Approach and How to Use This Book; 0.1. Strategy of the Book; 0.2. Why R?; 0.3. Why the Tidyverse?; 0.4. R Packages Required for This Book; 0.5. What This Book Is Not; 0.6. How to Use This Book; 0.7. Information for Teachers; 1 Introduction to R; 1.1. Introduction; 1.2. Baby Steps: Simple Math with R; 1.3. Your First R Script; 1.4. Assigning Variables; 1.5. Numeric Vectors; 1.6. Indexing; 1.7. Logical Vectors; 1.8. Character Vectors; 1.9. Factor Vectors; 1.10. Data Frames; 1.11. Loading in Files; 1.12. Plotting |
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1.13. Installing, Loading, and Citing Packages1.14. Seeking Help; 1.15. A Note on Keyboard Shortcuts; 1.16. Your R Journey: The Road Ahead; 2 The Tidyverse and Reproducible R Workflows; 2.1. Introduction; 2.2. tibble and readr; 2.3. dplyr; 2.4. ggplot2; 2.5. Piping with magrittr; 2.6. A More Extensive Example: Iconicity and the Senses; 2.7. R Markdown; 2.8. Folder Structure for Analysis Projects; 2.9. Readme Files and More Markdown; 2.10. Open and Reproducible Research; 3 Descriptive Statistics, Models, and Distributions; 3.1. Models; 3.2. Distributions; 3.3. The Normal Distribution |
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3.4. Thinking of the Mean as a Model3.5. Other Summary Statistics: Median and Range; 3.6. Boxplots and the Interquartile Range; 3.7. Summary Statistics in R; 3.8. Exploring the Emotional Valence Ratings; 3.9. Chapter Conclusions; 4 Introduction to the Linear Model: Simple Linear Regression; 4.1. Word Frequency Effects; 4.2. Intercepts and Slopes; 4.3. Fitted Values and Residuals; 4.4. Assumptions: Normality and Constant Variance; 4.5. Measuring Model Fit with R2; 4.6. A Simple Linear Model in R; 4.7. Linear Models with Tidyverse Functions; 4.8. Model Formula Notation: Intercept Placeholders |
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4.9. Chapter Conclusions5 Correlation, Linear, and Nonlinear Transformations; 5.1. Centering; 5.2. Standardizing; 5.3. Correlation; 5.4. Using Logarithms to Describe Magnitudes; 5.5. Example: Response Durations and Word Frequency; 5.6. Centering and Standardization in R; 5.7. Terminological Note on the Term 'Normalizing'; 5.8. Chapter Conclusions; 6 Multiple Regression; 6.1. Regression with More Than One Predictor; 6.2. Multiple Regression with Standardized Coefficients; 6.3. Assessing Assumptions; 6.4. Collinearity; 6.5. Adjusted R2; 6.6. Chapter Conclusions; 7 Categorical Predictors |
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7.1. Introduction7.2. Modeling the Emotional Valence of Taste and Smell Words; 7.3. Processing the Taste and Smell Data; 7.4. Treatment Coding in R; 7.5. Doing Dummy Coding 'By Hand'; 7.6. Changing the Reference Level; 7.7. Sum-coding in R; 7.8. Categorical Predictors with More Than Two Levels; 7.9. Assumptions Again; 7.10. Other Coding Schemes; 7.11. Chapter Conclusions; 8 Interactions and Nonlinear Effects; 8.1. Introduction; 8.2. Categorical * Continuous Interactions; 8.3. Categorical * Categorical Interactions; 8.4. Continuous * Continuous Interactions; 8.5. Nonlinear Effects |
| Notes |
8.6. Higher-Order Interactions |
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Print version record |
| Form |
Electronic book
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| ISBN |
9781351677431 |
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1351677438 |
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