Table of contents
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 base 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
1.13. Installing, loading, and citing packages
1.14. Seeking help
1.15. A note on keyboard shortcuts
1.16. Your R journey: The road ahead
2. Tidy functions 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. Models and distributions
3.1. Models
3.2. Distributions
3.3. The normal distribution
3.4. Thinking of the mean as a model
3.5. 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
4.6. A simple linear model in R
4.7. Linear models with tidyverse functions
4.8. Model formula notation: Intercept placeholders
4.9. Chapter conclusions
5. 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
6.6. Chapter conclusions
7. Categorical predictors
7.1. Introduction
7.2. 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 le
