Learning Statistics with R
Part 1: Background
- Chapter 1: Why do we learn statistics? Psychology and statistics. Statistics in everyday life. Some examples where intuition is misleading, and statistics is critical.
- Chapter 2: A brief introduction to research design Basics of psychological measurement. Reliability and validity of a measurement. Experimental and non-experimental design. Predictors versus outcomes.
Part 2: An Introduction to R
- Chapter 3: Getting started with R. Getting R and Rstudio. Typing commands at the console. Simple calculations. Using functions. Introduction to variables. Numeric, character and logical data. Storing multiple values asa vector.
- Chapter 4: Additional R concepts. Installing and loading packages. The workspace. Navigating the file system. More complicated data structures: factors, data frames, lists and formulas. A brief discussion of generic functions.
Part 3: Working with Data
- Chapter 5: Descriptive statistics. Mean, median and mode. Range, interquartile range and standard deviations. Skew and kurtosis. Standard scores. Correlations. Tools for computing these things in R. Brief comments missing data.
- Chapter 6: Drawing graphs. Discussion of R graphics. Histograms. Stem and leaf plots. Boxplots. Scatterplots. Bar graphs.
- Chapter 7: Pragmatic matters. Tabulating data. Transforming a variable. Subsetting vectors and data frames. Sorting, transposing and merging data. Reshaping a data frame. Basics of text processing. Reading unusual data files. Basics of variable coercion. Even more data structures. Other miscellaneous topics, including floating point arithmetic.
- Chapter 8: Basic programming. Scripts. Loops. Conditionals. Writing functions. Implicit loops.
Part 4: Statistical Theory
- Prelude. The riddle of induction, and why statisticians make assumptions.
- Chapter 9: Introduction to probability. Probability versus statistics. Basics of probability theory. Common distributions: normal, binomial, t, chi-square, F. Bayesian versus frequentist probability.
- Chapter 10: Estimating unknown quantities from a sample. Sampling from populations. Estimating population means and standard deviations. Sampling distributions. The central limit theorem. Confidence intervals.
- Chapter 11: Hypothesis testing. Research hypotheses versus statistical hypotheses. Null versus alternative hypotheses. Type I and Type II errors. Sampling distributions for test statistics. Hypothesis testing as decision making. p-values. Reporting the results of a test. Effect size and power. Controversies and traps in hypothesis testing.
Part 5: Statistical Tools
- Chapter 12: Categorical data analysis. Chi-square goodness of fit test. Chi-square test of independence. Yate's continuity correction. Effect size with Cramer's V. Assumptions of the tests. Other tests: Fisher exact test and McNemar's test.
- Chapter 13: Comparing two means. One sample z-test. One sample t-test. Student's independent sample t-test. Welch's independent samples t-test. Paired sample t-test. Effect size with Cohen's d. Checking the normality assumption. Wilcoxon tests for non-normal data.
- Chapter 14: Comparing several means (one-way ANOVA). Introduction to one-way ANOVA. Doing it in R. Effect size with eta-squared. Simple corrections for multiple comparisons (post hoc tests). Assumptions of one-way ANOVA. Checking homogeneity of variance using Levene tests. Avoiding the homogeneity of variance assumption. Checking and avoiding the normality assumption. Relationship between ANOVA and t-tests.
- Chapter 15: Linear regression. Introduction to regression. Estimation by least squares. Multiple regression models. Measuring the fit of a regression model. Hypothesis tests for regression models. Standardised regression coefficient. Assumptions of regression models. Basic regression diagnostics. Model selection methods for regression.
- Chapter 16: Factorial ANOVA. Factorial ANOVA without interactions. Factorial ANOVA with interactions. Effect sizes, estimated marginal means, confidence intervals for effects. Assumption checking. F-tests as model selection. Interpreting ANOVA as a linear model. Specifying contrasts. Post hoc testing via Tukey's HSD. Factorial ANOVA with unbalanced data (Type I, III and III sums of squares)
The list below contains links to all of the data files referred to in the book. There are a few data sets that were introduced in version 0.4 that weren't in version 0.3.1, and some of the data sets from version 0.3.1 were dropped in version 0.4. You can also download a zip file containing all of the R data files.
In late 2013 Dan gave a one-day workshop out at CSIRO that aimed to provide a brief introduction to R for an audience who knew statistics but not R. The workshop consisted of two distinct parts, an introduction to the basic mechanics of R, followed by a fairly rapid coverage of a lot of core statistical tools in R. (There's also a bonus "Part 3" that covers a few additional topics that he's fond of). Anyway, given that the University owns the IP associated with the workshop, and with the agreement of both CSIRO and the University, he's posted copies of all the slides, the exercises and the solution sets to the exercises.
Dan also had the presence of mind to record screencasts of my practice talk, so there's about 5 hours of him talking about statistics linked to below! Two warnings about the videos. Firstly, they were practice talks, and so my delivery isn't as smooth as he'd have liked Secondly, the quality of the audio leaves a lot to be desired (in hindsight, he should have invested in a USB mike). He hasn't made any attempt to edit either -- just posted them because they might be useful to people, but don't get too excited! There's a lot of flaws in how they're put together. On the other hand, they're free.
Part 1: Introducing R
- Getting Started. About R and Rstudio. Typing commands at the console. Arithmetic operators. Logical operators. Functions. Getting help. [slides] [exercises] [solution set] [mp4: 328Mb, 34min]
- Variables and the R Workspace. Creating variables. Numeric, character and logical data. Vectors. Data frames. Indexing and subsetting. Factors. Lists. Matrices. The R workspace. [slides] [exercises] [solution set] [mp4: 565Mb, 46min]
- Some Important Practical Matters. Installing and loading packages. Loading a workspace file. Saving a workspace file. Importing a CSV file. Scripts. The working directory. [slides] [exercises] [solution set] [mp4: 870Mb, 50min]
Part 2: Introductory Statistics in R
- Descriptive Statistics. Central tendency, spread, higher moments. Frequency tables. Describe and summary. Correlations. Descriptive statistics by group. [slides] [exercises] [solution set] [mp4: 490Mb, 32min]
- Statistical Graphics. Scatter plots. Box plots. Histograms. Bar plots. Bar graphs of means and confidence intervals. Line plots [slides] [exercises] [solution set] [mp4: 327Mb, 27min]
- Simple Inferential Statistics. Confidence intervals. t-tests. Wilcoxon tests. chi-square tests. Fisher exact test. Correlation tests. Effect sizes [slides] [exercises] [solution set] [mp4: 403Mb, 25min]
- Linear Models. Multiple regression. One-way and factorial ANOVA. Effect sizes and post hoc tests. Hierarchical regression. ANCOVA. Repeated measures ANOVA. [slides] [exercises] [solution set] [mp4: 383Mb, 32min]
- A Few Data Manipulation Tricks. Reshaping data from long to wide and back again. Coercion. Cutting variables into categories. Permuting factor levels [slides] [exercises] [solution set] [mp4: 189Mb, 16mins]