The list below contains links to all of the data files referred to in the book. There are a few data sets that are new to version 0.4 that weren't in version 0.3.1, and some of the data sets from version 0.3.1 have been dropped in version 0.4. You can also download a zip file containing all of the R data files.
The book is associated with the lsr package available on CRAN. The source code for the package is available on bitbucket.
In late 2013 I 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 I'm fond of). Anyway, given that the University owns the IP associated with the workshop, and with the agreement of both CSIRO and the University, I've posted copies of all the slides, the exercises and the solution sets to the exercises.
I also had the presence of mind to record screencasts of my practice talk, so there's about 5 hours of me 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 I'd have liked Secondly, I've only just started learning about recording video, so the quality of the audio leaves a lot to be desired (in hindsight, I should have invested in a USB mike). I haven't made any attempt to edit either. I've 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]
Part 3: Extras
- Additional Statistical Tools. General linear model. Factor analysis. Scale reliability. Bayesian methods. [slides] [mp4: 190Mb, 15min]
- A Few More R Tricks. Writing functions. If-else. For loops. R Markdown. Text processing. [slides] [mp4: 171Mb, 15min]