install.packages(c("ggplot2", "plyr", "maps", "lubridate", "stringr", "reshape2", "profr"))
Introductions and course outline.
Create informative scatterplots: add extra variables with aesthetics (like color, shape and size) or facetting. Create graphics for large data: histograms and bar charts for displaying distributional summaries; boxplots; scatterplots variations that overcome the over-plotting problems associated with large data.
Basic tools for critiquing a graphic. Advanced layered techniques. Overlay graphic elements using ggplot layers: combining raw data with statistical summaries and contextual information.
Learn about data tidying, the art of getting your data in the right form for visualisation, manipulation and modelling. You’ll learn to use the
dcast functions from the
reshape2 package to deal with a wide range of untidy datasets.
Advanced data aggregation. Build on your knowledge of
plyr to fit large ensembles of simple models, then extract coefficients, predictions, residuals, and other summary statistics. Many examples of advanced layering. Key functions:
First class functions. Learn how to take advantage of R’s functional programming capabilities to write code that is both simpler and more general.
Development best practices. How to write code that is correct, maintainable and fast. A survey of development best-practices including a discussion of code style, commenting, profiling, improving performance and testing. We’ll touch on the new byte-code compiler in R, and on writing high-performance code in C++ with the Rcpp package.