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Data visualisation in R with ggplot2 and plyr

Rice University. 23 September 2011

Make sure you have R and Rstudio. Then install the packages you’ll need with this R code:

install.packages(c("ggplot2", "plyr", "stringr"))

Course outline

    Introductions and course outline.

    ggplot2 basics

    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.

    Data manipulation

    Manipulate and transform data: add extra information to your plots via group-wise summaries and transformations; visualize time series. Introduction to the plyr package.

    Graphics: critique and creation

    Basic tools for critiquing a graphic. Advanced layered techniques. Overlay graphic elements using ggplot layers: combining raw data with statistical summaries and contextual information.

    Polishing graphics for presentation

    Polish your plots: tweak your plots for maximum presentation impact; introduction to color theory; labels, legends and axes; tweaking the plot themes.