QCBS R workshop - Visualization

QCBS R workshop - Visualization

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eric-pedersen

March 22, 2013
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Transcript

  1. Plotting in Open Source Statistical Programming map courtesy of James

    Cheshire (spatialanalysis.co.uk/2012/02/great-maps-ggplot2/)
  2. Tables Data Graphs Statistics Understanding Sigmaplot Excel SAS Why do

    your plotting in R? Simpler workflow:
  3. Tables Data Statistics Understanding Graphs Why do your plotting in

    R? Simpler workflow:
  4. Why do your plotting in R? Flexible and beautiful graphics:

  5. Why do your plotting in R? Flexible and beautiful graphics:

  6. Why do your plotting in R? Flexible and beautiful graphics:

  7. Enough slides! Now for a live demo

  8. * Challenge 1: Building a plot layer by layer 1.

    Use the 'iris' dataset to build the following three plots: a. A plot of Sepal.Length vs. Sepal.Width b. The same plot, but coloured by 'Species' c. A plot of Petal.Length vs. Species
  9. * Challenge 2: Playing with aesthetics and geoms 1. Use

    the qplot function and the 'iris' dataset to re- build the following three plots: a. A plot of Sepal.Length vs. Sepal.Width b. The same plot, but coloured by 'Species' c. A plot of Petal.Length vs. Species 2. For the plot in part 'c', try changing the geom argument to "boxplot". Try building a graph with both points and boxplot on them
  10. * Challenge 3: Faceting and scaling 1. Try to build

    the following plots using the iris data: a. Petal.Width vs. Petal.Height, faceted in a row by Species; and the same plot by column b. Sepal.Width vs. Sepal.Height, with the x-axis log-transformed, the y-axis log-transformed, and both axes log-transformed
  11. ggplot2: using the Grammar of Graphics ggplot2: a different way

    of plotting: building plots by describing what you want, and let it figure out how to plot it.
  12. ggplot2: using the Grammar of Graphics ggplot2: Builds plots out

    of simpler components: • data • aesthetics: e.g. coordinates, colour, size of points • geoms: the type of plot layer: line, point, etc. • facets: organizing graphs into subplots • transforms: logs, square roots, continuous to discrete etc. • stats: functions of the raw data • coordinates: e.g. Euclidean, map coords, polar • themes: font size, grids, etc.
  13. Further reading Plotting in general: R Graphics The R Book

    ggplot2 references: The creator, Hadley Wickham's website The in-depth documentation website The book: ggplot2: Elegant Graphics for Data Analysis