Effective Graphs

Basic issues with data visualization

Journal requirements

  • dimensions
  • fonts
  • linewidths
  • greyscale vs. color

Reader requirements

  • clarity
  • legibility
  • aesthetics

Extra dimensions!

  • x
  • y
  • character size
  • color
  • (plotting character)
  • (transparency)

Raw Data and Model Fits

  • Font size
  • Line width
  • Color (greyscale / colorblind/ clashy)

Functionalize and clearly label code for all plots

 (may want to revise for talks)

Also check:

par / plot

Why use par / plot?

1. increased flexibility

2. linguistically consistent with the rest of R

I can build anything ggplot can build, with more flexibility and better control

Useful functions

lwd

(x/y)axt

las

border

Useful arguments

  • layout() -- especially for asymmetric gridding
  • rgb() -- especially "alpha" argument for transparency, and to precisely control color ramps
  • axis()
  • mtext() -- to label grid cells in lattice-like configurations
  • polygon() -- to show model fits
  • segments() -- to build marginal boxplots /  Gelman coefficient plots

Ideas and examples

Parallel structures

  • Limit dimensions
  • Limit # panels
  • Align axis of most important comparison
  • Transformations:
    • multiplicative vs. additive scales

Multiple versions

Main text vsn

Supp. info vsn

Not too busy

Give big points big visual impact

Low visual impact

High visual impact

Big data and small

Annotate!

Take-homes

1. R has lots of graphical flexibility, even using just plot()

2. Graphics reinforce model results

3. Graphics clarify relationship between models and data

4. Designing and building effective graphics requires a little time / iteration

BUT it's worth it:

People pay attention to graphs

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