• Exploration

• Inference

• Explanation

Goal: find patterns (& problems) in data, explore hypotheses

• nonparametric/robust approaches: impose as few assumptions as possible
  • histograms for distributions
  • boxplots (median/IQR) instead of mean/std dev for grouped data
  • loess/GAM instead of linear/polynomial regression for continous data
• need for speed: quick and dirty
• canned routines for standard tasks, flexibility for non-standard tasks
• manipulate to visualize: summarize on the fly

HW on Twitter

• Avoiding snooping:
  • Separate response variables from predictors
  • Look at residuals but not fits

• determine fitting problems, evaluate model assumptions
  • badness of fit, heteroscedasticity, non-Normality, outliers
  • e.g. scale-location plot, Q-Q plot; plot.lm
• plot methods: generic (e.g. residuals vs fitted) vs specific (e.g. residuals vs predictors)
• predictions (intuitive) vs residuals (amplifies/zooms in on discrepancies)
  • looking for absence of patterns in residuals
• performance::check_model(), DHARMa packages

• A picture is worth 1000 words
• coefficient plots: replacement for tables (Gelman, Pasarica, and Dodhia 2002); dotwhisker::dwplot
• also: tests of inference (Wickham et al. 2010)

• should analyses match graphs?
  • “Let the data speak for themselves” vs “Tell a story”
• display data (e.g. boxplots, standard deviations) or inferences from data (confidence intervals)
• superimposing model fits (geom_smooth)

• tell an accurate story
• high information density
• Tufte, Cleveland

• Cleveland hierarchy: position, length, angle, area, volume, colour: also see John Rauser (2016)

• Edward Tufte: chartjunk, data- and non-data-ink, small multiples, direct labeling, …
• graphics fascists: dynamite plots, pie charts, dual-axes plots …

• Extending the pipeline: R vs GLE vs. D3.js vs. plot.ly vs. …
• Telling a story vs. letting the data speak
• Showing the data vs. representing the statistical model accurately
• Dynamic graphics

• advantages: speed, simplicity, breadth
• disadvantages: lack of structure, canvas (draw-and-forget) metaphor
• basic commands:
  • plot(), lines() and points()
  • legend(), axis() (for decoration)
  • other kinds of graphs: matplot() for multi-series plots, boxplot() for box plots, hist() for histograms, contour() and image(), pairs(), …
  • par(mfrow), layout() for multiple plots on a page
  • useful packages: car (for scatterplot(), scatterplotMatrix()), plotrix (miscellaneous “plot tricks”)

• data
• mappings: between variables in the data frame and aesthetics, or graphical attributes (x position, y position, size, colour …)
• first two show up as (e.g.)
  ggplot(my_data,aes(x=age,y=rootgrowth,colour=phosphate))
• geoms:
• simple: geom_point, geom_line

(ggplot(my_data,aes(x=age,y=rootgrowth,colour=phosphate))+geom_point())

• more complex: geom_boxplot, geom_smooth

• geoms are added to an existing data/mapping combination
• facets: facet_wrap (free-form wrapping of subplots), facet_grid (two-D grid of subplots)
• also: scales, coordinate transformations, statistical summaries, position adjustments
• advantages: pretty defaults (mostly), flexible, easy to overlay model predictions etc.
• disadvantages: slow, magical, tricky to customize

• primary response as y axis
• most important predictor as x axis
  • (or substitute most important continuous or many-category predictor)
• next (categorical) predictors as groupings (preferably colour/shape)
• next (categorical) predictors as facets

• Most salient comparisons within the same subplot (distinguished by color/shape), and nearby within the subplot when grouping bars/points
• Facet rows > facet columns
• flip axes to display labels better (ggplot: coord_flip(), ggstance() package)
• Use transparency to include important but potentially distracting detail
• Do category levels need to be identified or just distinguished?

• Order categorical variables meaningfully (“What’s wrong with Alabama?”): forcats::fct_reorder(), forcats::fct_infreq()
• Choose population variation (standard deviations, boxplots) vs. estimate variation (standard errors, mean \(\pm\) 2 SE, boxplot notches)
• Choose colors carefully (RColorBrewer/ColorBrewer, IWantHue), colorspace, viridis packages: respect dichromats and B&W printouts
• visual design (tweaking) vs. reproducibility (e.g. ggrepel, directlabels packages)

• multiple continuous predictors
• multivariate responses
• high-dimensional data generally
• factors with lots of (unordered) levels
• spatial data
• phylogenetic trees + tip data
• Large data sets

• small: show all points, possibly dodged/jittered, with some summary statistics: dotplot, beeswarm. Simple trends (linear/GLM)
• medium: boxplots, loess, histograms, GAM (or linear regression)
• large: modern nonparametrics: violin plots, hexbin plots, kernel densities computational burden, and display overlapping problems, relevant
• combinations or overlays where appropriate (beanplot)

• ggplot2 extensions
• Cookbook for R: Graphs, R Graph Gallery, R Graphics Cookbook ($$)
• Exporting graphics: bitmap (PNG, JPEG) vs vector (SVG, PDF) representations
• Andrew Gelman’s blog: Infovis vs. statistical graphics
• Paul Krugman on axes starting from zero
• Beyond 2D: googleVis, Rggobi, rgl, Mondrian, …