STAT 209 A Taxonomy of Graphics February 16, 2018 Colin Reimer - - PowerPoint PPT Presentation

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STAT 209 A Taxonomy of Graphics February 16, 2018 Colin Reimer - - PowerPoint PPT Presentation

Jan 06, 2024 24 likes •536 views

Review ggplot2 Big Picture Types of Graphs ( geom s) Color Schemes ggplot2 themes STAT 209 A Taxonomy of Graphics February 16, 2018 Colin Reimer Dawson 1 / 39 Review ggplot2 Big Picture Types of Graphs ( geom s) Color Schemes

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STAT 209 A Taxonomy of Graphics

February 16, 2018 Colin Reimer Dawson 1 / 39

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Course Business

  • HW3 or GitHub issues?
  • Project 1 Description
  • New, exciting reference resources!
  • Everybody met with/signed up to meet w/ me?
  • Office hours schedule

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Today

  • Plot (geom) types
  • Color palettes
  • ggplot2 themes

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Next Week

  • Monday: Lab on colors and themes
  • Wednesday: Lab to “reverse engineer” a published

visualization

  • Friday: Project 1 workshop

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Outline

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Quick Summary: ggplot2

Figure: Slide by Jordan Crouser at Smith College

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Quick Summary: ggplot2

Figure: Slide by Jordan Crouser at Smith College

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Quick Summary: ggplot2

Figure: Slide by Jordan Crouser at Smith College

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Quick Summary: ggplot2

Figure: Slide by Jordan Crouser at Smith College

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Quick Summary: ggplot2

Figure: Slide by Jordan Crouser at Smith College

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Quick Summary: ggplot2

Figure: Slide by Jordan Crouser at Smith College

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Outline

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Common Graph Types

  • Bar Graph
  • Histogram
  • Box Plot
  • Scatterplot
  • Line Chart
  • Map

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Bar Graph

  • Allows comparison of a statistic (often just “amount of

data”) across categories

  • Can use grouping or stacking to bring in a second

categorical variable and depict combinations 9 / 39

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Histogram

  • Show count or proportion within bins of a quantitative

variable

  • Choice of bin width/cutoffs can affect impression

dramatically 10 / 39

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Box Plot

  • Alternative to histogram to show distribution of a

quantitative variable

  • Can group by a categorical variable to compare

distributions 11 / 39

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Scatterplot

  • Depicts the relationship between two quantitative

variables

  • Can bring in additional variables using hue, saturation,

symbol

  • Can show trend line based on a model (e.g., regression)

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Line Chart

  • Useful to depict trends in sequential data
  • Essentially a scatterplot with dots connected, but this

must make sense

  • Can depict groups, or overlay multiple variables to see

when variables move together/apart/switch ordering 13 / 39

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Map

  • Useful for geographic data (duh)
  • A filled map (like this) is called a chloropleth
  • May alternatively plot data point in specific locations

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Pitfalls and Distortions of Maps

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Pitfalls and Distortions of Maps

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Pitfalls and Distortions of Maps

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Pitfalls and Distortions of Maps

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Pitfalls and Distortions of Maps

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My Personal Fave

Figure: http://xkcd.com/1939/

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Outline

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Choose Colors Intentionally

  • Color is not just about aesthetic preference! Colors

communicate information

  • Universal design: Keep in mind not everyone sees color

the same way 18 / 39

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Color Coordinate Systems

  • RGB (Red/Green/Blue)
  • denotes intensity of each primary color in the color
  • useful for color generation, not particularly intuitive for

color perception

  • HSL (Hue/Saturation/Lightness)
  • More naturally describe perceptual properties
  • Hue: the dominant “color word” in the rainbow
  • Saturation: how “pure” vs. “muted” is the color
  • Lightness: how much white or black is in the color

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Taxonomy of Color Palettes

  • Sequential: Colors fall on an ordered scale
  • Single hue: Uses only saturation/lightness to distinguish
  • Multi hue: Varies colors on a hue spectrum
  • Diverging: Colors move two directions from a neutral

point

  • Categorical: No sense of a quantitative scale; mapping for

categorical variables only 20 / 39

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Sequential Palette: Single Hue

  • Maps a numeric variable

to the saturation dimension (sometimes combination of saturation and lightness)

  • Higher saturation =

More of the thing 21 / 39

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Sequential Palette: Multi Hue

  • Maps a numeric variable

to an arc in HSB color space, varying hue

  • Typically, darker = more

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Diverging Palette

  • Two sequential (single

hue) scales that “meet in the middle” at a neutral (low saturation) color

  • Useful for signed

quantitative variable with anchor at zero

  • Careful to distinguish

missing data! 23 / 39

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Categorical Palette

  • A set of (roughly)

equally spaced points in a circle in HSB space (i.e., only hue varies)

  • Colors indicate levels of

a categorical variable 24 / 39

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Some convenient built-in options

  • RColorBrewer
  • by Cynthia Brewer (http://www.colorbrewer2.org)
  • has sequential, diverging, categorical palettes
  • viridis
  • Rudis, Ross and Garnier (link to CRAN documentation)
  • Based on color schemes in Python’s matplotlib
  • Sequential (multi-hue) only

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ggplot2 default

library(tidyverse) rand_data <- data.frame(x = rnorm(10000), y = rnorm(10000)) norm_plot <- ggplot(rand_data, aes(x = x, y = y)) + geom_hex() + coord_fixed() norm_plot

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x y

25 50 75 100

count

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ColorBrewer Purples palette

library(RColorBrewer) norm_plot + scale_fill_distiller(palette = "Purples")

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x y

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count

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ColorBrewer YlGn palette

library(RColorBrewer) norm_plot + scale_fill_distiller(palette = "YlGn")

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x y

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count

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viridis palette

library(viridis) norm_plot + scale_fill_viridis()

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x y

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count

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Universal Design: Color Blindness

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Universal Design: Color Blindness

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Universal Design: Color Blindness

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Universal Design: Color Blindness

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Outline

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ggplot2 themes

library(tidyverse) p1 <- ggplot(mtcars, aes(x = disp, y = mpg, color = factor(cyl))) + geom_point() p1

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disp mpg factor(cyl)

  • 4

6 8

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Wall Street Journal Theme

library(ggthemes) p1 + theme_wsj()

  • 10

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factor(cyl)

  • 4

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Economist Theme

library(ggthemes) p1 + theme_economist()

  • 10

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  • 4

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FiveThirtyEight Theme

library(ggthemes) p1 + theme_fivethirtyeight()

  • 10

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factor(cyl)

  • 4

6 8

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Solarized Theme

library(ggthemes) p1 + theme_solarized()

  • 10

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disp mpg factor(cyl)

  • 4

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Edward Tufte’s Minimalist Theme

library(ggthemes) p1 + theme_tufte()

  • 10

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disp mpg factor(cyl)

  • 4

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Why Does This Exist?

library(ggthemes) p1 + theme_excel()

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disp mpg factor(cyl)

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Many More...

  • ggthemes package documentation link

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