31 Data visualisation

31.1 Visual variables

A visual variable is an aspect of a mark that can be controlled to change its appearance.

Visual variables include:

• Size
• Shape
• Orientation
• Colour (hue)
• Colour value (brightness)
• Texture
• Position (2 dimensions)

31.2 Grammar of graphics

Grammars provide rules for languages

“The grammar of graphics takes us beyond a limited set of charts (words) to an almost unlimited world of graphical forms (statements)” (Wilkinson, 2005)

Statistical graphic specifications are expressed in six statements:

1. Data manipulation
2. Variable transformations (e.g., rank),
3. Scale transformations (e.g., log),
4. Coordinate system transformations (e.g., polar),
5. Element: mark (e.g., points) and visual variables (e.g., color)
6. Guides (axes, legends, etc.).

31.3 ggplot2

The ggplot2 library offers a series of functions for creating graphics declaratively, based on the Grammar of Graphics.

To create a graph in ggplot2:

• provide the data
• specify elements
  • which visual variables (aes)
  • which marks (e.g., geom_point)
• apply transformations
• guides

library(tidyverse)
library(nycflights13)
library(knitr)

31.4 Histograms

• x variable to plot
• geom_histogram

nycflights13::flights %>%
  filter(month == 11) %>%
  ggplot(
    aes(
      x = dep_delay
    )
  ) +
  geom_histogram(
    binwidth = 10
  )

31.5 Histograms

31.6 Boxplots

• x categorical variable
• y variable to plot
• geom_boxplot

nycflights13::flights %>%
  filter(month == 11) %>%
  ggplot(
    aes(
      x = carrier, 
      y = arr_delay
    )
  ) +
  geom_boxplot()

31.7 Boxplots

31.8 Jittered points

• x categorical variable
• y variable to plot
• geom_jitter

nycflights13::flights %>%
  filter(month == 11) %>%
  ggplot(
    aes(
      x = carrier, 
      y = arr_delay
    )
  ) +
  geom_jitter()

31.9 Jittered points

31.10 Violin plot

• x categorical variable
• y variable to plot
• geom_violin

nycflights13::flights %>%
  filter(month == 11) %>%
  ggplot(
    aes(
      x = carrier, 
      y = arr_delay
    )
  ) +
  geom_violin()

31.11 Violin plot

31.12 Lines

• x e.g., a temporal variable
• y variable to plot
• geom_line

nycflights13::flights %>%
  filter(!is.na(dep_delay)) %>%
  mutate(flight_date = ISOdate(year, month, day)) %>%
  group_by(flight_date) %>%
  summarize(avg_dep_delay = mean(dep_delay)) %>%
  ggplot(aes(
    x = flight_date,
    y = avg_dep_delay
  )) +
  geom_line()

31.13 Lines

31.14 Scatterplots

• x and y variable to plot
• geom_point

nycflights13::flights %>%
  filter(
    month == 11, 
    carrier == "US",
    !is.na(dep_delay),
    !is.na(arr_delay)
  ) %>%
  ggplot(aes(
    x = dep_delay,
    y = arr_delay
  )) +
  geom_point()

31.15 Scatterplots

31.16 Overlapping points

• x and y variable to plot
• geom_count counts overlapping points and maps the count to size

nycflights13::flights %>%
  filter(
    month == 11, carrier == "US",
    !is.na(dep_delay), !is.na(arr_delay)
  ) %>%
  ggplot(aes(
    x = dep_delay,
    y = arr_delay
  )) +
  geom_count()

31.17 Overlapping points

31.18 Bin counts

• x and y variable to plot
• geom_bin2d

nycflights13::flights %>%
  filter(
    month == 11, 
    carrier == "US",
    !is.na(dep_delay),
    !is.na(arr_delay)
  ) %>%
  ggplot(aes(
    x = dep_delay,
    y = arr_delay
  )) +
  geom_bin2d()

31.19 Bin counts