Visualizing Data (in R)An opinionated style guide

June Choe

Phonetics Lab2020-10-231 / 51

Why?2 / 51

Why?

Why bother making your plots look nice?

2 / 51

Why?

Why bother making your plots look nice?

Helps people remember your results better

2 / 51

Why?

Why bother making your plots look nice?

Helps people remember your results better
Makes your findings look more credible

2 / 51

Why?

Why bother making your plots look nice?

Helps people remember your results better
Makes your findings look more credible
Increases the chance of your work being shared

2 / 51

Why?

Why bother making your plots look nice?

Helps people remember your results better
Makes your findings look more credible
Increases the chance of your work being shared
Shows respect for your audience

2 / 51

Why?

Why bother making your plots look nice?

Helps people remember your results better
Makes your findings look more credible
Increases the chance of your work being shared
Shows respect for your audience

Goal of this presentation

2 / 51

Why?

Why bother making your plots look nice?

Helps people remember your results better
Makes your findings look more credible
Increases the chance of your work being shared
Shows respect for your audience

Goal of this presentation

Share practical tips for improving readability

2 / 51

Why?

Why bother making your plots look nice?

Helps people remember your results better
Makes your findings look more credible
Increases the chance of your work being shared
Shows respect for your audience

Goal of this presentation

Share practical tips for improving readability
Demonstrate implementations of these ideas (w/ code)

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Outline3 / 51

Outline

Four practical tips for improving data visualizations

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Outline

Four practical tips for improving data visualizations

Make your text readable

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Outline

Four practical tips for improving data visualizations

Make your text readable
Be generous about margins and spacing

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Outline

Four practical tips for improving data visualizations

Make your text readable
Be generous about margins and spacing
Make your legends clear

3 / 51

Outline

Four practical tips for improving data visualizations

Make your text readable
Be generous about margins and spacing
Make your legends clear
Make color easy on your reader's eyes

3 / 51

Outline

Four practical tips for improving data visualizations

Make your text readable
Be generous about margins and spacing
Make your legends clear
Make color easy on your reader's eyes

Showcases

3 / 51

Outline

Four practical tips for improving data visualizations

Make your text readable
Be generous about margins and spacing
Make your legends clear
Make color easy on your reader's eyes

Showcases

Vowel formant plot
Vowel space plot
Bar plot of proportions
Multiple categorical levels
Animations

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Preliminaries

Work with the {ggplot2} ecosystem in R

Widely used in academia
Easy to use and highly customizable
Tons of free resources for learning
Lots of extensions

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Preliminaries

Work with the {ggplot2} ecosystem in R

Widely used in academia
Easy to use and highly customizable
Tons of free resources for learning
Lots of extensions

library(scales)
library(colorspace)
library(ggtext)
library(lemon)
library(gganimate)
# library(patchwork)
# library(gghighlight)
# library(ggforce)
# library(ggrepel)

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How does it work?

Source: https://www.youtube.com/watch?v=h29g21z0a68

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state_election_votes

  # A tibble: 1,097 x 3
     State       demVote  Year
     <chr>         <dbl> <dbl>
   1 Alabama       0.85   1932
   2 Arizona       0.67   1932
   3 Arkansas      0.86   1932
   4 California    0.580  1932
   5 Colorado      0.55   1932
   6 Connecticut   0.47   1932
   7 Delaware      0.48   1932
   8 Florida       0.74   1932
   9 Georgia       0.92   1932
  10 Idaho         0.59   1932
  # ... with 1,087 more rows
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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  )

  # A tibble: 66 x 3
     State        demVote  Year
     <chr>          <dbl> <dbl>
   1 California     0.580  1932
   2 Illinois       0.55   1932
   3 Pennsylvania   0.45   1932
   4 California     0.67   1936
   5 Illinois       0.580  1936
   6 Pennsylvania   0.570  1936
   7 California     0.570  1940
   8 Illinois       0.51   1940
   9 Pennsylvania   0.53   1940
  10 California     0.56   1944
  # ... with 56 more rows
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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot()

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  )

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_point()

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_point() +
  scale_y_continuous(
    labels = percent_format(accuracy = 1)
  )

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_point() +
  scale_y_continuous(
    labels = percent_format(accuracy = 1)
  ) +
  scale_x_continuous(
    breaks = pretty_breaks(n= 5)
  )

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_point() +
  scale_y_continuous(
    labels = percent_format(accuracy = 1)
  ) +
  scale_x_continuous(
    breaks = pretty_breaks(n= 5)
  ) +
  labs(
    y = "Democrat Votes",
    title = "Go Vote!"
  )

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_point() +
  scale_y_continuous(
    labels = percent_format(accuracy = 1)
  ) +
  scale_x_continuous(
    breaks = pretty_breaks(n= 5)
  ) +
  labs(
    y = "Democrat Votes",
    title = "Go Vote!"
  )

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_point() +
  scale_y_continuous(
    labels = percent_format(accuracy = 1)
  ) +
  scale_x_continuous(
    breaks = pretty_breaks(n= 5)
  ) +
  labs(
    y = "Democrat Votes",
    title = "Go Vote!"
  )

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state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_line() +
  scale_y_continuous(
    labels = percent_format(accuracy = 1)
  ) +
  scale_x_continuous(
    breaks = pretty_breaks(n= 5)
  ) +
  labs(
    y = "Democrat Votes",
    title = "Go Vote!"
  )

6 / 51

state_election_votes %>%
  filter(State %in%
    c("Pennsylvania",
    "Illinois",
    "California")
  ) %>%
  ggplot() +
  aes(
    x = Year,
    y = demVote,
    color = State
  ) +
  geom_line(size = 1) +
  scale_y_continuous(
    labels = percent_format(accuracy = 1)
  ) +
  scale_x_continuous(
    breaks = pretty_breaks(n= 5)
  ) +
  labs(
    y = "Democrat Votes",
    title = "Go Vote!"
  )

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Yay a plot!

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Yay a plot... ?

A few problems

Text is small and narrow
Plot elements are squished together
Color doesn't grab attention
Legend is off to the side on its own

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1. Text

Many different ways to style text:

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1. Text

Many different ways to style text:

font size

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1. Text

Many different ways to style text:

font size
Font face (bold, italic, small caps)

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1. Text

Many different ways to style text:

font size
Font face (bold, italic, small caps)
Font family ( Times New Roman, Calibri, Arial, Hi friends )

9 / 51

1. Text

Many different ways to style text:

font size
Font face (bold, italic, small caps)
Font family ( Times New Roman, Calibri, Arial, Hi friends )
Font color, line height, letter spacing, angle, weight, etc.

9 / 51

1. Text

Many different ways to style text:

font size
Font face (bold, italic, small caps)
Font family ( Times New Roman, Calibri, Arial, Hi friends )
Font color, line height, letter spacing, angle, weight, etc.

Unless you make your plots with html/css/js, you'll likely only have access to:

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1. Text

Many different ways to style text:

font size
Font face (bold, italic, small caps)
Font family ( Times New Roman, Calibri, Arial, Hi friends )
Font color, line height, letter spacing, angle, weight, etc.

Unless you make your plots with html/css/js, you'll likely only have access to:

Font size, font family, and limited set of font styles

9 / 51

1. Text

Many different ways to style text:

font size
Font face (bold, italic, small caps)
Font family ( Times New Roman, Calibri, Arial, Hi friends )
Font color, line height, letter spacing, angle, weight, etc.

Unless you make your plots with html/css/js, you'll likely only have access to:

Font size, font family, and limited set of font styles
But you can get pretty far with these!

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state_election_plot

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state_election_plot +
  theme(text = element_text(family = "Roboto"))

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15))

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20))

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab"))

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot")

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year")

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL)

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state")

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!")

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state_election_plot +
  theme(text = element_text(family = "Roboto")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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state_election_plot +
  theme(text = element_text(family = "Adelle")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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state_election_plot +
  theme(text = element_text(family = "Bitter")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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state_election_plot +
  theme(text = element_text(family = "Futura Bk BT")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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state_election_plot +
  theme(text = element_text(family = "Montserrat")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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state_election_plot +
  theme(text = element_text(family = "Montserrat Medium")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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state_election_plot +
  theme(text = element_text(family = "Arial")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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state_election_plot +
  theme(text = element_text(family = "xkcd")) +
  theme(text = element_text(size = 15)) +
  theme(plot.title = element_text(size = 20)) +
  theme(plot.title = element_text(family = "Roboto Slab")) +
  theme(plot.title.position = "plot") +
  labs(x = "Election Year") +
  labs(y = NULL) +
  labs(title = "Percent of democrat votes by state") +
  labs(subtitle = "We're a swing state! Go vote!") +
  theme(plot.subtitle = element_text(face = "italic"))

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1. Text (End!)

state_election_plot +
  theme(
    text = element_text(size = 16, family = "Roboto"),
    plot.title = element_text(size = 20, family = "Roboto Slab"),
    plot.title.position = "plot",
    plot.subtitle = element_text(face = "italic"),
    axis.text = element_text(size = 14)
  ) +
  labs(
    x = "Election Year",
    y = NULL,
    title = "Percent of democrat votes by state",
    subtitle = "We're a swing state! Go vote!"
  )

Save our progress!

state_election_plot_A

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1. Text (Before-After)

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2. Margins & Spacing

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2. Margins & Spacing

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2. Margins & Spacing

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2. Margins & Spacing

Base {ggplot2} themes don't have great margin defaults for:

15 / 51

2. Margins & Spacing

Base {ggplot2} themes don't have great margin defaults for:

Margins around plot

15 / 51

2. Margins & Spacing

Base {ggplot2} themes don't have great margin defaults for:

Margins around plot
Margins between plot title and panel

15 / 51

2. Margins & Spacing

Base {ggplot2} themes don't have great margin defaults for:

Margins around plot
Margins between plot title and panel
Margins between axis titles and panel

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2. Margins & Spacing

Base {ggplot2} themes don't have great margin defaults for:

Margins around plot
Margins between plot title and panel
Margins between axis titles and panel
Margins between axis texts and axis title

15 / 51

2. Margins & Spacing

Base {ggplot2} themes don't have great margin defaults for:

Margins around plot
Margins between plot title and panel
Margins between axis titles and panel
Margins between axis texts and axis title

Know your margin/spacing elements!

margin(t = 0, r = 0, b = 0, l = 0, unit = c("pt", "mm", "cm", "in"))
hjust, vjust, and lineheight in element_text()
expand in scale_*()/coord_*()

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state_election_plot_A

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  )

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  )

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  )

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm"))
  )

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    axis.text.x = element_text(margin = margin(t = .2, unit = "cm"))
  )

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    axis.text.x = element_text(margin = margin(t = .2, unit = "cm"))
  ) +
  theme(
    axis.text.y = element_text(margin = margin(r = .1, unit = "cm"))
  )

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    axis.text.x = element_text(margin = margin(t = .2, unit = "cm"))
  ) +
  theme(
    axis.text.y = element_text(margin = margin(r = .1, unit = "cm"))
  ) +
  theme(
    axis.title.x = element_text(margin = margin(t = .3, unit = "cm"))
  )

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    axis.text.x = element_text(margin = margin(t = .2, unit = "cm"))
  ) +
  theme(
    axis.text.y = element_text(margin = margin(r = .1, unit = "cm"))
  ) +
  theme(
    axis.title.x = element_text(margin = margin(t = .3, unit = "cm"))
  ) +
  coord_cartesian(expand = FALSE)

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    axis.text.x = element_text(margin = margin(t = .2, unit = "cm"))
  ) +
  theme(
    axis.text.y = element_text(margin = margin(r = .1, unit = "cm"))
  ) +
  theme(
    axis.title.x = element_text(margin = margin(t = .3, unit = "cm"))
  ) +
  scale_x_continuous(expand = expansion(mult = 0, add = 3))

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state_election_plot_A +
  theme(
    plot.background = element_rect(color = 'black')
  ) +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm")
  ) +
  theme(
    plot.title = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm"))
  ) +
  theme(
    axis.text.x = element_text(margin = margin(t = .2, unit = "cm"))
  ) +
  theme(
    axis.text.y = element_text(margin = margin(r = .1, unit = "cm"))
  ) +
  theme(
    axis.title.x = element_text(margin = margin(t = .3, unit = "cm"))
  ) +
  scale_x_continuous(expand = expansion(mult = 0, add = 3), breaks = pretty_breaks(5))

15 / 51

2. Margins & Spacing (End!)

state_election_plot_A +
  theme(
    plot.margin = margin(1, .8, .8, .8, "cm"),
    plot.title = element_text(margin = margin(b = .3, unit = "cm")),
    plot.subtitle = element_text(margin = margin(b = .3, unit = "cm")),
    axis.text.x = element_text(margin = margin(t = .2, unit = "cm")),
    axis.text.y = element_text(margin = margin(r = .1, unit = "cm")),
    axis.title.x = element_text(margin = margin(t = .3, unit = "cm"))
  ) +
  scale_x_continuous(
    expand = expansion(mult = 0, add = 3),
    breaks = pretty_breaks(5)
  )

Save our progress!

state_election_plot_B

16 / 51

2. Margins & Spacing (Before-After)

17 / 51

2. Margins & Spacing (Before-After)

18 / 51

3. Legends19 / 51

3. Legends

Legends are really hard:

19 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place

19 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place
It's better not to have a legend, if you can get away with it

19 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place
It's better not to have a legend, if you can get away with it
But sometimes we don't have the luxury of doing so

19 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place
It's better not to have a legend, if you can get away with it
But sometimes we don't have the luxury of doing so

Alternative: consider labeling the data directly

19 / 51

20 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place
It's better not to have a legend, if you can get away with it
But sometimes we don't have the luxury of doing so

Alternative: consider labeling the data directly

21 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place
It's better not to have a legend, if you can get away with it
But sometimes we don't have the luxury of doing so

Alternative: consider labeling the data directly

But if you must have a legend, make it so that the reader can't help but to look at

21 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place
It's better not to have a legend, if you can get away with it
But sometimes we don't have the luxury of doing so

Alternative: consider labeling the data directly

But if you must have a legend, make it so that the reader can't help but to look at

Positioned at the top-left or top-center of the plot

21 / 51

3. Legends

Legends are really hard:

They contain important info, but difficult to make them not look out of place
It's better not to have a legend, if you can get away with it
But sometimes we don't have the luxury of doing so

Alternative: consider labeling the data directly

But if you must have a legend, make it so that the reader can't help but to look at

Positioned at the top-left or top-center of the plot
Blend smoothly into the rest of the plot (NOT make them stand out!)

21 / 51

state_election_plot_B

21 / 51

state_election_plot_B +
  theme(
    legend.key = element_rect(fill = NA)
  )

21 / 51

state_election_plot_B +
  theme(
    legend.key = element_rect(fill = NA)
  ) +
  theme(
    legend.position = c(.45, .93)
  )

21 / 51

state_election_plot_B +
  theme(
    legend.key = element_rect(fill = NA)
  ) +
  theme(
    legend.position = c(.45, .93)
  ) +
  theme(
    legend.direction = "horizontal"
  )

21 / 51

state_election_plot_B +
  theme(
    legend.key = element_rect(fill = NA)
  ) +
  theme(
    legend.position = c(.45, .93)
  ) +
  theme(
    legend.direction = "horizontal"
  ) +
  theme(
    legend.background = element_rect(fill = "grey92")
  )

21 / 51

state_election_plot_B +
  theme(
    legend.key = element_rect(fill = NA)
  ) +
  theme(
    legend.position = c(.45, .93)
  ) +
  theme(
    legend.direction = "horizontal"
  ) +
  theme(
    legend.background = element_rect(fill = "grey92")
  ) +
  scale_y_continuous(
    expand = expansion(0, .05),
    labels = percent_format(accuracy = 1)
  )

21 / 51

state_election_plot_B +
  theme(
    legend.key = element_rect(fill = NA)
  ) +
  theme(
    legend.position = c(.45, .93)
  ) +
  theme(
    legend.direction = "horizontal"
  ) +
  theme(
    legend.background = element_rect(fill = "grey92")
  ) +
  scale_y_continuous(
    expand = expansion(0, .05),
    labels = percent_format(accuracy = 1)
  ) +
  labs(color = NULL)

21 / 51

3. Legends (End!)

state_election_plot_B +
  theme(
    legend.key = element_rect(fill = NA),
    legend.position = c(.45, .93),
    legend.direction = "horizontal",
    legend.background = element_rect(fill = "grey92")
  ) + 
  scale_y_continuous(
    expand = expansion(0, .05),
    labels = percent_format(accuracy = 1)
  ) +
  labs(color = NULL)

Save our progress!

state_election_plot_C

22 / 51

3. Legends (Before-After)

23 / 51

4. Color24 / 51

4. Color

Colors are a double-edged sword

24 / 51

4. Color

Colors are a double-edged sword

Perception can vary widely depending on reader, medium, culture, etc.

24 / 51

4. Color

Colors are a double-edged sword

Perception can vary widely depending on reader, medium, culture, etc.
There are multiple, complex representations (RGB, wavelength, hex, HSV)

24 / 51

25 / 51

4. Color

Colors are a double-edged sword

Perception can vary widely depending on reader, medium, culture, etc.

There are multiple, complex representations (RGB, wavelength, hex, HSV)

26 / 51

4. Color

Colors are a double-edged sword

Perception can vary widely depending on reader, medium, culture, etc.

There are multiple, complex representations (RGB, wavelength, hex, HSV)

If you must, DO:

26 / 51

4. Color

Colors are a double-edged sword

Perception can vary widely depending on reader, medium, culture, etc.

There are multiple, complex representations (RGB, wavelength, hex, HSV)

If you must, DO:

Avoid pure colors (no random sampling from the rainbow!)

26 / 51

4. Color

Colors are a double-edged sword

Perception can vary widely depending on reader, medium, culture, etc.

There are multiple, complex representations (RGB, wavelength, hex, HSV)

If you must, DO:

Avoid pure colors (no random sampling from the rainbow!)
Contrast colors in more than one dimension

26 / 51

source: https://blog.datawrapper.de/beautifulcolors/

27 / 51

source: https://blog.datawrapper.de/beautifulcolors/

28 / 51

4. Color

Colors are a double-edged sword

Perception can vary widely depending on reader, medium, culture, etc.

There are multiple, complex representations (RGB, wavelength, hex, HSV)

If you must, DO:

Avoid pure colors (no random sampling from the rainbow!)

Contrast colors in more than one dimension

If you aren't sure, use pre-made palettes or play around with online color tools

29 / 51

30 / 51

state_election_plot_C

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA))

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA))

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line())

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  scale_color_manual(
    values = c("#F8766D", "#00BA38", "#619CFF")
  )

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  scale_color_manual(
    values = c("#F8766D", "#00BA38", "#619CFF")
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  4,   0.56,    0.97)),
      hex(HSV(138,      1,    0.73)),
      hex(HSV(218,   0.62,       1))
    )
  )

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  scale_color_manual(
    values = c("#F8766D", "#00BA38", "#619CFF")
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  4,   0.56,    0.97)),
      hex(HSV(138,      1,    0.73)),
      hex(HSV(218,   0.62,       1))
    )
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  0,      1,       1)),
      hex(HSV(120,      1,       1)),
      hex(HSV(210,      1,       1))
    )
  )

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  scale_color_manual(
    values = c("#F8766D", "#00BA38", "#619CFF")
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  4,   0.56,    0.97)),
      hex(HSV(138,      1,    0.73)),
      hex(HSV(218,   0.62,       1))
    )
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  0,      1,       1)),
      hex(HSV(120,      1,       1)),
      hex(HSV(210,      1,       1))
    )
  ) +
  scale_color_manual(
    values = c(
      hex(HSV( 42,   0.56,    0.84)),
      hex(HSV(133,   0.17,    0.57)),
      hex(HSV(205,   0.75,    0.51))
    )
  )

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  scale_color_manual(
    values = c("#F8766D", "#00BA38", "#619CFF")
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  4,   0.56,    0.97)),
      hex(HSV(138,      1,    0.73)),
      hex(HSV(218,   0.62,       1))
    )
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  0,      1,       1)),
      hex(HSV(120,      1,       1)),
      hex(HSV(210,      1,       1))
    )
  ) +
  scale_color_manual(
    values = c(
      hex(HSV( 42,   0.56,    0.84)),
      hex(HSV(133,   0.17,    0.57)),
      hex(HSV(205,   0.75,    0.51))
    )
  ) +
  scale_color_manual(
    values = c("#D6B25E", "#79917E", "#215982")
  )

30 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  scale_color_manual(
    values = c("#F8766D", "#00BA38", "#619CFF")
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  4,   0.56,    0.97)),
      hex(HSV(138,      1,    0.73)),
      hex(HSV(218,   0.62,       1))
    )
  ) +
  scale_color_manual(
    values = c(
      hex(HSV(  0,      1,       1)),
      hex(HSV(120,      1,       1)),
      hex(HSV(210,      1,       1))
    )
  ) +
  scale_color_manual(
    values = c(
      hex(HSV( 42,   0.56,    0.84)),
      hex(HSV(133,   0.17,    0.57)),
      hex(HSV(205,   0.75,    0.51))
    )
  ) +
  scale_color_manual(
    values = c("#D6B25E", "#79917E", "#215982")
  ) +
  geom_line(size = 1.5)

30 / 51

library(colorBlindness)

31 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  geom_line(size = 1.5) +
  ggthemes::scale_color_colorblind()

31 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  geom_line(size = 1.5) +
  nord::scale_color_nord(palette = "aurora", discrete = TRUE)

31 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  geom_line(size = 1.5) +
  ggsci::scale_color_simpsons()

31 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  geom_line(size = 1.5) +
  ghibli::scale_color_ghibli_d("SpiritedDark", direction = -1)

31 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  geom_line(size = 1.5) +
  scale_color_manual(values = wesanderson::wes_palette("Moonrise2", 3, "discrete"))

31 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  geom_line(size = 1.5) +
  palettetown::scale_color_poke(pokemon = "pikachu")

31 / 51

state_election_plot_C +
  theme(panel.background = element_rect(fill = NA)) +
  theme(legend.background = element_rect(fill = NA)) +
  theme(axis.line = element_line()) +
  geom_line(size = 1.5) +
  scale_color_manual(values = LaCroixColoR::lacroix_palette("PeachPear", 3, "discrete"))

31 / 51

state_election_plot_C

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  )

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  )

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  )

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  ) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  )

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  ) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  guides(
    color = guide_none()
  )

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  ) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  guides(
    color = guide_none()
  ) +
  labs(subtitle = "<span style='color:#2b5e82'>Pennsylvania</span> is a swing state! Go vote!")

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  ) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  guides(
    color = guide_none()
  ) +
  labs(subtitle = "<span style='color:#2b5e82'>Pennsylvania</span> is a swing state! Go vote!") +
  theme(
    plot.subtitle = ggtext::element_markdown()
  )

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  ) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  guides(
    color = guide_none()
  ) +
  labs(subtitle = "<span style='color:#2b5e82'>Pennsylvania</span> is a swing state! Go vote!") +
  theme(
    plot.subtitle = ggtext::element_markdown()
  ) +
  geom_line(size = 1.5)

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  ) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  guides(
    color = guide_none()
  ) +
  labs(subtitle = "<span style='color:#0E4369'>Pennsylvania</span> is a swing state! Go vote!") +
  theme(
    plot.subtitle = ggtext::element_markdown()
  ) +
  geom_line(size = 1.5)

31 / 51

state_election_plot_C +
  theme(
    panel.background = element_rect(fill = NA)
  ) +
  theme(
    legend.background = element_rect(fill = NA)
  ) +
  theme(
    axis.line = element_line()
  ) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  guides(
    color = guide_none()
  ) +
  labs(subtitle = "<strong style='color:#0E4369'>Pennsylvania</strong> is a swing state! Go vote!") +
  theme(
    plot.subtitle = ggtext::element_markdown()
  ) +
  geom_line(size = 1.5)

31 / 51

4. Colors (End!)

state_election_plot_C +
  geom_line(size = 1.5) +
  theme(
    panel.background = element_rect(fill = NA),
    legend.background = element_rect(fill = NA),
    axis.line = element_line(),
    plot.subtitle = element_markdown()
  ) +
  guides(color = guide_none()) +
  scale_color_manual(values = c("#e1e1e1", "#e1e1e1", "#2b5e82")) +
  labs(
    subtitle = "<strong style='color:#0E4369'>Pennsylvania</strong>
                is a swing state! Go vote!"
  )

Save our progress!

state_election_plot_D

32 / 51

4. Colors (Before-After)

33 / 51

4. Colors (Before-After)

34 / 51

35 / 51

state_election_plot

35 / 51

state_election_plot +
  theme_classic(
    base_family = "Roboto",
    base_size = 16
  )

35 / 51

state_election_plot +
  theme_classic(
    base_family = "Roboto",
    base_size = 16
  ) +
  guides(color = guide_none())

35 / 51

state_election_plot +
  theme_classic(
    base_family = "Roboto",
    base_size = 16
  ) +
  guides(color = guide_none()) +
  geom_line(size = 1.5)

35 / 51

state_election_plot +
  theme_classic(
    base_family = "Roboto",
    base_size = 16
  ) +
  guides(color = guide_none()) +
  geom_line(size = 1.5) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  )

35 / 51

state_election_plot +
  theme_classic(
    base_family = "Roboto",
    base_size = 16
  ) +
  guides(color = guide_none()) +
  geom_line(size = 1.5) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  labs(
    y = NULL,
    x = "Election Year",
    title = "Percent of democrat votes by state",
    subtitle = "<strong style='color:#0E4369'>Pennsylvania</strong> is a swing state! Go vote!"
  )

35 / 51

state_election_plot +
  theme_classic(
    base_family = "Roboto",
    base_size = 16
  ) +
  guides(color = guide_none()) +
  geom_line(size = 1.5) +
  scale_color_manual(
    values = c("#e1e1e1", "#e1e1e1", "#2b5e82")
  ) +
  labs(
    y = NULL,
    x = "Election Year",
    title = "Percent of democrat votes by state",
    subtitle = "<strong style='color:#0E4369'>Pennsylvania</strong> is a swing state! Go vote!"
  ) +
  theme(
    plot.margin = margin(.8, 1, .7, .8, "cm"),
    plot.title = element_text(
      family = "Roboto Slab",
      size = 24,
      margin = margin(b = .3, unit = "cm")
    ),
    plot.title.position = "plot",
    plot.subtitle = element_markdown(
      margin = margin(b = .3, unit = "cm")
    ),
    axis.title.x = element_text(
      margin = margin(t = .5, unit = "cm")
    ),
  )

35 / 51

Showcasing36 / 51

First, save our theme!

You can set global theme with theme_set() and theme_update():

theme_set() takes a custom theme as a argument (e.g., theme_bw(), theme_classic(), etc.)

theme_update() takes individual theme elements as arguments

theme_set(
  theme_classic(
    base_family = "Roboto",
    base_size = 16
  )
)
theme_update(
  plot.margin = margin(.8, 1, .7, .8, "cm"),
  plot.title = element_text(
    family = "Roboto Slab",
    size = 24,
    margin = margin(b = .5, unit = "cm")
  ),
  plot.title.position = "plot",
  axis.title.x = element_text(
    margin = margin(t = .5, unit = "cm")
  )
)

37 / 51

1. Vowel Formant Plot38 / 51

sim_vowel_data

  # A tibble: 300 x 3
     Vowel    F1    F2
     <chr> <dbl> <dbl>
   1 i      371. 2431.
   2 i      332. 2435.
   3 i      286. 2630.
   4 i      110. 2527.
   5 i      302. 2315.
   6 i      199. 2279.
   7 i      152. 2303.
   8 i      173. 2116.
   9 i      258. 2540.
  10 i      200. 2604.
  # ... with 290 more rows
38 / 51

sim_vowel_data %>%
  ggplot(aes(x = F2, y = F1))

38 / 51

sim_vowel_data %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel))

38 / 51

sim_vowel_data %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel)) +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5)
  )

38 / 51

sim_vowel_data %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel)) +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5)
  ) +
  scale_y_reverse(position = "right")

38 / 51

sim_vowel_data %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel)) +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5)
  ) +
  scale_y_reverse(position = "right") +
  theme(
    plot.margin = margin(.5,.5, 1, 1, unit = "cm"),
    axis.title.x.top = element_text(margin = margin(b = .2, unit = "cm")),
    axis.title.y.right = element_text(
      angle = 0, vjust = 0.5, margin = margin(l = .3, unit = "cm")
    )
  )

38 / 51

sim_vowel_data %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel)) +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5)
  ) +
  scale_y_reverse(position = "right") +
  theme(
    plot.margin = margin(.5,.5, 1, 1, unit = "cm"),
    axis.title.x.top = element_text(margin = margin(b = .2, unit = "cm")),
    axis.title.y.right = element_text(
      angle = 0, vjust = 0.5, margin = margin(l = .3, unit = "cm")
    )
  ) +
  stat_ellipse(aes(group = Vowel))

38 / 51

2. Vowel Space Plot39 / 51

sim_vowel_data_all

  # A tibble: 800 x 3
     Vowel    F1    F2
     <chr> <dbl> <dbl>
   1 i      371. 2431.
   2 i      332. 2435.
   3 i      286. 2630.
   4 i      110. 2527.
   5 i      302. 2315.
   6 i      199. 2279.
   7 i      152. 2303.
   8 i      173. 2116.
   9 i      258. 2540.
  10 i      200. 2604.
  # ... with 790 more rows
39 / 51

sim_vowel_data_all %>%
  group_by(Vowel)

  # A tibble: 800 x 3
  # Groups:   Vowel [16]
     Vowel    F1    F2
     <chr> <dbl> <dbl>
   1 i      371. 2431.
   2 i      332. 2435.
   3 i      286. 2630.
   4 i      110. 2527.
   5 i      302. 2315.
   6 i      199. 2279.
   7 i      152. 2303.
   8 i      173. 2116.
   9 i      258. 2540.
  10 i      200. 2604.
  # ... with 790 more rows
39 / 51

sim_vowel_data_all %>%
  group_by(Vowel) %>%
  summarize(across(c(F1, F2), mean), .groups = 'drop')

  # A tibble: 16 x 3
     Vowel    F1    F2
     <chr> <dbl> <dbl>
<U+0251>      740.  912.
<U+0252>      699.  762.
<U+0254>      498.  748.
<U+025B>      606. 1905.
<U+0264>      466. 1300.
<U+026F>      305. 1400.
<U+0276>      852. 1532.
<U+028C>      603. 1165.
a      842. 1617.
e      383. 2321.
i      224. 2394.
o      332.  620.
ø      373. 1924.
œ      597. 1699.
u      266.  614.
y      235. 2099.
/ 51

sim_vowel_data_all %>%
  group_by(Vowel) %>%
  summarize(across(c(F1, F2), mean), .groups = 'drop') %>%
  ggplot(aes(x = F2, y = F1))

39 / 51

sim_vowel_data_all %>%
  group_by(Vowel) %>%
  summarize(across(c(F1, F2), mean), .groups = 'drop') %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel), size = 6, family = "Charis SIL")

39 / 51

sim_vowel_data_all %>%
  group_by(Vowel) %>%
  summarize(across(c(F1, F2), mean), .groups = 'drop') %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel), size = 6, family = "Charis SIL") +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5),
    expand = expansion(.1)
  )

39 / 51

sim_vowel_data_all %>%
  group_by(Vowel) %>%
  summarize(across(c(F1, F2), mean), .groups = 'drop') %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel), size = 6, family = "Charis SIL") +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5),
    expand = expansion(.1)
  ) +
  scale_y_reverse(
    position = "right",
    expand = expansion(.1)
  )

39 / 51

sim_vowel_data_all %>%
  group_by(Vowel) %>%
  summarize(across(c(F1, F2), mean), .groups = 'drop') %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel), size = 6, family = "Charis SIL") +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5),
    expand = expansion(.1)
  ) +
  scale_y_reverse(
    position = "right",
    expand = expansion(.1)
  ) +
  theme(
    plot.margin = margin(.5,.5, 1, 1, unit = "cm"),
    axis.title.x.top = element_text(margin = margin(b = .2, unit = "cm")),
    axis.title.y.right = element_text(
      angle = 0, vjust = 0.5, margin = margin(l = .3, unit = "cm")
    )
  )

39 / 51

sim_vowel_data_all %>%
  group_by(Vowel) %>%
  summarize(across(c(F1, F2), mean), .groups = 'drop') %>%
  ggplot(aes(x = F2, y = F1)) +
  geom_text(aes(label = Vowel), size = 6, family = "Charis SIL") +
  scale_x_reverse(
    position = "top",
    breaks = pretty_breaks(5),
    expand = expansion(.1)
  ) +
  scale_y_reverse(
    position = "right",
    expand = expansion(.1)
  ) +
  theme(
    plot.margin = margin(.5,.5, 1, 1, unit = "cm"),
    axis.title.x.top = element_text(margin = margin(b = .2, unit = "cm")),
    axis.title.y.right = element_text(
      angle = 0, vjust = 0.5, margin = margin(l = .3, unit = "cm")
    )
  ) +
  stat_chull(
    fill = NA,
    color = "black",
    linetype = 2
  )

39 / 51

3. Bar Plot of Proportions40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv")

  # A tibble: 4,516 x 10
     Value Item    Cond   Group Subject          Time Answer Accuracy Type   logRT
     <chr> <chr>   <chr>  <chr> <chr>           <dbl> <chr>     <dbl> <chr>  <dbl>
   1 Yes   Awaken~ Subje~ B     5cfa77e907f7dd~   917 No            0 Criti~  6.82
   2 No    Awaken~ Subje~ B     5c1d21196036e4~  2624 No            1 Criti~  7.87
   3 No    Awaken~ Subje~ B     5d730aab6b4b16~  2809 No            1 Criti~  7.94
   4 No    Awaken~ Subje~ B     5ddef0fae454cd~  1587 No            1 Criti~  7.37
   5 No    Awaken~ Subje~ B     5e7a8e8b11bf56~  1697 No            1 Criti~  7.44
   6 No    Awaken~ Verb   A     5d4613a2da9cb6~  1484 No            1 Criti~  7.30
   7 No    Awaken~ Verb   A     579e1e2b275be6~  1556 No            1 Criti~  7.35
   8 No    Awaken~ Verb   A     5dfe69ed11d879~  1699 No            1 Criti~  7.44
   9 No    Awaken~ Verb   A     5b9442cecd3808~  2159 No            1 Criti~  7.68
  10 No    Awaken~ Verb   A     5e7f3328849917~  1132 No            1 Criti~  7.03
  # ... with 4,506 more rows
40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical")

  # A tibble: 1,466 x 10
     Value Item    Cond   Group Subject          Time Answer Accuracy Type   logRT
     <chr> <chr>   <chr>  <chr> <chr>           <dbl> <chr>     <dbl> <chr>  <dbl>
   1 Yes   Awaken~ Subje~ B     5cfa77e907f7dd~   917 No            0 Criti~  6.82
   2 No    Awaken~ Subje~ B     5c1d21196036e4~  2624 No            1 Criti~  7.87
   3 No    Awaken~ Subje~ B     5d730aab6b4b16~  2809 No            1 Criti~  7.94
   4 No    Awaken~ Subje~ B     5ddef0fae454cd~  1587 No            1 Criti~  7.37
   5 No    Awaken~ Subje~ B     5e7a8e8b11bf56~  1697 No            1 Criti~  7.44
   6 No    Awaken~ Verb   A     5d4613a2da9cb6~  1484 No            1 Criti~  7.30
   7 No    Awaken~ Verb   A     579e1e2b275be6~  1556 No            1 Criti~  7.35
   8 No    Awaken~ Verb   A     5dfe69ed11d879~  1699 No            1 Criti~  7.44
   9 No    Awaken~ Verb   A     5b9442cecd3808~  2159 No            1 Criti~  7.68
  10 No    Awaken~ Verb   A     5e7f3328849917~  1132 No            1 Criti~  7.03
  # ... with 1,456 more rows
40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group)

  # A tibble: 1,466 x 10
  # Groups:   Cond, Group [4]
     Value Item    Cond   Group Subject          Time Answer Accuracy Type   logRT
     <chr> <chr>   <chr>  <chr> <chr>           <dbl> <chr>     <dbl> <chr>  <dbl>
   1 Yes   Awaken~ Subje~ B     5cfa77e907f7dd~   917 No            0 Criti~  6.82
   2 No    Awaken~ Subje~ B     5c1d21196036e4~  2624 No            1 Criti~  7.87
   3 No    Awaken~ Subje~ B     5d730aab6b4b16~  2809 No            1 Criti~  7.94
   4 No    Awaken~ Subje~ B     5ddef0fae454cd~  1587 No            1 Criti~  7.37
   5 No    Awaken~ Subje~ B     5e7a8e8b11bf56~  1697 No            1 Criti~  7.44
   6 No    Awaken~ Verb   A     5d4613a2da9cb6~  1484 No            1 Criti~  7.30
   7 No    Awaken~ Verb   A     579e1e2b275be6~  1556 No            1 Criti~  7.35
   8 No    Awaken~ Verb   A     5dfe69ed11d879~  1699 No            1 Criti~  7.44
   9 No    Awaken~ Verb   A     5b9442cecd3808~  2159 No            1 Criti~  7.68
  10 No    Awaken~ Verb   A     5e7f3328849917~  1132 No            1 Criti~  7.03
  # ... with 1,456 more rows
40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop')

  # A tibble: 4 x 3
    Cond    Group Accuracy
    <chr>   <chr>    <dbl>
  1 Subject A        0.875
  2 Subject B        0.780
  3 Verb    A        0.846
  4 Verb    B        0.710
40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group))

40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group)) +
  geom_col(position = "dodge", color = "white", width = .7, size = 2)

40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group)) +
  geom_col(position = "dodge", color = "white", width = .7, size = 2) +
  scale_fill_manual(values = c("grey30", "grey70"))

40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group)) +
  geom_col(position = "dodge", color = "white", width = .7, size = 2) +
  scale_fill_manual(values = c("grey30", "grey70")) +
  labs(
    title = "Accuracy on Comprehension Task",
    x = "Pitch Accent Condition", y = NULL,
    fill = "Experiment Group"
  )

40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group)) +
  geom_col(position = "dodge", color = "white", width = .7, size = 2) +
  scale_fill_manual(values = c("grey30", "grey70")) +
  labs(
    title = "Accuracy on Comprehension Task",
    x = "Pitch Accent Condition", y = NULL,
    fill = "Experiment Group"
  ) +
  guides(fill = guide_legend(direction = "horizontal", title.position = "top"))

40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group)) +
  geom_col(position = "dodge", color = "white", width = .7, size = 2) +
  scale_fill_manual(values = c("grey30", "grey70")) +
  labs(
    title = "Accuracy on Comprehension Task",
    x = "Pitch Accent Condition", y = NULL,
    fill = "Experiment Group"
  ) +
  guides(fill = guide_legend(direction = "horizontal", title.position = "top")) +
  coord_capped_cart(
    ylim = c(0.5, 1),
    left = "top"
  )

40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group)) +
  geom_col(position = "dodge", color = "white", width = .7, size = 2) +
  scale_fill_manual(values = c("grey30", "grey70")) +
  labs(
    title = "Accuracy on Comprehension Task",
    x = "Pitch Accent Condition", y = NULL,
    fill = "Experiment Group"
  ) +
  guides(fill = guide_legend(direction = "horizontal", title.position = "top")) +
  coord_capped_cart(
    ylim = c(0.5, 1),
    left = "top"
  ) +
  theme(
    axis.ticks.x = element_blank(),
    axis.text.x = element_text(color = "black", margin = margin(t = .2, unit = "cm")),
    legend.position = c(.3, .93),
    plot.title = element_text(margin = margin(b = 1, unit = "cm"))
  )

40 / 51

read_csv("https://raw.githubusercontent.com/yjunechoe/Semantic-Persistence/master/processed.csv") %>%
  filter(Type == "Critical") %>%
  group_by(Cond, Group) %>%
  summarize(Accuracy = mean(Accuracy, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = Cond, y = Accuracy, fill = Group)) +
  geom_col(position = "dodge", color = "white", width = .7, size = 2) +
  scale_fill_manual(values = c("grey30", "grey70")) +
  labs(
    title = "Accuracy on Comprehension Task",
    x = "Pitch Accent Condition", y = NULL,
    fill = "Experiment Group"
  ) +
  guides(fill = guide_legend(direction = "horizontal", title.position = "top")) +
  coord_capped_cart(
    ylim = c(0.5, 1),
    left = "top"
  ) +
  theme(
    axis.ticks.x = element_blank(),
    axis.text.x = element_text(color = "black", margin = margin(t = .2, unit = "cm")),
    legend.position = c(.3, .93),
    plot.title = element_text(margin = margin(b = 1, unit = "cm"))
  ) +
  scale_y_continuous(
    expand = expansion(0, 0),
    labels = percent_format(accuracy = 1)
  )

40 / 51

4. Multiple Categorical Levels41 / 51

Source: Husband & Patson (2020)

42 / 51

df <- crossing(level_1 = fct_inorder(c("Within", "Between")),
               level_2 = fct_inorder(c("Some", "Number", "Or")),
               level_3 = factor(c("Strong", "Weak")))
df$barheight <- c(.63, .35, .72, .55, .61, .15, .60, .55, .52, .63, .17, .16)
df %>% 
  ggplot(aes(level_3, barheight)) +
  geom_col(
    aes(fill = level_3),
    show.legend = FALSE
  ) +
  geom_errorbar(
    aes(ymin = barheight - .05, ymax = barheight + .05),
    width = .1) +
  facet_grid(level_2 ~ level_1) +
  theme_bw() +
  scale_fill_manual(values = c('grey40', 'grey80')) +
  ylim(0, 1) +
  labs(
    y = "Proportion of Strong Responses",
    x = "Prime Type") +
  theme_bw()

43 / 51

(Code)

44 / 51

(Code)

45 / 51

5. Animations: Vowel shift46 / 51

read_csv("https://raw.githubusercontent.com/bodowinter/canadian_vowel_shift_analysis/master/processed_data/production_processed.csv") %>% 
  group_by(Gender, Vowel, BirthDecade = 10 * BirthYear %/% 10) %>% 
  summarize(across(F1:F2, mean), .groups = 'drop') %>% 
  filter(Gender == "F") %>% 
  ggplot(aes(F2, F1)) +
  geom_text(aes(1450, 615, label = as.character(BirthDecade)),
            color = "gray80", size = 48) +
  stat_chull(fill = NA, color = "black", linetype = 2) +
  geom_label(aes(label = Vowel), size = 6, family = "Charis SIL") +
  scale_x_reverse(position = "top") +
  scale_y_reverse(position = "right") +
  theme(
    plot.margin = margin(.5,.8, 1.2, 1, unit = "cm"),
    axis.title.x.top = element_text(margin = margin(b = .2, unit = "cm")),
    axis.title.y.right = element_text(angle = 0, vjust = 0.5, margin = margin(l = .3, unit = "cm"))
  ) +
  coord_cartesian(clip = 'off') + 
  labs(title = "Canadian Vowel Shift", caption = "Source: Kettig & Winter (2017)") +
  transition_states(BirthDecade) +
  shadow_mark(
    alpha = .1,
    color = "grey",
    exclude_layer = c(1, 2)
  )

47 / 51

5. Animations: Eye-tracking48 / 51

de Carvalho et al. (2017)

49 / 51

(Code)

50 / 51

Resources

light

The Glamour of Graphics (20-min video)
Gallery of plot tyes
What to consider when choosing colors for data viz
Palettes (in R)

heavy

Font Recommendations (book chapter)
Theme elements in ggplot2 (documentation)
ggplot2 tutorial (4-hour video, 2 parts)
ggplot2 book

51 / 51

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Visualizing Data (in R)

An opinionated style guide

June ChoePhonetics Lab

2020-10-23

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1. Text

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2. Margins & Spacing

2. Margins & Spacing

2. Margins & Spacing

2. Margins & Spacing

2. Margins & Spacing

2. Margins & Spacing

2. Margins & Spacing

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2. Margins & Spacing (End!)

2. Margins & Spacing (Before-After)

2. Margins & Spacing (Before-After)

3. Legends

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4. Colors (End!)

4. Colors (Before-After)

4. Colors (Before-After)

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First, save our theme!

1. Vowel Formant Plot

2. Vowel Space Plot

3. Bar Plot of Proportions

4. Multiple Categorical Levels

5. Animations: Vowel shift

5. Animations: Eye-tracking

June Choe

Phonetics Lab