Presentation ready plots 2:
Telling a story

Lecture 19

Dr. Mine Çetinkaya-Rundel

Duke University
STA 113 - Fall 2023

Warm-up

Announcements

• Project 2 presentations in class on Thursday – food preference?

• Any questions?

Exam 2 redo (optional)

• Due: Monday 12/11 at 5 pm
• Take home:
  • Message me on Slack to let me know if you want to work on this.
  • Work in exam-2-redo.qmd, this is a copy of your exam submission, without any changes I might have implemented to get it to render – do not overwrite exam-2.qmd.
• In class:
  • Write/type up corrections + reasoning for corrections (even for questions that didn’t originally ask for reasoning) on a separate piece of paper.
  • Return original exam + redo together at my office (slide under door if I’m not there).
• Improve your answers working on your own. The same rules as the exam applies.
• You will be eligible to receive up to 50% of the points you missed on each portion of the exam.

Code review

• Clone your assigned team’s project and (try to) render it
• First, review the organization of the project/repo
• Then, review the code and open issues associated with any lines of code you want to make specific comments about [DEMO]
• Finally, fill out the “Code review” issue
• Be critical, but constructive

25:00

Setup

# load packages
library(tidyverse)
library(palmerpenguins)
library(fs)
library(openintro)
library(glue)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

Animation (as requested)

Philosophy

• The purpose of interactivity is to display more than can be achieved with persistent plot elements, and to invite the reader to engage with the plot.

• Animation allows more information to be displayed, but developer keeps control

• Beware that it is easy to forget what was just displayed, so keeping some elements persistent, maybe faint, can be useful for the reader

gganimate

• gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation

• It provides a range of new grammar classes that can be added to the plot object in order to customize how it should change with time

How does gganimate work?

• Start with a ggplot2 specification

• Add layers with graphical primitives (geoms)

• Add formatting specification

• Add animation specification

Grammar of animation

• Transitions: transition_*() defines how the data should be spread out and how it relates to itself across time

• Views: view_*() defines how the positional scales should change along the animation

• Shadows: shadow_*() defines how data from other points in time should be presented in the given point in time

• Entrances/Exits: enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation

• Easing: ease_aes() defines how different aesthetics should be eased during transitions

Learn more

• STA 313 lecture on animation: https://vizdata.org/slides/23/23-animation#/animation

• gganimate vignette: https://gganimate.com/articles/gganimate.html

Themes

Complete themes

p <- ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point()

p + theme_gray() + labs(title = "Gray")
p + theme_void() + labs(title = "Void")
p + theme_dark() + labs(title = "Dark")

Themes from ggthemes

library(ggthemes)

p + theme_fivethirtyeight() + labs(title = "FiveThirtyEight")
p + theme_economist() + labs(title = "Economist")
p + theme_wsj() + labs(title = "Wall Street Journal")

Themes and color scales from ggthemes

p + 
  aes(color = species) +
  scale_color_wsj() +
  theme_wsj() + 
  labs(title = "Wall Street Journal")

Modifying theme elements

p + 
  labs(title = "Palmer penguins") +
  theme(
    plot.title = element_text(color = "red", face = "bold", family = "Comic Sans MS"),
    plot.background = element_rect(color = "red", fill = "mistyrose")
  )

Axes

Presidential terms

How can the following figure be improved with custom breaks in axes, if at all?

• Plot
• Code
• Data

ggplot(presidential, aes(x = start, xend = end, y = name, yend = name)) +
  geom_segment()

presidential

# A tibble: 12 × 4
   name       start      end        party     
   <chr>      <date>     <date>     <chr>     
 1 Eisenhower 1953-01-20 1961-01-20 Republican
 2 Kennedy    1961-01-20 1963-11-22 Democratic
 3 Johnson    1963-11-22 1969-01-20 Democratic
 4 Nixon      1969-01-20 1974-08-09 Republican
 5 Ford       1974-08-09 1977-01-20 Republican
 6 Carter     1977-01-20 1981-01-20 Democratic
 7 Reagan     1981-01-20 1989-01-20 Republican
 8 Bush       1989-01-20 1993-01-20 Republican
 9 Clinton    1993-01-20 2001-01-20 Democratic
10 Bush       2001-01-20 2009-01-20 Republican
11 Obama      2009-01-20 2017-01-20 Democratic
12 Trump      2017-01-20 2021-01-20 Republican

Context matters: y-axis breaks

presidential |>
  mutate(name = fct_reorder(name, start)) |>
  ggplot(aes(x = start, xend = end, y = name, yend = name)) +
  geom_segment()

Context matters: y-axis breaks

presidential |>
  mutate(
    name = case_when(
      name == "Bush" & year(start) == 1989 ~ "Bush I",
      name == "Bush" & year(start) == 2001 ~ "Bush II",
      .default = name
    ),
    name = fct_reorder(name, start)
  ) |>
  ggplot(aes(x = start, xend = end, y = name, yend = name)) +
  geom_segment()

Context matters: y-axis breaks

Context matters: x-axis breaks

presidential <- presidential |>
  mutate(
    name = case_when(
      name == "Bush" & year(start) == 1989 ~ "Bush I",
      name == "Bush" & year(start) == 2001 ~ "Bush II",
      .default = name
    ),
    name = fct_reorder(name, start),
    start = year(start),
    end = year(end)
  )

ggplot(
  presidential,
  aes(x = start, xend = end, y = name, yend = name)
  ) +
  geom_segment() +
  scale_x_continuous(
    breaks = seq(from = 1952, to = 2020, by = 4),
    minor_breaks = NULL
  )

Context matters: x-axis breaks

Colors matter

ggplot(
  presidential,
  aes(x = start, xend = end, y = name, yend = name,
      color = party)
  ) +
  geom_segment(show.legend = FALSE) +
  scale_x_continuous(
    breaks = seq(from = 1952, to = 2020, by = 4),
    minor_breaks = NULL
  ) +
  scale_color_manual(
    values = c(
      "Democratic" = "blue",
      "Republican" = "red"
    )
  )

Colors matter

Precision matters

ggplot(
  presidential,
  aes(x = start, xend = end, y = name, yend = name, color = party)
  ) +
  geom_segment(show.legend = FALSE) +
  scale_x_continuous(
    breaks = seq(from = 1952, to = 2020, by = 4),
    minor_breaks = NULL
  ) +
  scale_color_manual(
    values = c(
      "Democratic" = "blue",
      "Republican" = "red"
    )
  ) +
  labs(x = "Election year", y = "President")

Precision matters

Annotation

Why annotate?

https://bl.ocks.org/susielu/23dc3082669ee026c552b85081d90976

geom_text()

Can be useful when individual observations are identifiable, but can also get overwhelming…

How would you improve this visualization?

ggplot(state_stats, aes(x = homeownership, y = pop2010)) + 
  geom_point()

ggplot(state_stats, aes(x = homeownership, y = pop2010)) + 
  geom_text(aes(label = abbr))

All of the data doesn’t tell a story

How the Recession Reshaped the Economy, in 255 Charts, NY Times (2014)

How the Recession Reshaped the Economy, in 255 Charts, NY Times (2014)

How the Recession Reshaped the Economy, in 255 Charts, NY Times (2014)

Highlighting in ggplot2

We have (at least) two options:

1. Native ggplot2 – use layers

2. gghighlight: https://yutannihilation.github.io/gghighlight/articles/gghighlight.html

Data: SF AQI

sf_files <- fs::dir_ls(here::here("data/san-francisco"))

sf <- read_csv(sf_files, na = c(".", ""))

sf <- sf |>
  janitor::clean_names() |>
  mutate(date = mdy(date)) |>
  arrange(date) |>
  select(date, aqi_value)

sf

# A tibble: 2,557 × 2
   date       aqi_value
   <date>         <dbl>
 1 2016-01-01        32
 2 2016-01-02        37
 3 2016-01-03        45
 4 2016-01-04        33
 5 2016-01-05        27
 6 2016-01-06        39
 7 2016-01-07        39
 8 2016-01-08        31
 9 2016-01-09        20
10 2016-01-10        20
# ℹ 2,547 more rows

Data prep

sf <- sf |>
  mutate(
    year = year(date),
    day_of_year = yday(date)
  )

# check
sf |>
  filter(day_of_year < 3)

# A tibble: 14 × 4
   date       aqi_value  year day_of_year
   <date>         <dbl> <dbl>       <dbl>
 1 2016-01-01        32  2016           1
 2 2016-01-02        37  2016           2
 3 2017-01-01        55  2017           1
 4 2017-01-02        36  2017           2
 5 2018-01-01        87  2018           1
 6 2018-01-02        95  2018           2
 7 2019-01-01        33  2019           1
 8 2019-01-02        50  2019           2
 9 2020-01-01        53  2020           1
10 2020-01-02        43  2020           2
11 2021-01-01        79  2021           1
12 2021-01-02        57  2021           2
13 2022-01-01        53  2022           1
14 2022-01-02        55  2022           2

Plot AQI over years

ggplot(sf, aes(x = day_of_year, y = aqi_value, group = year)) +
  geom_line()

Plot AQI over years

ggplot(sf, aes(x = day_of_year, y = aqi_value, group = year, color = year)) +
  geom_line()

Plot AQI over years

ggplot(sf, aes(x = day_of_year, y = aqi_value, group = year, color = factor(year))) +
  geom_line()

Highlight 2016

ggplot(sf, aes(x = day_of_year, y = aqi_value, group = year)) +
  geom_line(color = "gray") +
  geom_line(data = sf |> filter(year == 2016), color = "red") +
  labs(
    title = "AQI levels in SF in 2016",
    subtitle = "Versus all years 2016 - 2022",
    x = "Day of year", y = "AQI value"
    )

Highlight 2017

ggplot(sf, aes(x = day_of_year, y = aqi_value, group = year)) +
  geom_line(color = "gray") +
  geom_line(data = sf |> filter(year == 2017), color = "red") +
  labs(
    title = "AQI levels in SF in 2017",
    subtitle = "Versus all years 2016 - 2022",
    x = "Day of year", y = "AQI value"
    )

Highlight 2018

ggplot(sf, aes(x = day_of_year, y = aqi_value, group = year)) +
  geom_line(color = "gray") +
  geom_line(data = sf |> filter(year == 2018), color = "red") +
  labs(
    title = "AQI levels in SF in 2018",
    subtitle = "Versus all years 2016 - 2022",
    x = "Day of year", y = "AQI value"
    )

Highlight any year

year_to_highlight <- 2018

ggplot(sf, aes(x = day_of_year, y = aqi_value, group = year)) +
  geom_line(color = "gray") +
  geom_line(data = sf |> filter(year == year_to_highlight), color = "red") +
  labs(
    title = glue("AQI levels in SF in {year_to_highlight}"),
    subtitle = "Versus all years 2016 - 2022",
    x = "Day of year", y = "AQI value"
    )

Quarto

Quarto tips

• Figures and tables

• Cross references

• Bibliography

• Slides