Themes, axes, annotations

Lecture 10

Dr. Greg Chism

University of Arizona
INFO 526 - Fall 2024

Announcements

• RQ 03 is due Wednesday, October 09 at 5:00pm

Setup

# load packages
if(!require(pacman))
  install.packages("pacman")

pacman::p_load(countdown,
               tidyverse,
               colorspace,
               fs,
               palmerpenguins,
               scales,
               openintro,
               gghighlight,
               glue)

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

# set width of code output
options(width = 65)

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7, # 7" width
  fig.asp = 0.618, # the golden ratio
  fig.retina = 3, # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300 # higher dpi, sharper image
)

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")

UArizona theme!

Warning

This package is a work in progress. Feedback and issues welcome! See https://github.com/Gchism94/uarizona for more info.

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

Axis breaks

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

Context matters

pac_plot +
  scale_x_continuous(breaks = seq(from = 2000, to = 2022, by = 2))

Conciseness matters

pac_plot +
  scale_x_continuous(breaks = seq(2000, 2022, 4))

Precision matters

pac_plot +
  scale_x_continuous(breaks = seq(2000, 2022, 4)) +
  labs(x = "Election year")

Annotation

Why annotate?

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

Video recap

Discuss the main take aways from each of the videos assigned for today with your neighbor. Add one takeaway per video to the Slack thread.

geom_text()

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

How would you improve this visualization? Discuss with your neighbor and add your ideas to the Slack thread.

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

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

Revisit Tucson AQI

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"
    )

Highlight with gghighlight

year_to_highlight <- 2018
sf |> 
  ungroup() |>
ggplot(aes(x = day_of_year, y = aqi_value, group = year)) +
  geom_line(color = "red") + 
  gghighlight(year == 2018, use_direct_label = FALSE) +
  labs(
    title = glue("AQI levels in SF in {year_to_highlight}"),
    subtitle = "Versus all years 2016 - 2022",
    x = "Day of year", y = "AQI value"
  ) +
  theme_minimal(base_size = 12) +
  theme(legend.position = "none")  # Hide the legend