Designing Charts in R

Reproducible Graphic Design with {ggplot2}

Cédric Scherer // Data Visualization Society Masterclass // March 9, 2023

Welcome!

The ggplot2 Package

{ggplot2} is a system for declaratively creating graphics,
based on “The Grammar of Graphics” (Wilkinson, 2005).

You provide the data, tell {ggplot2} how to map variables to aesthetics,
what graphical primitives to use, and it takes care of the details.

Advantages of {ggplot2}

• code-first approach → reproducible and transparent workflow
  
• consistent underlying “grammar of graphics”
  
• very flexible, layered plot specification
  
• theme system for polishing plot appearance
  
• lots of additional functionality thanks to extensions
  
• active and helpful community

Illustration by Allison Horst

ggplot2 Examples featured on ggplot2.tidyverse.org

Illustration by Allison Horst

Selection of visualizations created 100% with ggplot2 by Thomas Linn Pedersen,
Georgios Karamanis, Timo Gossenbacher, Torsten Sprengler, Jake Kaupp, Jack Davison, and myself.

A Motivational Example

Setup

The ggplot2 Package

… is an R package to visualize data created by Hadley Wickham in 2005

# install.packages("ggplot2")
library(ggplot2)

… is part of the {tidyverse}

# install.packages("tidyverse")
library(tidyverse)

The Grammar of {ggplot2}

The Grammar of {ggplot2}

Component	Function	Explanation
Data	ggplot(data)	The raw data that you want to visualise.
Aesthetics	aes()	Aesthetic mappings between variables and visual properties.
Geometries	geom_*()	The geometric shapes representing the data.

The Grammar of {ggplot2}

Component	Function	Explanation
Data	ggplot(data)	The raw data that you want to visualise.
Aesthetics	aes()	Aesthetic mappings between variables and visual properties.
Geometries	geom_*()	The geometric shapes representing the data.
Statistics	stat_*()	The statistical transformations applied to the data.
Scales	scale_*()	Maps between the data and the aesthetic dimensions.
Facets	facet_*()	The arrangement of the data into a grid of plots.
Coordinate System	coord_*()	Maps data into the plane of the data rectangle.
Visual Themes	theme() and theme_*()	The overall visual defaults of a plot.

The Data

Bike sharing counts in London, UK, powered by TfL Open Data

• covers the years 2015 and 2016
• incl. weather data acquired from freemeteo.com
• prepared by Hristo Mavrodiev for Kaggle
• further modification by myself

bikes <- readr::read_csv(
  "./data/london-bikes-custom.csv",
  ## or: "https://cedricscherer.com/data/london-bikes-custom.csv"
  col_types = "Dcfffilllddddc"
)

bikes$season <- forcats::fct_inorder(bikes$season)

Variable	Description	Class
date	Date encoded as `YYYY-MM-DD`	date
day_night	`day` (6:00am–5:59pm) or `night` (6:00pm–5:59am)	character
year	`2015` or `2016`	factor
month	`1` (January) to `12` (December)	factor
season	`winter`, `spring`, `summer`, or `autumn`	factor
count	Sum of reported bikes rented	integer
is_workday	`TRUE` being Monday to Friday and no bank holiday	logical
is_weekend	`TRUE` being Saturday or Sunday	logical
is_holiday	`TRUE` being a bank holiday in the UK	logical
temp	Average air temperature (°C)	double
temp_feel	Average feels like temperature (°C)	double
humidity	Average air humidity (%)	double
wind_speed	Average wind speed (km/h)	double
weather_type	Most common weather type	character

Fundamentals of {ggplot2}

ggplot2::ggplot()

Data

ggplot(data = bikes)

Aesthetic Mapping

= link variables to graphical properties

• positions (x, y)
• colors (color, fill)
• shapes (shape, linetype)
• size (size)
• transparency (alpha)
• groupings (group)

Aesthetic Mapping

= link variables to graphical properties

• feels-like temperature ⇄ x
• reported bike shares ⇄ y
• season ⇄ color
• year ⇄ shape
• …

Aesthetic Mapping

ggplot(data = bikes) +
  aes(x = temp_feel, y = count)

Aesthetic Mapping

ggplot(
  data = bikes,
  mapping = aes(x = temp_feel, y = count)
)

Aesthetic Mapping

ggplot(
  bikes,
  aes(x = temp_feel, y = count)
)

Geometrical Layers

Geometries

= interpret aesthetics as graphical representations

• points
• lines
• polygons
• text labels
• …

Geometries

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point()

Geometries

ggplot(
    bikes,
    aes(x = humidity)
  ) +
  geom_histogram()

Geometries

ggplot(
    bikes,
    aes(x = season, y = count)
  ) +
  geom_boxplot()

Visual Properties of Layers

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    color = "dodgerblue",
    alpha = .5,
    shape = "X",
    stroke = 1,
    size = 4
  )

Setting vs Mapping of Visual Properties

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    color = "dodgerblue",
    alpha = .5
  )

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    aes(color = season),
    alpha = .5
  )

Mapping Expressions

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    aes(color = temp_feel > 20),
    alpha = .5
  )

Your Turn

• Create a chart showing a time series of reported bike shares
  • What is the difference between geom_line() and geom_path()?
  • Map the variable day_night to the color of the lines.
  • Add points for each observation, encoded by the same variable.
  • Turn the points into diamonds.

Solution Exercise

ggplot(
    bikes,
    aes(x = date, y = count)
  ) +
  geom_line()

Solution Exercise

ggplot(
    bikes,
    aes(x = count, y = date)
  ) +
  geom_line()

ggplot(
    bikes,
    aes(x = count, y = date)
  ) +
  geom_path()

Solution Exercise

ggplot(
    bikes,
    aes(x = date, y = count)
  ) +
  geom_line(
    aes(color = day_night)
  )

Solution Exercise

ggplot(
    bikes,
    aes(x = date, y = count)
  ) +
  geom_line(
    aes(color = day_night)
  ) +
  geom_point()

Solution Exercise

ggplot(
    bikes,
    aes(x = date, y = count)
  ) +
  geom_line(
    aes(color = day_night)
  ) +
  geom_point(
    aes(color = day_night)
  )

Solution Exercise

ggplot(
    bikes,
    aes(x = date, y = count,
        color = day_night)
  ) +
  geom_line() +
  geom_point()

Solution Exercise

ggplot(
    bikes,
    aes(x = date, y = count,
        color = day_night)
  ) +
  geom_line() +
  geom_point(
    shape = "diamond",
    size = 3
  )

Solution Exercise

ggplot(
    bikes,
    aes(x = date, y = count,
        color = day_night)
  ) +
  geom_line() +
  geom_point(
    shape = 18,
    size = 3
  )

Source: Albert’s Blog

Local vs. Global Encoding

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    aes(color = season),
    alpha = .5
  )

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season)
  ) +
  geom_point(
    alpha = .5
  )

Adding More Layers

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season)
  ) +
  geom_point(
    alpha = .5
  ) +
  geom_smooth(
    method = "lm"
  )

Global Color Encoding

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season)
  ) +
  geom_point(
    alpha = .5
  ) +
  geom_smooth(
    method = "lm"
  )

Local Color Encoding

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    aes(color = season),
    alpha = .5
  ) +
  geom_smooth(
    method = "lm"
  )

The `group` Aesthetic

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    aes(color = season),
    alpha = .5
  ) +
  geom_smooth(
    aes(group = day_night),
    method = "lm"
  )

Set Both as Global Aesthetics

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season,
        group = day_night)
  ) +
  geom_point(
    alpha = .5
  ) +
  geom_smooth(
    method = "lm"
  )

Overwrite Global Aesthetics

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season,
        group = day_night)
  ) +
  geom_point(
    alpha = .5
  ) +
  geom_smooth(
    method = "lm",
    color = "black"
  )

Store a ggplot as Object

g <-
  ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season,
        group = day_night)
  ) +
  geom_point(
    alpha = .5
  ) +
  geom_smooth(
    method = "lm",
    color = "black"
  )

class(g)

[1] "gg"     "ggplot"

Add More Layers

g +
  geom_rug(
    alpha = .2
  )

Remove a Layer from the Legend

g +
  geom_rug(
    alpha = .2,
    show.legend = FALSE
  )

Add More Layers

g +
  geom_rug(
    alpha = .2,
    show.legend = FALSE
  ) +
  geom_linerange(
    aes(ymin = 0, ymax = count)
  )

A Polished
ggplot Example

Add Labels

g +
  labs(
    x = "Feels-like temperature (°C)",
    y = "Reported bike shares",
    title = "TfL bike sharing trends"
  )

Add Labels

g <- g +
  labs(
    x = "Feels-like temperature (°C)",
    y = "Reported bike shares",
    title = "TfL bike sharing trends",
    color = NULL
  )

g

Add Labels

g +
  labs(
    subtitle = "Reported bike rents versus feels-like temperature in London",
    caption = "Data: TfL",
    tag = "A)",
    color = "Season:"
  )

Add Labels

g +
  labs(
    x = "",
    caption = "Data: TfL"
  )

g +
  labs(
    x = NULL,
    caption = "Data: TfL"
  )

Themes

g + theme_light()

g + theme_minimal()

Themes

g + ggthemes::theme_excel()

g + tvthemes::theme_rickAndMorty()

Change the Theme Base Settings

g + theme_light(
  base_size = 14,
  base_family = "Asap Condensed"
)

Set a Theme Globally

theme_set(theme_light())

g

Change the Theme Base Settings

theme_set(theme_light(
  base_size = 14,
  base_family = "Asap Condensed"
))

g

{systemfonts}

# install.packages("systemfonts")
library(systemfonts)

system_fonts() %>%
  filter(stringr::str_detect(family, "Asap")) %>%
  pull(family) %>%
  unique() %>% 
  sort()

[1] "Asap"               "Asap Condensed"     "Asap Expanded"      "Asap SemiCondensed" "Asap SemiExpanded" 

{systemfonts}

register_variant(
  name = "Cabinet Grotesk Black",
  family = "Cabinet Grotesk",
  weight = "heavy",
  features = font_feature(letters = "stylistic")
)

{systemfonts} + {ggplot2}

g +
  theme_light(
    base_size = 18,
    base_family = "Cabinet Grotesk Black"
  )

Overwrite Specific Theme Settings

g +
  theme(
    panel.grid.minor = element_blank()
  )

Overwrite Specific Theme Settings

g +
  theme(
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold")
  )

Overwrite Specific Theme Settings

g +
  theme(
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold"),
    legend.position = "top"
  )

Overwrite Specific Theme Settings

g +
  theme(
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold"),
    legend.position = "none"
  )

Overwrite Specific Theme Settings

g +
  theme(
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold"),
    legend.position = "top",
    plot.title.position = "plot"
  )

Overwrite Theme Settings Globally

theme_update(
  panel.grid.minor = element_blank(),
  plot.title = element_text(face = "bold"),
  legend.position = "top",
  plot.title.position = "plot"
)

g

Export Your Graphic

Save the Graphic

ggsave(g, filename = "my_plot.png")

ggsave("my_plot.png")

ggsave("my_plot.png", width = 8, height = 5, dpi = 600)

ggsave("my_plot.pdf", width = 20, height = 12, unit = "cm", device = cairo_pdf)

Modified from canva.com

Scales

Scales

= translate between variable and property ranges

• feels-like temperature ⇄ x
• reported bike shares ⇄ y
• season ⇄ color
• year ⇄ shape
• …

Scales

The scale_*() components control the properties of all the
aesthetic dimensions mapped to the data.

Consequently, there are scale_*() functions for all aesthetics such as:

• positions via scale_x_*() and scale_y_*()
• colors via scale_color_*() and scale_fill_*()
• sizes via scale_size_*() and scale_radius_*()
• shapes via scale_shape_*() and scale_linetype_*()
• transparency via scale_alpha_*()

… with tons of options for * such as continuous, discrete, manual, log10, gradient, and many more

Scales

The scale_*() components control the properties of all the
aesthetic dimensions mapped to the data.

Use scales to:

• set breaks (tick marks)
• overwrite axis / legend labels
• adjust axis / legend title
• modify colors and palettes
• define shapes and line types
• scale sizes (e.g. bubbles)

Illustration by Allison Horst

Continuous vs. Discrete in {ggplot2}

Continuous:
quantitative or numerical data

• height
• weight
• age
• counts

Discrete:
qualitative or categorical data

• species
• sex
• study sites
• age group

Continuous vs. Discrete in {ggplot2}

Continuous:
quantitative or numerical data

• height (continuous)
• weight (continuous)
• age (continuous or discrete)
• counts (discrete)

Discrete:
qualitative or categorical data

• species (nominal)
• sex (nominal)
• study site (nominal or ordinal)
• age group (ordinal)

Aesthetics + Scales

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season)
  ) +
  geom_point() +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_color_discrete()

Aesthetics + Scales

ggplot(
    bikes,
    aes(x = season, y = temp_feel)
  ) +
  geom_boxplot() +
  scale_x_discrete() +
  scale_y_continuous()

Aesthetics + Scales

g +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_color_discrete()

Overwrite Scales

g +
  scale_x_binned() +
  scale_y_log10() +
  scale_color_viridis_d()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0)
  ) +
  scale_y_continuous() +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5)
  ) +
  scale_y_continuous() +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C")
  ) +
  scale_y_continuous() +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous() +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    limits = c(-20000, NA)
  ) +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    limits = c(5000, 20000)
  ) +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    breaks = c(100, 10000, 20000, 50000)
  ) +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    breaks = 0:5*10000
  ) +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scale_color_discrete()

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous( 
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scale_color_discrete(
    type = c("#3c89d9", "#1ec99b", "#f7b01b", "#a26e7c")
  )

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scale_color_manual(
    values = c("#3c89d9", "#1ec99b", "#f7b01b", "#a26e7c")
  )

Modify Scales

colors_sorted <- c(
  autumn = "#a26e7c",
  spring = "#1ec99b",
  summer = "#f7b01b",
  winter = "#3c89d9"
)

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scale_color_manual(
    values = colors_sorted
  )

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scale_color_brewer(
    palette = "Dark2"
  )

{RColorBrewer}

RColorBrewer::display.brewer.all()

{RColorBrewer}

RColorBrewer::display.brewer.all(colorblindFriendly = TRUE)

{rcartocolor}

# install.packages("rcartocolor")
rcartocolor::display_carto_all()

{rcartocolor}

# install.packages("rcartocolor")
rcartocolor::display_carto_all(colorblind_friendly = TRUE)

{rcartocolor}

g +
  scale_x_continuous(
    expand = c(mult = 0.02, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    expand = c(mult = 0, add = 1500), 
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  rcartocolor::scale_color_carto_d(
    palette = "Bold"
  )

{scico}

# install.packages("scico")
scico::scico_palette_show()

{scico}

g +
  scale_x_continuous(
    expand = c(mult = 0.02, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    expand = c(mult = 0, add = 1500), 
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scico::scale_color_scico_d(
    palette = "hawaii"
  )

{scico}

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = humidity)
  ) +
  geom_point(alpha = .7) +
  scico::scale_color_scico(
    palette = "davos",
    direction = -1,
    end = .8
  )

{scico}

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = humidity)
  ) +
  geom_point(alpha = .7) +
  scale_color_viridis_c(
    direction = -1
  )

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous( 
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scale_color_manual(
    values = colors_sorted,
    name = NULL
  )

Modify Scales

g +
  scale_x_continuous(
    expand = c(mult = 0, add = 0),
    breaks = seq(0, 30, by = 5), 
    labels = function(x) paste0(x, "°C"),
    name = "Feels-like temperature"
  ) +
  scale_y_continuous(
    breaks = 0:5*10000, 
    labels = scales::label_comma()
  ) +
  scale_color_manual(
    values = colors_sorted,
    name = NULL,
    labels = stringr::str_to_title
  )

Your Turn

• Style the time series of reported bike shares.
  • Add a plot title and meaningful axis and legend titles.
  • Use a custom set of colors for day and night.
  • Explore complete themes and pick your favorite.
  • Bonus: Modify the x axis to show every four months along with the year.

Solution Exercise

(p <- 
   ggplot(
    bikes,
    aes(x = date, y = count,
        color = day_night)
  ) +
  geom_line() +
  geom_point()
)

Solution Exercise

p + 
  labs(
    title = "Most bikes are rented during summer days",
    subtitle = "Reported rents of TfL bikes for 2015 and 2016.",
    x = NULL, 
    y = "Reported bike shares", 
    color = NULL
  )

Solution Exercise

p + 
  scale_color_manual(
    values = c("#98730F", "#44458e"),
    labels = c("Day (6am-6pm)", "Night (6pm-6am)")
  ) +
  labs(title = "Most bikes are rented during summer days",
       subtitle = "Reported rents of TfL bikes for 2015 and 2016.",
       x = NULL, y = "Reported bike shares", color = NULL)

Solution Exercise

p + 
  scale_color_manual(
    values = c("#98730F", "#44458e"),
    labels = c("Day (6am-6pm)", "Night (6pm-6am)")
  ) +
  theme_minimal(base_family = "Spline Sans", base_size = 15) +
  labs(title = "Most bikes are rented during summer days",
       subtitle = "Reported rents of TfL bikes for 2015 and 2016.",
       x = NULL, y = "Reported bike shares", color = NULL)

Solution Exercise

p + 
  scale_color_manual(
    values = c("#98730F", "#44458e"),
    labels = c("Day (6am-6pm)", "Night (6pm-6am)")
  ) +
  theme_minimal(base_family = "Spline Sans", base_size = 15) +
  theme(
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold"),
    plot.title.position = "plot",
    legend.position = "top"
  ) +
  labs(title = "Most bikes are rented during summer days",
       subtitle = "Reported rents of TfL bikes for 2015 and 2016.",
       x = NULL, y = "Reported bike shares", color = NULL)

Solution Exercise

p + 
  scale_x_date(
    date_breaks = "4 months",
    date_labels = "%m/'%y"
  ) +
  scale_color_manual(
    values = c("#98730F", "#44458e"),
    labels = c("Day (6am-6pm)", "Night (6pm-6am)")
  ) +
  theme_minimal(base_family = "Spline Sans", base_size = 15) +
  theme(
    panel.grid.minor = element_blank(),
    plot.title = element_text(face = "bold"),
    plot.title.position = "plot",
    legend.position = "top"
  ) +
  labs(title = "Most bikes are rented during summer days",
       subtitle = "Reported rents of TfL bikes for 2015 and 2016.",
       x = NULL, y = "Reported bike shares", color = NULL)

Facets

Facets

= split variables to multiple panels

Facets are also known as:

• small multiples
• trellis graphs
• lattice plots
• conditioning

Wrapped Facets

g +
  facet_wrap(
    vars(day_night)
  )

Wrapped Facets

g +
  facet_wrap(
    ~ day_night
  )

Facet Multiple Variables

g +
  facet_wrap(
    ~ is_workday + day_night
  )

Gridded Facets

g +
  facet_grid(
    rows = vars(day_night),
    cols = vars(is_workday)
  )

Gridded Facets

g +
  facet_grid(
    day_night ~ is_workday
  )

Facet Multiple Variables

g +
  facet_grid(
    day_night ~ is_workday + season
  )

Facet Options: Free Scaling

g +
  facet_grid(
    day_night ~ is_workday,
    scales = "free"
  )

Facet Options: Proportional Spacing

g +
  facet_grid(
    day_night ~ is_workday,
    scales = "free",
    space = "free"
  )

Facet Options: Proportional Spacing

g +
  facet_grid(
    day_night ~ is_workday,
    scales = "free_y",
    space = "free_y"
  )

Facet Labellers

g +
  facet_wrap(
    ~ day_night,
    labeller = label_both
  )

Facet Labellers

g +
  facet_wrap(
    ~ is_workday + day_night,
    labeller = labeller(
      day_night = stringr::str_to_title
    )
  )

Facet Labellers

codes <- c(
  `TRUE` = "Workday",
  `FALSE` = "Weekend or Holiday"
)

g +
  facet_wrap(
    ~ is_workday + day_night,
    labeller = labeller(
      day_night = stringr::str_to_title,
      is_workday = codes
    )
  )

Coordinate Systems

Coordinate Systems

= interpret the position aesthetics

• linear coordinate systems: preserve the geometrical shapes
  • coord_cartesian()
  • coord_fixed()
  • coord_flip()
• non-linear coordinate systems: likely change the geometrical shapes
  • coord_polar()
  • coord_map() and coord_sf()
  • coord_trans()

Cartesian Coordinate System

ggplot(
    bikes,
    aes(x = season, y = count)
  ) +
  geom_boxplot() +
  coord_cartesian()

Cartesian Coordinate System

ggplot(
    bikes,
    aes(x = season, y = count)
  ) +
  geom_boxplot() +
  coord_cartesian(
    ylim = c(NA, 15000)
  )

Changing Limits

ggplot(
    bikes,
    aes(x = season, y = count)
  ) +
  geom_boxplot() +
  coord_cartesian(
    ylim = c(NA, 15000)
  )

ggplot(
    bikes,
    aes(x = season, y = count)
  ) +
  geom_boxplot() +
  scale_y_continuous(
    limits = c(NA, 15000)
  )

Clipping

ggplot(
    bikes,
    aes(x = season, y = count)
  ) +
  geom_boxplot() +
  coord_cartesian(
    ylim = c(NA, 15000),
    clip = "off"
  ) +
  theme(plot.margin = margin(300, 5, 5, 5))

Remove All Padding

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point() +
  coord_cartesian(
    expand = FALSE,
    clip = "off"
  )

Fixed Coordinate System

ggplot(
    bikes,
    aes(x = temp_feel, y = temp)
  ) +
  geom_point() +
  coord_fixed()

ggplot(
    bikes,
    aes(x = temp_feel, y = temp)
  ) +
  geom_point() +
  coord_fixed(ratio = 4)

Flipped Coordinate System

ggplot(
    bikes,
    aes(x = weather_type)
  ) +
  geom_bar() +
  coord_cartesian()

ggplot(
    bikes,
    aes(x = weather_type)
  ) +
  geom_bar() +
  coord_flip()

Flipped Coordinate System

ggplot(
    bikes,
    aes(y = weather_type)
  ) +
  geom_bar() +
  coord_cartesian()

ggplot(
    bikes,
    aes(x = weather_type)
  ) +
  geom_bar() +
  coord_flip()

Reminder: Sort Your Bars!

library(forcats)

ggplot(
    filter(bikes, !is.na(weather_type)),
    aes(y = fct_infreq(weather_type))
  ) +
  geom_bar()

Reminder: Sort Your Bars!

library(forcats)

ggplot(
    filter(bikes, !is.na(weather_type)),
    aes(y = fct_rev(
      fct_infreq(weather_type)
    ))
  ) +
  geom_bar()

Wrap-Up

g1 <- 
  ## create scatter plot, encoded based on season
  ggplot(bikes, aes(temp_feel, count)) +
  geom_point(
    aes(color = season),
    size = 2.2, alpha = .55
  ) 

g1

g2 <- g1 +
  ## add a linear fitting for each time of the day
  geom_smooth(
    aes(group = day_night),
    method = "lm", color = "black"
  )

g2

g3 <- g2 +
  ## add titles + labels
  labs(
    x = "Feels-Like Temperature", y = NULL,
    caption = "Data: Transport for London (TfL), Jan 2015—Dec 2016",
    title = "Reported TfL bike rents versus feels-like temperature in London, 2015–2016"
  )

g3

g4 <- g3 + 
  theme_light(base_size = 18, base_family = "Spline Sans")

g4

codes <- c(
  `TRUE` = "Workday",
  `FALSE` = "Weekend or Holiday"
)

g5 <- g4 +
  facet_grid(
    day_night ~ is_workday,
    scales = "free_y", space = "free_y",
    labeller = labeller(
      day_night = stringr::str_to_title,
      is_workday = codes
    )
  )

g5

g6 <- g5  +
  ## adjust labels x-axis
  scale_x_continuous(
    expand = c(mult = 0, add = 1),
    breaks = 0:6*5, labels = function(x) paste0(x, "°C")
  ) +
  ## adjust labels y-axis
  scale_y_continuous(
    expand = c(mult = .05, add = 0), limits = c(0, NA),
    breaks = 0:5*10000, labels = scales::label_comma()
  ) +
  ## modify colors + legend
  scale_color_manual(
    values = c("#3c89d9", "#1ec99b", "#F7B01B", "#a26e7c"), 
    name = NULL, labels = stringr::str_to_title
  ) 

g6

g6 + 
  ## theme modifications
  theme(
    plot.title.position = "plot",
    plot.caption.position = "plot",
    plot.title = element_text(face = "bold", size = rel(1.5)),
    axis.text = element_text(family = "Spline Sans Mono", color = "grey45"),
    axis.title.x = element_text(hjust = 0, margin = margin(t = 12), color = "grey25"),
    strip.text = element_text(face = "bold", size = rel(1.1)),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    legend.text = element_text(size = rel(.9)),
    legend.position = "top",
    legend.justification = "left"
  )

That’s it Folks…
— Thank you! —

Appendix

Statistical Layers

geom_*() and stat_*()

ggplot(bikes, aes(x = temp_feel, y = count)) +
  geom_smooth(stat = "smooth")

ggplot(bikes, aes(x = temp_feel, y = count)) +
  stat_smooth(geom = "smooth")

geom_*() and stat_*()

ggplot(bikes, aes(x = date, y = temp_feel)) +
  geom_point(stat = "identity")

ggplot(bikes, aes(x = date, y = temp_feel)) +
  stat_identity(geom = "point")

geom_*() and stat_*()

ggplot(bikes, aes(x = is_weekend)) +
  geom_bar(stat = "count")

ggplot(bikes, aes(x = is_weekend)) +
  stat_count(geom = "bar")

Statistical Summaries

ggplot(
    bikes, 
    aes(x = season, y = temp_feel)
  ) +
  stat_summary() 

Statistical Summaries

ggplot(
    bikes, 
    aes(x = season, y = temp_feel)
  ) +
  stat_summary(
    fun.data = mean_se, ## the default
    geom = "pointrange"  ## the default
  ) 

Statistical Summaries

ggplot(
    bikes, 
    aes(x = season, y = temp_feel)
  ) +
  geom_boxplot() +
  stat_summary(
    fun = mean,
    geom = "point",
    color = "#28a87d",
    size = 3
  ) 

Statistical Summaries

ggplot(
    bikes, 
    aes(x = season, y = temp_feel)
  ) +
  stat_summary() +
  stat_summary(
    fun = mean,
    geom = "text",
    aes(label = after_stat(y))
  )

Statistical Summaries

ggplot(
    bikes, 
    aes(x = season, y = temp_feel)
  ) +
  stat_summary() +
  stat_summary(
    fun = mean,
    geom = "text",
    aes(label = after_stat(
      paste0(round(y, 2), "°C"))
    ),
    hjust = -.2,
    size = 3.5
  )

Aspect Ratios

How to Work with Aspect Ratios

• don’t rely on the Rstudio viewer pane!
• once you have a “it’s getting close” prototype, settle on a plot size
  
  
• Approach 1: save the file to disk and inspect it; go back to your IDE
  • tedious and time-consuming…
    
    
• Approach 2: use a qmd or rmd with inline output and chunk settings
  • set fig.width and fig.height per chunk or globally
    
    
• Approach 3: use our {camcorder} package
  • saves output from all ggplot() calls and displays it in the viewer pane

Setting Plot Sizes in Rmd’s

Setting Plot Sizes via {camcorder}