Engaging and Beautiful Data Visualizations with ggplot2

Fundamentals & Workflows

Cédric Scherer // posit::conf // September 2023

{ggplot2}

{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}

• consistent underlying “grammar of graphics” (Wilkinson 2005)
  
• very flexible, layered plot specification
  
• theme system for polishing plot appearance
  
• lots of additional functionality thanks to extensions
  
• active and helpful community

The Components of a ggplot

Component	Function	Explanation
Data	ggplot(data)	The raw data that you want to visualize (initialising a plot).
Aesthetics	aes()	The mapping between variables and visual properties.
Geometries	geom_*()	The geometric shape of a layer representing the data.

ggplot2 examples featured on ggplot2.tidyverse.org

The Components of a ggplot

Component	Function	Explanation
Data	ggplot(data)	The raw data that you want to visualize (initializing a plot).
Aesthetics	aes()	The mapping between variables and visual properties.
Geometries	geom_*()	The geometric shape of a layer representing the data.
Statistics	stat_*()	The statistical transformation of a layer applied to the data.
Scales	scale_*()	The representation of mapped aesthetic attributes.
Coordinate System	coord_*()	The transformation to map data coordinates into the plot plane.
Facets	facet_*()	The arrangement of the data into a set of small multiples.
Visual Themes	theme() | theme_*()	The overall visual defaults of non-data elements of the graphic.

Illustration by Allison Horst

Illustration by Allison Horst

The {ggplot2} Showcase

Collection of Graphics from the BBC R Cookbook

Collection of Graphics from the BBC R Cookbook

“Bill Dimensions of Brush-Tailed Penguins”

“Netflix Content Explosion” by Tanya Shapiro

My reinterpreted The Economist graphic

“Not My Cup of Coffee”

“Food Carbon Footprint Index 2018”

“Popular Programming Languages in CRAN Packages” by Torsten Sprenger

“Appearance of X-Men Characters”

“Artists in the US” by Lee Olney

My Contribution to the SWDchallenge “Small Multiples”

“European Energy Generation” by Jack Davison

Moon Charts as a Tile Grid Map showing the 2nd Vote Results from the German Election 2021

Our Winning Contribution to the BES MoveMap Contest

Bivariate Choropleth x Hillshade Map by Timo Gossenbacher

Pixel Art by Georgios Karamanis

Generative Art by Thomas Lin Pedersen

A Walk-Through Example

The Data Set

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

The Data Set

library(readr)
library(ggplot2)

bikes <-
  read_csv(
    here::here("data", "london-bikes.csv"),
    col_types = "Dcfffilllddddc"
  )

The Data Set

bikes

# A tibble: 1,454 × 14
   date       day_night year  month season count is_workday is_weekend is_holiday  temp temp_feel humidity wind_speed weather_type    
   <date>     <chr>     <fct> <fct> <fct>  <int> <lgl>      <lgl>      <lgl>      <dbl>     <dbl>    <dbl>      <dbl> <chr>           
 1 2015-01-04 day       2015  1     3       6830 FALSE      TRUE       FALSE       2.17     -0.75     95.2      10.4  broken clouds   
 2 2015-01-04 night     2015  1     3       2404 FALSE      TRUE       FALSE       2.79      2.04     93.4       4.58 clear           
 3 2015-01-05 day       2015  1     3      14763 TRUE       FALSE      FALSE       8.96      7.71     81.1       8.67 broken clouds   
 4 2015-01-05 night     2015  1     3       5609 TRUE       FALSE      FALSE       7.12      5.71     79.5       9.04 cloudy          
 5 2015-01-06 day       2015  1     3      14501 TRUE       FALSE      FALSE       9         6.46     80.2      19.2  broken clouds   
 6 2015-01-06 night     2015  1     3       6112 TRUE       FALSE      FALSE       6.71      4.21     77.6      12.8  clear           
 7 2015-01-07 day       2015  1     3      16358 TRUE       FALSE      FALSE       8.17      5.08     75.2      21.2  scattered clouds
 8 2015-01-07 night     2015  1     3       4706 TRUE       FALSE      FALSE       6.68      3.86     81.3      18.1  clear           
 9 2015-01-08 day       2015  1     3       9971 TRUE       FALSE      FALSE       9.46      7.12     79.4      18.8  scattered clouds
10 2015-01-08 night     2015  1     3       5630 TRUE       FALSE      FALSE      10.0       8.46     79.2      22.2  clear           
# ℹ 1,444 more rows

The Data Set

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	`0` (spring), `1` (summer), `2` (autumn), or `3` (winter)	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

A Default ggplot

# scatter plot of plot bikes$count versus bikes$temp_feel
ggplot(data = bikes) +              # initial call + data
  aes(x = temp_feel, y = count) +   # aesthetics
  geom_point()                      # geometric layer

A Default ggplot

# scatter plot of plot bikes$count versus bikes$temp_feel
ggplot(bikes, aes(x = temp_feel, y = count)) +
  geom_point()

Combine Layers

ggplot(bikes, aes(x = temp_feel, y = count)) +
  geom_point() + 
  # add a GAM smoothing
  stat_smooth() # also: geom_smooth()

Mapping Aesthetics

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

Mapping Aesthetics

ggplot(bikes, aes(x = temp_feel, y = count)) + 
  # color mapping only applied to points
  geom_point(aes(color = day_night)) + 
  # invisible grouping to create two trend lines
  stat_smooth(aes(group = day_night))

Setting Properties

ggplot(bikes, aes(x = temp_feel, y = count)) + 
  geom_point(
    aes(color = day_night), 
    # setting larger points with 50% opacity
    alpha = .5, size = 1.5
  ) + 
  stat_smooth(
    aes(group = day_night), 
    # use linear fitting + draw black smoothing lines
    method = "lm", color = "black"
  )

Split into Facets

ggplot(bikes, aes(x = temp_feel, y = count)) + 
  geom_point(
    aes(color = day_night), 
    alpha = .5, size = 1.5
  ) + 
  stat_smooth(
    method = "lm", color = "black"
  ) +
  # small multiples
  facet_wrap(facets = vars(day_night)) # also: ~ day_night

Split into Facets

ggplot(bikes, aes(x = temp_feel, y = count)) + 
  geom_point(
    aes(color = season), 
    alpha = .5, size = 1.5
  ) + 
  stat_smooth(
    method = "lm", color = "black"
  ) +
  # small multiples
  facet_grid(
    rows = vars(day_night), cols = vars(year) # also: day_night ~ year
  )

Free Facets Axes

ggplot(bikes, aes(x = temp_feel, y = count)) + 
  geom_point(
    aes(color = season), 
    alpha = .5, size = 1.5
  ) + 
  stat_smooth(
    method = "lm", color = "black"
  ) +
  facet_grid(
    day_night ~ year, 
    # free y axis range
    scales = "free_y", 
    # scale heights proportionally
    space = "free_y"
  )

Store ggplot

g1 <- 
  ggplot(bikes, aes(x = temp_feel, y = count)) + 
  geom_point(
    aes(color = season), 
    alpha = .5, size = 1.5
  ) + 
  stat_smooth(
    method = "lm", color = "black"
  ) +
  facet_grid(
    day_night ~ year, 
    scales = "free_y", 
    space = "free_y"
  )

Change the Axis Scaling

g1 +
  # x axis
  scale_x_continuous(
    # add °C symbol
    labels = function(x) paste0(x, "°C"), 
    # use 5°C spacing
    breaks = -1:6*5  # also: seq(-5, 30, by = 5)
  )

Change the Axis Scaling

g2 <- g1 +
  # x axis
  scale_x_continuous(
    # add °C symbol
    labels = function(x) paste0(x, "°C"), 
    # use 5°C spacing
    breaks = -1:6*5  # also: seq(-5, 30, by = 5)
  ) +
  # y axis
  scale_y_continuous(
    # add a thousand separator
    labels = scales::label_comma(), 
    # use consistent spacing across rows
    breaks = 0:5*10000
  )

Use a Custom Color Palette

g2 +
  # use a custom color palette for season colors
  scale_color_manual(
    values = c("#6681FE", "#1EC98D", "#F7B01B", "#A26E7C")
  )

Use a Custom Color Palette

# use a named vector for explicit matching
colors <- c(
  `0` = "#1EC98D",
  `1` = "#F7B01B",
  `2` = "#A26E7C",
  `3` = "#6681FE"
)

g2 +
  scale_color_manual(
    values = colors
  )

Adjust Labels and Titles

# use a named vector for explicit matching
colors <- c(
  `0` = "#1EC98D",
  `1` = "#F7B01B",
  `2` = "#A26E7C",
  `3` = "#6681FE"
)

g2 +
  scale_color_manual(
    values = colors,
    # overwrite legend keys
    labels = c("Winter", "Spring", "Summer", "Autumn")
  )

Adjust Labels and Titles

g3 <- g2 +
  scale_color_manual(
    values = colors,
    labels = c("Winter", "Spring", "Summer", "Autumn")
  ) +
  labs(
    # overwrite axis and legend titles
    x = "Average feels-like temperature", y = NULL, color = NULL,
    # add plot title and caption
    title = "Trends of Reported Bike Rents versus Feels-Like Temperature in London",
    caption = "Data: TfL (Transport for London), Jan 2015–Dec 2016"
  )

Apply a Complete Theme

g3 +
  # add theme with a custom font + larger element sizes
  theme_light(
    base_size = 15, base_family = "Spline Sans"
  )

Apply a Complete Theme

g4 <- g3 +
  theme_light(base_size = 15, base_family = "Spline Sans") +
  # theme adjustments
  theme(
    plot.title.position = "plot", # left-align title 
    plot.caption.position = "plot", # right-align caption
    legend.position = "top", # place legend above plot
    plot.title = element_text(face = "bold", size = rel(1.4)), # larger, bold title
    axis.text = element_text(family = "Spline Sans Mono"), # monospaced font for axes
    axis.title.x = element_text( # left-aligned, grey x axis label
      hjust = 0, color = "grey20", margin = margin(t = 12)
    ),
    legend.text = element_text(size = rel(1)), # larger legend labels
    strip.text = element_text(face = "bold", size = rel(1.15)), # larger, bold facet labels
    panel.grid.major.x = element_blank(), # no vertical major lines
    panel.grid.minor = element_blank(), # no minor grid lines
    panel.spacing.x = unit(20, "pt"), # increase white space between panels
    panel.spacing.y = unit(10, "pt"), # increase white space between panels
    plot.margin = margin(rep(15, 4)) # adjust white space around plot
  )

Adjust Legend

g4 +
  # adjust symbol size in legend
  guides(
    color = guide_legend(override.aes = list(size = 4))
  )

Adjust Legend

g4 +
  scale_color_manual(
    values = colors,
    labels = c("Winter", "Spring", "Summer", "Autumn"),
    # adjust symbol size in legend size
    guide = guide_legend(override.aes = list(size = 4))
  )

Full Code

# create named color vector
colors <- c(
  `0` = "#1EC98D",
  `1` = "#F7B01B",
  `2` = "#A26E7C",
  `3` = "#6681FE"
)

# scatter plot of plot bikes$count versus bikes$temp_feel
ggplot(bikes, aes(x = temp_feel, y = count)) + 
  # add points
  geom_point(
    # color mapping only applied to points
    aes(color = season), 
    # setting larger points with 50% opacity
    alpha = .5, size = 1.5
  ) + 
  # add a smoothing
  stat_smooth(  # also: geom_smooth()
    # use linear fitting + draw black smoothing lines
    method = "lm", color = "black"
  ) +
  # small multiples
  facet_grid(
    day_night ~ year,  # also: vars(day_night), vars(year)
    # free y axis range
    scales = "free_y", 
    # scale heights proportionally 
    space = "free_y"
  ) +
  # x axis
  scale_x_continuous(
    # add °C symbol
    labels = function(x) paste0(x, "°C"), 
    # use 5°C spacing
    breaks = -1:6*5  # also: seq(-5, 30, by = 5)
  ) +
  # y axis
  scale_y_continuous(
    # add a thousand separator
    labels = scales::label_comma(), 
    # use consistent spacing across rows
    breaks = 0:5*10000
  ) +
  # colors
  scale_color_manual(
    # use a custom color palette
    values = colors,
    # overwrite legend keys
    labels = c("Winter", "Spring", "Summer", "Autumn"),
    # adjust symbol size in legend size
    guide = guide_legend(override.aes = list(size = 4))
  ) +
  labs(
    # overwrite axis and legend titles
    x = "Average feels-like temperature", y = NULL, color = NULL,
    # add plot title and caption
    title = "Trends of Reported Bike Rents versus Feels-Like Temperature in London",
    caption = "Data: TfL (Transport for London), Jan 2015–Dec 2016"
  ) +
  # add theme with a custom font + larger element sizes
  theme_light(
    base_size = 15, base_family = "Spline Sans"
  ) +
  # theme adjustments
  theme(
    plot.title.position = "plot", # left-align title 
    plot.caption.position = "plot", # right-align caption
    legend.position = "top", # place legend above plot
    plot.title = element_text(face = "bold", size = rel(1.4)), # larger, bold title
    axis.text = element_text(family = "Spline Sans Mono"), # monospaced font for axes
    axis.title.x = element_text( # left-aligned, grey x axis label
      hjust = 0, color = "grey20", margin = margin(t = 12)
    ),
    legend.text = element_text(size = rel(1)), # larger legend labels
    strip.text = element_text(face = "bold", size = rel(1.15)), # larger, bold facet labels
    panel.grid.major.x = element_blank(), # no vertical major lines
    panel.grid.minor = element_blank(), # no minor grid lines
    panel.spacing.x = unit(20, "pt"), # increase white space between panels
    panel.spacing.y = unit(10, "pt"), # increase white space between panels
    plot.margin = margin(rep(15, 4)) # adjust white space around plot
  )

Saving Plots

Save the Graphic

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

ggsave("my_plot.png")

ggsave("my_plot.png", width = 6, height = 5, dpi = 600)

Plot Resolution

Plot Resolution

Save the Graphic

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

ggsave("my_plot.png")

ggsave("my_plot.png", width = 6, height = 5, dpi = 600)

ggsave("my_plot.png", width = 6*2.54, height = 5*2.54, unit = "cm", dpi = 600)

ggsave("my_plot.png", device = agg_png)

ggsave("my_plot.pdf", device = cairo_pdf)

Modified from canva.com

The {ragg} Package

provides drop-in replacements for the default raster graphic devices

• faster
• direct access to all system fonts
• advanced text rendering
  • including support for right-to-left text, emojis, and font fallback
• high quality anti-aliasing
• high quality rotated text
• supports 16-bit output
• system independent rendering

The {ragg} Package

Source: tidyverse.org/blog/2021/02/modern-text-features

The {ragg} Package

Source: tidyverse.org/blog/2021/02/modern-text-features

The {ragg} Package

Source: tidyverse.org/blog/2021/02/modern-text-features

The {ragg} Package

Source: tidyverse.org/blog/2021/02/modern-text-features

The {ragg} Package

• use {ragg} when saving ggplots by passing agg device function: ggsave(device = agg_png)) (used by default if installed)
• use {ragg} in the Rstudio Plots pane be setting the backend to AGG:
• use {ragg} when knitting Rmarkdown files by setting dev="ragg_png" in the code chunk options.

Save the Graphic

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

ggsave("my_plot.png")

ggsave("my_plot.png", width = 6, height = 5, dpi = 600)

ggsave("my_plot.png", width = 6*2.54, height = 5*2.54, unit = "cm", dpi = 600)

ggsave("my_plot.png", device = agg_png)

ggsave("my_plot.pdf", device = cairo_pdf)

ggsave("my_plot.svg")

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 and inspect it—go back to your IDE—repeat
  • tedious and time-consuming…
    
    
• Approach 2: use a qmd or rmd with inline output and chunk settings
  • set fig-width / fig.width and fig-height / fig.height
    per chunk or globally
    
    

Setting Plot Sizes in Quarto and Rmarkdown

Setting Plot Sizes in Quarto and Rmarkdown

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 and inspect it—go back to your IDE—repeat
  • tedious and time-consuming…
    
    
• Approach 2: use a qmd or rmd with inline output and chunk settings
  • set fig-width / fig.width and fig-height / 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 via {camcorder}

Setting Plot Sizes via {camcorder}

Setting Plot Sizes via {camcorder}

camcorder::gg_record(
  dir = here::here("temp"),  # path for plot files
  device = "png",            # device to use
  width = 10,                # figure width
  height = 5,                # figure height
  dpi = 600                  # plot resolution
)

g <- ggplot(bikes, aes(x = temp, y = count, color = day_night)) +
  geom_point(alpha = .3, size = 2) +
  scale_color_manual(values = c(day = "#FFA200", night = "#757BC7")) +
  theme_minimal(base_size = 14, base_family = "Asap SemiCondensed") +
  theme(panel.grid.minor = element_blank())

g

Setting Plot Sizes via {camcorder}

camcorder::gg_record(
  dir = here::here("temp"),  # path for plot files
  device = "png",            # device to use
  width = 10,                # figure width
  height = 5,                # figure height
  dpi = 600                  # plot resolution
)

g <- ggplot(bikes, aes(x = temp, y = count, color = day_night)) +
  geom_point(alpha = .3, size = 2) +
  scale_color_manual(values = c(day = "#FFA200", night = "#757BC7")) +
  theme_minimal(base_size = 14, base_family = "Asap SemiCondensed") +
  theme(panel.grid.minor = element_blank())

g

camcorder::gg_resize_film(width = 20) # update figure width

g

Like a Pro: Set Theme Globally

theme_set(theme_minimal(base_size = 14, base_family = "Asap SemiCondensed"))
theme_update(panel.grid.minor = element_blank())

Programming
with ggplot2

Conditional Components

smooth <- TRUE

ggplot(bikes, aes(x = temp, y = humidity)) +
  { if(smooth) geom_smooth(color = "red") } +
  geom_point(alpha = .5)

Conditional Components

smooth <- TRUE

ggplot(bikes, aes(x = temp, y = humidity)) +
  { if(smooth) geom_smooth(color = "red") } +
  geom_point(alpha = .5)

Conditional Components

smooth <- FALSE

ggplot(bikes, aes(x = temp, y = humidity)) +
  { if(smooth) geom_smooth(color = "red") } +
  geom_point(alpha = .5)

Wrapper Functions for Plots

draw_scatter <- function(smooth = TRUE) {
  ggplot(bikes, aes(x = temp, y = humidity)) +
    { if(smooth) geom_smooth(color = "red") } +
    geom_point(alpha = .5)
}

Wrapper Functions for Plots

draw_scatter()

Wrapper Functions for Plots

draw_scatter(smooth = FALSE)

Components as Functions

geom_scatterfit <- function(pointsize = 1, pointalpha = 1, 
                            method = "lm", linecolor = "red", ...) {
  list(
    geom_point(size = pointsize, alpha = pointalpha, ...),
    geom_smooth(method = method, color = linecolor, ...)
  )
}

Components as Functions

ggplot(bikes,
       aes(x = humidity, y = count)) +
  geom_scatterfit()

Components as Functions

ggplot(bikes,
       aes(x = humidity, y = count)) +
  geom_scatterfit(
    color = "#28A87D", 
    linewidth = 3
  )

Components as Functions

ggplot(diamonds, 
       aes(x = carat, y = price)) +
  geom_scatterfit(
    pointsize = .5, 
    pointalpha = .1,
    method = "gam",
    linecolor = "#EFAC00"
  )

Components as Functions

scales_log <- function(sides = "xy") {
  list(
    if(stringr::str_detect(sides, "x")) {
      scale_x_log10(
        breaks = c(10^(1:100)), labels = scales::label_log()
      )
    },
    if(stringr::str_detect(sides, "y")) {
      scale_y_log10(
        breaks = c(10^(1:100)), labels = scales::label_log()
      )
    }
  )
}

Components as Functions

ggplot(diamonds, 
       aes(x = carat, y = price)) +
  geom_scatterfit(
    pointsize = .5, 
    pointalpha = .1,
    method = "gam",
    linecolor = "#EFAC00"
  ) +
  scales_log(sides = "y")

Iterative Graphics

trends_monthly <- function(grp = "January") {
  bikes |> 
    dplyr::mutate(month = lubridate::month(date, label = TRUE, abbr = FALSE)) |> 
    dplyr::filter(month %in% grp) |> 
    ggplot(aes(x = temp, y = count, color = day_night)) +
    geom_point(alpha = .2, show.legend = FALSE) +
    geom_smooth(se = FALSE) +
    scale_color_manual(values = c("#FFA200", "#757bc7")) +
    labs(title = grp, x = "Temperature", y = "Bike shares", color = NULL)
}

Iterative Graphics

trends_monthly("July")

Iterative Graphics

trends_monthly <- function(grp = "January") {
  bikes |> 
    dplyr::mutate(month = lubridate::month(date, label = TRUE, abbr = FALSE)) |> 
    dplyr::filter(month %in% grp) |> 
    ggplot(aes(x = temp, y = count, color = day_night)) +
    geom_point(alpha = .2, show.legend = FALSE) +
    geom_smooth(se = FALSE) +
    # keep axis ranges consistent
    scale_x_continuous(limits = range(bikes$temp)) +
    scale_y_continuous(limits = range(bikes$count)) +
    scale_color_manual(values = c("#FFA200", "#757bc7")) +
    labs(title = grp, x = "Temperature", y = "Bike shares", color = NULL)
}

Iterative Graphics

trends_monthly("July")

Iterative Graphics

plots <- purrr::map(month.name[1:12], trends_monthly) ## also: ~ trends_monthly(.x)

Iterative Graphics

plots <- purrr::map(month.name[1:12], trends_monthly) ## also: ~ trends_monthly(.x)
plots[[9]]

Iterative Graphics

plots <- purrr::map(month.name[1:12], trends_monthly) ## also: ~ trends_monthly(.x)
patchwork::wrap_plots(plots)

Iterative Graphics

plot_density <- function(data, var, grp = "") {
  ggplot(data, aes(x = !!sym(var))) +
    geom_density(aes(fill = !!sym(grp)), position = "identity",
                 color = "grey30", alpha = .3) +
    coord_cartesian(expand = FALSE, clip = "off") +
    scale_y_continuous(labels = scales::label_number()) +
    scale_fill_brewer(palette = "Dark2", name = NULL) +
    theme(legend.position = "top")
}

Iterative Graphics

plot_density(
  bikes, "count"
)

Iterative Graphics

plots <- purrr::map(
  c("count", "temp", "humidity", "wind_speed"), 
  ~ plot_density(data = bikes, var = .x, grp = "day_night")
)
patchwork::wrap_plots(plots, nrow = 1)

Iterative Graphics

plots <- purrr::map(
  names(dplyr::select(midwest, where(is.numeric))),
  ~plot_density(data = midwest, var = .x)
)
patchwork::wrap_plots(plots)

Combine Plots

Combine Plots with {patchwork}

library(patchwork)

p1 <- plot_density(data = bikes, var = "count", grp = "day_night")

p2 <- plot_density(data = bikes, var = "humidity", grp = "day_night")

p3 <- ggplot(bikes, aes(x = humidity, y = count)) + geom_scatterfit(pointalpha = .3)

Combine Plots with {patchwork}

(p1 + p2) / p3

Combine Plots with {patchwork}

(p1 + p2) / p3 + plot_layout(heights = c(1, 2))

Combine Plots with {patchwork}

(p1 + p2) / p3 + plot_layout(heights = c(1, 2), guides = "collect")

Combine Plots with {patchwork}

(p1 + p2) / p3 + plot_layout(heights = c(1, 2), guides = "collect") +
  plot_annotation(theme = theme(legend.justification = "top"))

Combine Plots with {patchwork}

(p1 + p2) / p3 + plot_layout(heights = c(1, 2), guides = "collect") +
  plot_annotation(tag_levels = "A", tag_suffix = ".", theme = theme(legend.justification = "top"))

Exciting Extension Packages

Layers

• {geofacet} — tile grid maps
• {ggalluvial} — alluvial plots
• {ggalt} — dumbbell, horizon, and lollipop charts, splines, …
• {ggbeeswarm} — beeswarm plots and variants
• {ggbraid} — ribbons for alternating groups
• {ggbump} — parallel sets, pie charts, geometries, splines, voronoi, …
• {ggdensity} — improved density plots
• {ggdist} — uncertainty visualizations
• {ggforce} — several interesting layers (and more)
• {ggpattern} — pattern fills for layers

Layers (continued)

• {ggpointdensity} — density gradients for scatter plots
• {ggraph} — networks, graphs & trees
• {ggridges} — ridgeline plots
• {ggsankey} — sankey diagrams
• {ggsignif} — significance levels
• {ggstar} — more point shapes
• {ggstream} — stream graphs
• {ggupset} — upset graphs
• {treemapify} — treemaps

Utilities

• {cowplot} — combine ggplots
• {ggannotate} — point-n-click annotations
• {ggblend} — blend, compose, adjust layers
• {ggfittext} — scale text according to space
• {ggfx} — shaders and filters for layers
• {ggh4x} — facets, positions, and more
• {ggtext} — text rendering for theme elements + text layers
• {lemon} — axis lines (and a few layers)
• {patchwork} — combine ggplots
• {scales} — control scales

Themes

• {ggdark}
• {ggsci} (also color scales)
• {ggtech} (also color scales)
• {ggthemes} (also color scales)
• {ggthemr}
• {hrbrthemes} (also color scales)
• {tvthemes} (also color scales)

Color Palettes

• {colorspace}
• {jcolors}
• {MetBrewer}
• {nord}
• {rcartocolor}
• {RColorBrewer}
• {scico}
• {unikn}
• {viridis}
• {wesanderson}

Interactive Charts

• {ggiraph}
• {plotly}
• {echarts4r}*
• {highcharter}*
• {charter}*
• {streamgraph}*
• {tmap}*
• {leaflet}*
• {globe4r}*
• {grapher}*

* not using ggplot2

Recap

• a basic ggplot is build by specifying three components:
  data, aesthetics and a layer (usually a geom_* or stat_*)
• aesthetic mappings define how variables map to visual properties
• the default appearance of all other components can be modified via scale_*, coord_*, facet_* and theme_* / theme
• use the devices cairo (pdf) and agg (png, jpg, tiff) when saving plots
• find a suitable plot size by setting figure chunk options in qmd/rmd files or with the help of the {camcorder} package
• define conditional components, custom layers and functions to generate plots more efficiently and to iterate over multiple inputs
• combine multiple plot outputs with {patchwork}

Exercises

Exercise 1

• Discuss / investigate with your neighbor:
  • What are the differences between geom_line() and geom_path()?
  • Why can you use geom_smooth() and stat_smooth() interchangeably?
  • What are the three ways to remove a legend from a ggplot?

Exercise 2

• Explore the TfL bike share data visually:
  • Create a time series of counts per day and night.
  • Draw box and whisker plots of average temperatures per month.
  • Visualize bike counts per weather type and period as bar chart.
• Combine the three plots with {patchwork}.
• Export the final graphic in a format of your choice.