数据可视化

《区域水环境污染数据分析实践》
Data analysis practice of regional water environment pollution

mingsu@rcees.ac.cn

中国科学院大学资源与环境学院
中国科学院生态环境研究中心

2026-05-21

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}

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.
Coordinate System	`coord_*()`	Maps data into the plane of the data rectangle.
Facets	`facet_*()`	The arrangement of the data into a grid of plots.
Visual Themes	`theme() / 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

bikes <- readr::read_csv(
  "../../data/ggplot2/london-bikes-custom.csv",
  ## or: "https://raw.githubusercontent.com/z3tt/graphic-design-ggplot2/main/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

`ggplot2::ggplot()`

Data

ggplot(data = bikes)

Aesthetic Mapping（视觉映射）：`aes(.)`

= link variables to graphical properties

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

Aesthetic Mapping（视觉映射）：`aes(.)`

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

aesthetics

aes() outside as component

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

aes() inside, explicit matching

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

aes() inside, implicit matching

ggplot(bikes, aes(temp_feel, count))

aes() inside, mixed matching

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

Geometrical Layers

Geometries（几何图层）：geom_*

= interpret aesthetics as graphical representations

points
lines
polygons
text labels
…

Geometries（几何图层）：geom_*

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

Visual Properties of Layers（图层属性）

ggplot(
  bikes,
  aes(x = temp_feel, y = count)
) +
  geom_point(
    color = "#28a87d",
    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 = "#28a87d",
    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
  )

Filter Data

ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = temp, y = temp_feel)
) +
  geom_point(
    aes(color = weather_type == "clear", size = count),
    shape = 18,
    alpha = .5
  )

Filter Data

ggplot(
  bikes %>% filter(!is.na(weather_type)),
  aes(x = temp, y = temp_feel)
) +
  geom_point(
    aes(color = weather_type == "clear", size = count),
    shape = 18,
    alpha = .5
  )

Local vs. Global（应用至当前图层或所有图层）

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"
  )

Statistical Layers

`stat_()` and `geom_()`

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

`stat_()` and `geom_()`

ggplot(bikes, aes(x = season)) +
  stat_count(geom = "bar")
ggplot(bikes, aes(x = season)) +
  geom_bar(stat = "count")

`stat_()` and `geom_()`

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

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(
    fun = mean,
    fun.max = function(y) mean(y) + sd(y),
    fun.min = function(y) mean(y) - sd(y)
  )

Extending a ggplot

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] "ggplot2::ggplot" "ggplot"          "ggplot2::gg"     "S7_object"      
[5] "gg"

Inspect a ggplot Object

g$data

# A tibble: 1,454 × 14
   date       day_night year  month season count is_workday is_weekend
   <date>     <chr>     <fct> <fct> <fct>  <int> <lgl>      <lgl>     
 1 2015-01-04 day       2015  1     winter  6830 FALSE      TRUE      
 2 2015-01-04 night     2015  1     winter  2404 FALSE      TRUE      
 3 2015-01-05 day       2015  1     winter 14763 TRUE       FALSE     
 4 2015-01-05 night     2015  1     winter  5609 TRUE       FALSE     
 5 2015-01-06 day       2015  1     winter 14501 TRUE       FALSE     
 6 2015-01-06 night     2015  1     winter  6112 TRUE       FALSE     
 7 2015-01-07 day       2015  1     winter 16358 TRUE       FALSE     
 8 2015-01-07 night     2015  1     winter  4706 TRUE       FALSE     
 9 2015-01-08 day       2015  1     winter  9971 TRUE       FALSE     
10 2015-01-08 night     2015  1     winter  5630 TRUE       FALSE     
# ℹ 1,444 more rows
# ℹ 6 more variables: is_holiday <lgl>, temp <dbl>, temp_feel <dbl>,
#   humidity <dbl>, wind_speed <dbl>, weather_type <chr>

Inspect a ggplot Object

g$mapping

Aesthetic mapping: 
* `x`      -> `temp_feel`
* `y`      -> `count`
* `colour` -> `season`
* `group`  -> `day_night`

Extend a ggplot Object: Add Layers

g +
  geom_rug(
    alpha = .2
  )

Remove a Layer from the Legend

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

Extend a ggplot Object: Add Labels

g +
  xlab("Feels-like temperature (°F)") +
  ylab("Reported bike shares") +
  ggtitle("TfL bike sharing trends")

Extend a ggplot Object: Add Labels

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

Extend a ggplot Object: Add Labels

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

g

Extend a ggplot Object: Add Labels

g +
  labs(
    x = "Feels-like temperature (°F)",
    y = "Reported bike shares",
    title = "TfL bike sharing trends",
    subtitle = "Reported bike rents versus feels-like temperature in London",
    caption = "Data: TfL",
    color = "Season:",
    tag = "Fig. 1"
  )

Extend a ggplot Object: Add Labels

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

Extend a ggplot Object: Themes

g + theme_light()
g + theme_minimal()

Change the Theme Base Settings

g +
  theme_light(
    base_size = 14
  )

Set a Theme Globally

theme_set(theme_light())

g

Change the Theme Base Settings

theme_set(theme_light(
  base_size = 14
))

g

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

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)

grDevices::cairo_pdf("my_plot.pdf", width = 10, height = 7)
g
dev.off()

A comparison of vector and raster graphics. — Modified from canva.com

Setup

g <-
  ggplot(
    bikes,
    aes(x = temp_feel, y = count, color = season)
  ) +
  geom_point(
    alpha = .3,
    guide = "none"
  )

g

Diamonds Facet

ggplot(
  diamonds,
  aes(x = carat, y = price)
) +
  geom_point(
    alpha = .3
  ) +
  geom_smooth(
    method = "lm",
    se = FALSE,
    color = "dodgerblue"
  ) +
  facet_grid(
    cut ~ clarity,
    space = "free_x",
    scales = "free_x"
  )

Diamonds Facet (Dark Theme Bonus)

ggplot(
  diamonds,
  aes(x = carat, y = price)
) +
  geom_point(
    alpha = .3,
    color = "white"
  ) +
  geom_smooth(
    method = "lm",
    se = FALSE,
    color = "dodgerblue"
  ) +
  facet_grid(
    cut ~ clarity,
    space = "free_x",
    scales = "free_x"
  ) +
  theme_dark(
    base_size = 14
  )

Scales（尺度）

Scales

= translate between variable ranges 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_*()

Scales

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

The extensions (*) can be filled by e.g.:

continuous(), discrete(), reverse(), log10(), sqrt(), date() for positions
continuous(), discrete(), manual(), gradient(), gradient2(), brewer() for colors
continuous(), discrete(), manual(), ordinal(), area(), date() for sizes
continuous(), discrete(), manual(), ordinal() for shapes
continuous(), discrete(), manual(), ordinal(), date() for transparency

Allison Horsts illustration ofthe correct use of continuous versus discrete; however, in {ggplot2} these are interpeted in a different way: as quantitative and qualitative. — 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 = date, y = count, color = season)
) +
  geom_point()

Aesthetics + Scales

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

Scales

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

Scales

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_x_continuous() +
  scale_y_log10() +
  scale_color_discrete()

Scales

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_x_continuous() +
  scale_y_log10() +
  scale_color_viridis_d()

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    trans = "log10"
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares"
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = seq(0, 60000, by = 15000)
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = 0:4 * 15000
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = c(0, 2:12 * 2500, 40000, 50000)
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares in thousands",
    breaks = 0:4 * 15000,
    labels = 0:4 * 15
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares in thousands",
    breaks = 0:4 * 15000,
    labels = paste(0:4 * 15000, "bikes")
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = 0:4 * 15000,
    limits = c(NA, 60000)
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = 0:4 * 15000,
    expand = c(0, 0)
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = -1:5 * 15000,
    expand = c(.5, .5) ## c(add, mult)
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = -1:5 * 15000,
    expand = expansion(add = 2000)
  )

`scale_x|y_continuous`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_y_continuous(
    name = "Reported bike shares",
    breaks = 0:4 * 15000,
    guide = "none"
  )

`scale_x|y_date`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_x_date(
    name = NULL,
    date_breaks = "4 months"
  )

`scale_x|y_date`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_x_date(
    name = NULL,
    date_breaks = "20 weeks"
  )

`scale_x|y_date` with `strftime()`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_x_date(
    name = NULL,
    date_breaks = "6 months",
    date_labels = "%Y/%m/%d"
  )

`scale_x|y_date` with `strftime()`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_x_date(
    name = NULL,
    date_breaks = "6 months",
    date_labels = "%b '%y"
  )

`scale_x|y_discrete`

ggplot(
  bikes,
  aes(x = season, y = count)
) +
  geom_boxplot() +
  scale_x_discrete(
    name = "Period",
    labels = c("Dec-Feb", "Mar-May", "Jun-Aug", "Sep-Nov")
  )

`scale_x|y_discrete`

ggplot(
  bikes,
  aes(x = season, y = count)
) +
  geom_boxplot() +
  scale_x_discrete(
    name = "Season",
    expand = c(.5, 0) ## add, mult
  )

Discrete or Continuous?

ggplot(
  bikes,
  aes(x = as.numeric(season), y = count)
) +
  geom_boxplot(
    aes(group = season)
  )

Discrete or Continuous?

ggplot(
  bikes,
  aes(x = as.numeric(season), y = count)
) +
  geom_boxplot(
    aes(group = season)
  ) +
  scale_x_continuous(
    name = "Season",
    breaks = 1:4,
    labels = levels(bikes$season)
  )

Discrete or Continuous?

ggplot(
  bikes,
  aes(
    x = as.numeric(season) +
      as.numeric(season) / 8,
    y = count
  )
) +
  geom_boxplot(
    aes(group = season)
  ) +
  scale_x_continuous(
    name = "Season",
    breaks = 1:4,
    labels = levels(bikes$season)
  )

`scale_color|fill_discrete`

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_color_discrete(
    name = "Season:",
    type = c("#69b0d4", "#00CB79", "#F7B01B", "#a78f5f")
  )

Inspect Assigned Colors

g <- ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_color_discrete(
    name = "Season:",
    type = c("#3ca7d9", "#1ec99b", "#F7B01B", "#bb7e8f")
  )

gb <- ggplot_build(g)

gb$data[[1]][c(1:5, 200:205, 400:405), 1:5]

        x     y  colour PANEL group
1   16439  6830 #3ca7d9     1     1
2   16439  2404 #3ca7d9     1     1
3   16440 14763 #3ca7d9     1     1
4   16440  5609 #3ca7d9     1     1
5   16441 14501 #3ca7d9     1     1
200 16538  8830 #1ec99b     1     2
201 16539 24019 #1ec99b     1     2
202 16539 10500 #1ec99b     1     2
203 16540 25640 #1ec99b     1     2
204 16540 11830 #1ec99b     1     2
205 16541 22216 #1ec99b     1     2
400 16638 12079 #F7B01B     1     3
401 16639 26646 #F7B01B     1     3
402 16639 12446 #F7B01B     1     3
403 16640 11312 #F7B01B     1     3
404 16640  4722 #F7B01B     1     3
405 16641 22748 #F7B01B     1     3

`scale_color|fill_discrete`

my_colors <- c(
  `winter` = "#3c89d9",
  `spring` = "#1ec99b",
  `summer` = "#F7B01B",
  `autumn` = "#a26e7c"
)

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_color_discrete(
    name = "Season:",
    type = my_colors
  )

`scale_color|fill_discrete`

my_colors_alphabetical <- c(
  `autumn` = "#a26e7c",
  `spring` = "#1ec99b",
  `summer` = "#F7B01B",
  `winter` = "#3c89d9"
)

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_color_discrete(
    name = "Season:",
    type = my_colors_alphabetical
  )

`scale_color|fill_discrete`

library(RColorBrewer)

ggplot(
  bikes,
  aes(x = date, y = count, color = season)
) +
  geom_point() +
  scale_color_discrete(
    name = "Season:",
    type = brewer.pal(
      n = 4,
      name = "Dark2"
    )
  )

`scale_color|fill_manual`

ggplot(
  bikes,
  aes(x = date, y = count, color = weather_type)
) +
  geom_point() +
  scale_color_manual(
    name = "Season:",
    values = brewer.pal(n = 6, name = "Pastel1"),
    na.value = "black"
  )

Diamonds Facet

facet <-
  ggplot(
    diamonds,
    aes(x = carat, y = price)
  ) +
  geom_point(
    alpha = .3
  ) +
  geom_smooth(
    aes(color = cut),
    method = "lm",
    se = FALSE
  ) +
  facet_grid(
    cut ~ clarity,
    space = "free_x",
    scales = "free_x"
  )

facet

Diamonds Facet

facet +
  scale_x_continuous(
    breaks = 0:5
  ) +
  scale_y_continuous(
    limits = c(0, 30000),
    breaks = 0:3 * 10000,
    labels = c("$0", "$10,000", "$20,000", "$30,000")
  )

Diamonds Facet

facet +
  scale_x_continuous(
    breaks = 0:5
  ) +
  scale_y_continuous(
    limits = c(0, 30000),
    breaks = 0:3 * 10000,
    labels = paste0(
      "$",
      format(
        0:3 * 10000,
        big.mark = ",",
        trim = TRUE
      )
    )
  )

Diamonds Facet

facet +
  scale_x_continuous(
    breaks = 0:5
  ) +
  scale_y_continuous(
    limits = c(0, 30000),
    breaks = 0:3 * 10000,
    labels = function(y)
      paste0(
        "$",
        format(
          y,
          big.mark = ",",
          trim = TRUE
        )
      )
  )

Diamonds Facet

facet +
  scale_x_continuous(
    breaks = 0:5
  ) +
  scale_y_continuous(
    limits = c(0, 30000),
    breaks = 0:3 * 10000,
    labels = scales::dollar_format()
  ) +
  scale_color_brewer(
    palette = "Set2",
    guide = "none"
  )

Diamonds Facet

facet +
  scale_x_continuous(
    breaks = 0:5
  ) +
  scale_y_continuous(
    limits = c(0, 30000),
    breaks = 0:3 * 10000,
    labels = scales::dollar_format()
  ) +
  scale_color_brewer(
    palette = "Set2"
  ) +
  theme(
    legend.position = "none"
  )

Coordinate Systems（投影）

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"
  )

Clipping

ggplot(
  filter(bikes, is_holiday == TRUE),
  aes(x = temp_feel, y = count)
) +
  geom_point() +
  geom_text(
    aes(label = season),
    nudge_x = .3,
    hjust = 0
  ) +
  coord_cartesian(
    clip = "off"
  )

… or better use {ggrepel}

ggplot(
  filter(bikes, is_holiday == TRUE),
  aes(x = temp_feel, y = count)
) +
  geom_point() +
  ggrepel::geom_text_repel(
    aes(label = season),
    nudge_x = .3,
    hjust = 0
  ) +
  coord_cartesian(
    clip = "off"
  )

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!

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

Reminder: Sort Your Bars!

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

Circular Corrdinate System

ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = weather_type, fill = weather_type)
) +
  geom_bar() +
  coord_polar()
ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = weather_type, fill = weather_type)
) +
  geom_bar() +
  coord_cartesian()

Circular Cordinate System

ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = fct_infreq(weather_type), fill = weather_type)
) +
  geom_bar(width = 1) +
  coord_polar()
ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = fct_infreq(weather_type), fill = weather_type)
) +
  geom_bar(width = 1) +
  coord_cartesian()

Circular Corrdinate System

ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = fct_infreq(weather_type), fill = weather_type)
) +
  geom_bar() +
  coord_polar(theta = "x")
ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = fct_infreq(weather_type), fill = weather_type)
) +
  geom_bar() +
  coord_polar(theta = "y")

Circular Corrdinate System

ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = 1, fill = weather_type)
) +
  geom_bar(position = "stack") +
  coord_polar(theta = "y")
ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = 1, fill = weather_type)
) +
  geom_bar(position = "stack") +
  coord_cartesian()

Circular Corrdinate System

ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = 1, fill = fct_rev(fct_infreq(weather_type)))
) +
  geom_bar(position = "stack") +
  coord_polar(theta = "y") +
  scale_fill_discrete(name = NULL)
ggplot(
  filter(bikes, !is.na(weather_type)),
  aes(x = 1, fill = fct_rev(fct_infreq(weather_type)))
) +
  geom_bar(position = "stack") +
  coord_cartesian() +
  scale_fill_discrete(name = NULL)

Transform a Coordinate System

ggplot(
  bikes,
  aes(x = temp, y = count)
) +
  geom_point() +
  coord_trans(y = "log10")

Transform a Coordinate System

ggplot(
  bikes,
  aes(x = temp, y = count, group = day_night)
) +
  geom_point() +
  geom_smooth(method = "lm") +
  coord_trans(y = "log10")
ggplot(
  bikes,
  aes(x = temp, y = count, group = day_night)
) +
  geom_point() +
  geom_smooth(method = "lm") +
  scale_y_log10()

图形组合

Allison Horsts monster illustration of the patchwork extension package. — Illustration by Allison Horst

Graphic
Code

theme_std <- theme_set(theme_minimal(base_size = 18))
theme_update(
  # text = element_text(family = "Pally"),
  panel.grid = element_blank(),
  axis.text = element_text(color = "grey50", size = 12),
  axis.title = element_text(color = "grey40", face = "bold"),
  axis.title.x = element_text(margin = margin(t = 12)),
  axis.title.y = element_text(margin = margin(r = 12)),
  axis.line = element_line(color = "grey80", size = .4),
  legend.text = element_text(color = "grey50", size = 12),
  plot.tag = element_text(size = 40, margin = margin(b = 15)),
  plot.background = element_rect(fill = "white", color = "white")
)

bikes_sorted <-
  bikes %>%
  filter(!is.na(weather_type)) %>%
  group_by(weather_type) %>%
  mutate(sum = sum(count)) %>%
  ungroup() %>%
  mutate(
    weather_type = forcats::fct_reorder(
      str_to_title(str_wrap(weather_type, 5)),
      sum
    )
  )

p1 <- ggplot(
  bikes_sorted,
  aes(x = weather_type, y = count, color = weather_type)
) +
  geom_hline(yintercept = 0, color = "grey80", size = .4) +
  stat_summary(
    geom = "point",
    fun = "sum",
    size = 12
  ) +
  stat_summary(
    geom = "linerange",
    ymin = 0,
    fun.max = function(y) sum(y),
    size = 2,
    show.legend = FALSE
  ) +
  coord_flip(ylim = c(0, NA), clip = "off") +
  scale_y_continuous(
    expand = c(0, 0),
    limits = c(0, 8500000),
    labels = scales::comma_format(scale = .0001, suffix = "K")
  ) +
  scale_color_viridis_d(
    option = "magma",
    direction = -1,
    begin = .1,
    end = .9,
    name = NULL,
    guide = guide_legend(override.aes = list(size = 7))
  ) +
  labs(
    x = NULL,
    y = "Sum of reported bike shares",
    tag = "P1",
  ) +
  theme(
    axis.line.y = element_blank(),
    axis.text.y = element_text(
      family = "Pally",
      color = "grey50",
      face = "bold",
      margin = margin(r = 15),
      lineheight = .9
    )
  )

p1

Graphic
Code

p2 <- bikes_sorted %>%
  filter(season == "winter", is_weekend == TRUE, day_night == "night") %>%
  group_by(weather_type, .drop = FALSE) %>%
  mutate(id = row_number()) %>%
  ggplot(
    aes(x = weather_type, y = id, color = weather_type)
  ) +
  geom_point(size = 4.5) +
  scale_color_viridis_d(
    option = "magma",
    direction = -1,
    begin = .1,
    end = .9,
    name = NULL,
    guide = guide_legend(override.aes = list(size = 7))
  ) +
  labs(
    x = NULL,
    y = "Reported bike shares on\nweekend winter nights",
    tag = "P2",
  ) +
  coord_cartesian(ylim = c(.5, NA), clip = "off")

p2

Graphic
Code

my_colors <- c("#cc0000", "#000080")

p3 <- bikes %>%
  group_by(week = lubridate::week(date), day_night, year) %>%
  summarize(count = sum(count)) %>%
  group_by(week, day_night) %>%
  mutate(avg = mean(count)) %>%
  ggplot(aes(x = week, y = count, group = interaction(day_night, year))) +
  geom_line(color = "grey65", size = 1) +
  geom_line(aes(y = avg, color = day_night), stat = "unique", size = 1.7) +
  annotate(
    geom = "text",
    label = c("Day", "Night"),
    color = my_colors,
    x = c(5, 18),
    y = c(125000, 29000),
    size = 8,
    fontface = "bold",
    family = "Pally"
  ) +
  scale_x_continuous(breaks = c(1, 1:10 * 5)) +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_color_manual(values = my_colors, guide = "none") +
  labs(
    x = "Week of the Year",
    y = "Reported bike shares\n(cumulative # per week)",
    tag = "P3",
  )

p3

{patchwork}

# install.packages("patchwork")
require(patchwork)
(p1 + p2) / p3

“Collect Guides”

(p1 + p2) / p3 + plot_layout(guides = "collect")

Apply Theming

((p1 + p2) / p3 & theme(legend.justification = "top")) +
  plot_layout(guides = "collect")

Apply Theming

(p1 + p2) /
  p3 &
  theme(
    legend.position = "none",
    plot.background = element_rect(color = "black", size = 3)
  )

Adjust Widths and Heights

((p1 + p2) / p3 & theme(legend.position = "none")) +
  plot_layout(heights = c(.2, .1), widths = c(2, 1))

Use A Custom Layout

picasso <- "
AAAAAA#BBBB
CCCCCCCCC##
CCCCCCCCC##"
(p1 + p2 + p3 & theme(legend.position = "none")) +
  plot_layout(design = picasso)

Add Labels

pl1 <- p1 +
  labs(tag = NULL, title = "Plot One") +
  theme(legend.position = "none")
pl2 <- p2 +
  labs(tag = NULL, title = "Plot Two") +
  theme(legend.position = "none")
pl3 <- p3 +
  labs(tag = NULL, title = "Plot Three") +
  theme(legend.position = "none")

Add Labels

(pl1 + pl2) /
  pl3 +
  plot_annotation(
    tag_levels = "1",
    tag_prefix = "P",
    title = "An overarching title for all 3 plots, placed on the very top while all other titles are sitting below the tags."
  )

text <- tibble::tibble(
  x = 0,
  y = 0,
  label = "Lorem ipsum dolor sit amet, **consectetur adipiscing elit**, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation <b style='color:#000080;'>ullamco laboris nisi</b> ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat <b style='color:#cc0000;'>cupidatat non proident</b>, sunt in culpa qui officia deserunt mollit anim id est laborum."
)
pt <- ggplot(text, aes(x = x, y = y)) +
  ggtext::geom_textbox(
    aes(label = label),
    box.color = NA,
    width = unit(23, "lines"),
    color = "grey40",
    size = 6.5,
    lineheight = 1.4
  ) +
  coord_cartesian(expand = FALSE, clip = "off") +
  theme_void()
pt

Add Text

(p1 + pt) / p3

Add Inset Plots

pl1 + inset_element(pl2, l = .6, b = .1, r = 1, t = .6)

Add Inset Plots

pl1 + inset_element(pl2, l = .6, b = 0, r = 1, t = .5, align_to = 'full')

Add Inset Plots

(pl1 + inset_element(pl2, l = .6, b = .1, r = 1, t = .6) + pt) / pl3

练习

library(palmerpenguins)
library(ggthemes)
penguins

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

效果

练习

Code
Graphic

p <- ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point(aes(color = species, shape = species)) +
  geom_smooth(method = "lm") +
  labs(
    title = "Body mass and flipper length",
    subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
    x = "Flipper length (mm)",
    y = "Body mass (g)",
    color = "Species",
    shape = "Species"
  ) +
  scale_color_colorblind()

练习

Code
Graphic

p <- ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point(aes(color = bill_depth_mm)) +
  geom_smooth()

练习

Code
Graphic

p <- ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = island)
) +
  geom_point() +
  geom_smooth(se = FALSE)

分层展示

Code
Graphic

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

柱状图

Code
Graphic

p <- ggplot(penguins, aes(x = species)) +
  geom_bar()

柱状图

Code
Graphic

p <- ggplot(penguins, aes(x = fct_infreq(species))) +
  geom_bar()

直方图

Code
Graphic

p <- ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 200)

直方图

Code
Graphic

p1 <- ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 20)
p2 <- ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(binwidth = 2000)
p <- p1 + p2

密度图

Code
Graphic

p <- ggplot(penguins, aes(x = body_mass_g)) +
  geom_density()

箱图

Code
Graphic

p <- ggplot(penguins, aes(x = species, y = body_mass_g)) +
  geom_boxplot()

分组

Code
Graphic

p <- ggplot(penguins, aes(x = body_mass_g, color = species)) +
  geom_density(linewidth = 0.75)

分组

Code
Graphic

p <- ggplot(penguins, aes(x = body_mass_g, color = species, fill = species)) +
  geom_density(alpha = 0.5)

分组

Code
Graphic

p <- ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar()

分组

Code
Graphic

p <- ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar(position = "fill")

分组

Code
Graphic

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

分组

Code
Graphic

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

分面

Code
Graphic

p <- ggplot(penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point(aes(color = species, shape = species)) +
  facet_wrap(~island)

分面

Code
Graphic

p <- ggplot(
  data = penguins,
  mapping = aes(
    x = bill_length_mm,
    y = bill_depth_mm,
    color = species,
    shape = species
  )
) +
  geom_point() +
  labs(color = "Species")

练习

Code
Graphic

p1 <- ggplot(penguins, aes(x = island, fill = species)) +
  geom_bar(position = "fill")
p2 <- ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar(position = "fill")
p <- p1 + p2

练习

Code
Graphic

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

练习

Code
Graphic

p <- ggplot(mpg, aes(x = class)) +
  geom_bar()

练习

Code
Graphic

p <- ggplot(mpg, aes(x = cty, y = hwy)) +
  geom_point()

练习

Code
Graphic

p <- ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy))

欢迎讨论！

#| results: ‘asis’ #| echo: false rmdify::slideend( wechat = FALSE, type = “public”, tel = FALSE, thislink = “../” ) ` ``

数据可视化

The ggplot2 Package

The Grammar of {ggplot2}

The Grammar of {ggplot2}

The Grammar of {ggplot2}

The Data

ggplot2::ggplot()

Data

Aesthetic Mapping（视觉映射）：aes(.)

Aesthetic Mapping（视觉映射）：aes(.)

aesthetics

Geometrical Layers

Geometries（几何图层）：geom_*

Geometries（几何图层）：geom_*

Visual Properties of Layers（图层属性）

Setting vs Mapping of Visual Properties

Mapping Expressions

Filter Data

Filter Data

Local vs. Global（应用至当前图层或所有图层）

Adding More Layers

Global Color Encoding

Local Color Encoding

The `group` Aesthetic

Set Both as Global Aesthetics

Overwrite Global Aesthetics

Statistical Layers

`stat_*()` and `geom_*()`

`stat_*()` and `geom_*()`

`stat_*()` and `geom_*()`

Statistical Summaries

Statistical Summaries

Statistical Summaries

Statistical Summaries

Extending a ggplot

Store a ggplot as Object

Inspect a ggplot Object

Inspect a ggplot Object

Extend a ggplot Object: Add Layers

Remove a Layer from the Legend

Extend a ggplot Object: Add Labels

Extend a ggplot Object: Add Labels

Extend a ggplot Object: Add Labels

Extend a ggplot Object: Add Labels

Extend a ggplot Object: Add Labels

Extend a ggplot Object: Themes

Change the Theme Base Settings

Set a Theme Globally

Change the Theme Base Settings

Overwrite Specific Theme Settings

Overwrite Specific Theme Settings

Overwrite Specific Theme Settings

Overwrite Specific Theme Settings

Overwrite Specific Theme Settings

Overwrite Theme Settings Globally

Save the Graphic

Facets（面）

Facets（面）

Setup

Wrapped Facets

Wrapped Facets

Facet Multiple Variables

Facet Options: Cols + Rows

Facet Options: Free Scaling

Facet Options: Free Scaling

Facet Options: Switch Labels

Gridded Facets

Gridded Facets

Facet Multiple Variables

Facet Options: Free Scaling

Facet Options: Switch Labels

Facet Options: Proportional Spacing

Facet Options: Proportional Spacing

Diamonds Facet

Diamonds Facet

Diamonds Facet (Dark Theme Bonus)

Scales（尺度）

Scales

Scales

Scales

`ggplot2::ggplot()`

Aesthetic Mapping（视觉映射）：`aes(.)`

Aesthetic Mapping（视觉映射）：`aes(.)`

`stat_()` and `geom_()`

`stat_()` and `geom_()`

`stat_()` and `geom_()`