The package

{ggplot2} is a data visualization package for the statistical programming language R
created by Hadley Wickham in 2005.

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

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

The data

We use data from the National Morbidity and Mortality Air Pollution Study (NMMAPS),
filtered for the city of Chicago and the timespan January 1997 to December 2000.

chic <- readr::read_csv("https://raw.githubusercontent.com/z3tt/ggplot-courses/main/data/chicago-nmmaps-custom.csv")
chic$year <- factor(chic$year, levels = as.character(1997:2000))
tibble::glimpse(chic)

## Observations: 1,461
## Variables: 10
## $ city     <chr> "chic", "chic", "chic", "chic", "chic", "chic", "chic", "c...
## $ date     <date> 1997-01-01, 1997-01-02, 1997-01-03, 1997-01-04, 1997-01-0...
## $ death    <dbl> 137, 123, 127, 146, 102, 127, 116, 118, 148, 121, 110, 127...
## $ temp     <dbl> 36.0, 45.0, 40.0, 51.5, 27.0, 17.0, 16.0, 19.0, 26.0, 16.0...
## $ dewpoint <dbl> 37.500, 47.250, 38.000, 45.500, 11.250, 5.750, 7.000, 17.7...
## $ pm10     <dbl> 13.052268, 41.948600, 27.041751, 25.072573, 15.343121, 9.3...
## $ o3       <dbl> 5.659256, 5.525417, 6.288548, 7.537758, 20.760798, 14.9408...
## $ time     <dbl> 3654, 3655, 3656, 3657, 3658, 3659, 3660, 3661, 3662, 3663...
## $ season   <fct> Winter, Winter, Winter, Winter, Winter, Winter, Winter, Wi...
## $ year     <fct> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997...

For more details about the data set check Roger Peng's book Statistical Methods in Environmental Epidemiology with R.

The Structure of {ggplot2}

1. Data → The raw data that you want to visualise

2. Layers: geom_ and stat_ → The geometric shapes and statistical summaries representing the data

3. Aesthetics aes() → Aesthetic mappings of the geometric and statistical objects

4. Scales scale_ → Maps between the data and the aesthetic dimensions

5. Coordinate system coord_ → Maps data into the plane of the data rectangle

6. Facets facet_ → The arrangement of the data into a grid of plots

7. Visual themes theme() and theme_ → The overall visual defaults of a plot

1. Data: ggplot()

We need to specify data and the two variables we want to plot as aestethics of the ggplot() call:

ggplot(data = chic,
       mapping =
         aes(
          x = date,
          y = temp
        )
      )

1. Data: ggplot()

We need to specify data and the two variables we want to plot as aestethics of the ggplot() call:

ggplot(chic, aes(date, temp))

1. Data: ggplot()

We need to specify data and the two variables we want to plot as aestethics of the ggplot() call:

ggplot(chic, aes(date, temp))

ggplot(chic) +
  aes(date, temp)

2. Layers: geom_*() and stat_*()

By adding one or multiple layers we can tell {ggplot2} how to represent the data.
There are lots of build-in geometrics elements (geoms) and statistical transformations (stats):

2. Layers — Geometries: geom_point()

We can tell {ggplot2} to represent the data for example as a scatterplot:

ggplot(chic, aes(date, temp)) +
  geom_point()

2. Layers — Geometries: geom_line()

... or a line plot:

ggplot(chic, aes(date, temp)) +
  geom_line()

2. Layers — Geometries: geom_area()

... or an area plot:

ggplot(chic, aes(date, temp)) +
  geom_area()

2. Layers — Geometries: geom_boxplot()

... or a box and whiskers plot:

ggplot(chic, aes(date, temp)) +
  geom_boxplot()

2. Layers — Geometries: geom_boxplot()

... or a box and whiskers plot:

ggplot(chic, aes(date, temp)) +
  geom_boxplot()

2. Layers — Geometries: geom_boxplot()

We need to specify the variable as categorial (year), not as continuous (date):

ggplot(chic, aes(year, temp)) +
    geom_boxplot()

2. Layers — Geometries: Using Multiple geom_*()

Other layers can be added to an existing plot - a line and a rug representation for example:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  geom_line() +
  geom_rug(sides = "r")

2. Layers — Statistical transformations: stat_summary()

A handful of layers with attention to the statistical transformation rather than the visual appearance:

ggplot(chic, aes(year, temp)) +
  geom_boxplot() +
  stat_summary(
    fun.y = mean,
    geom = "point"
   )

2. Layers — Statistical transformations: stat_function()

stat_function() makes it easy to add a function to a plot, either continuous or discrete:

ggplot(tibble(x = c(-8, 8)), aes(x)) +
  stat_function(fun = dnorm) +
  stat_function(
    fun = dcauchy,
    geom = "point",
    n = 75
  )

2. Layers — Statistical transformations: stat_ecdf()

You can also easily plot the empirical cumulative distribution function (ECDF) of a variable:

ggplot(chic, aes(temp)) +
  stat_ecdf(geom = "step")

2. Layers — Statistical transformations: stat_summary_hex()

With stat_summary_hex() data is divided into bins defined by x and y and then filled with values of z:

ggplot(chic, aes(date, temp, z = o3)) +
  stat_summary_hex()

2. Layers — Statistical transformations: stat_smooth()

You can directly add smoothed conditional means:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  stat_smooth()

2. Layers — Statistical transformations: stat_smooth()

You can directly add smoothed conditional means:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  stat_smooth()

2. Layers — Statistical transformations: stat_smooth()

You can specify the fitting method and the formula:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  stat_smooth(
    method = "gam",
    formula = y ~ s(x, k = 100),
    se = F
  )

2. Layers — Statistical transformations: stat_smooth()

You can specify the fitting method and the formula:

ggplot(chic, aes(date, temp)) +
    geom_point() +
    stat_smooth(
        method = "lm",
        formula = y ~ poly(x, 10),
        se = F
    )

3. Aesthetics: aes()

Aesthetics of the geometric and statistical objects, such as

• position via x, y, xmin, xmax, ymin, ymax, ...

• colors via color and fill

• transparency via alpha

• sizes via size and width

• shapes via shape and linetype

In general, everything which maps to the data needs to be wrapped in aes()
while static arguments are placed outside the aes().

e.g.
geom_point(aes(color = season)) to color points based on the variable season
geom_point(color = "grey") to color all points in the same color

3. Aesthetics: aes(color/fill/alpha/size/shape)

This way, we can enlarge all points and add some transparency:

ggplot(chic, aes(date, temp)) +
  geom_point(
    size = 4,
    alpha = 0.2
  )

3. Aesthetics: aes(color/fill/alpha/size/shape)

... and change the color and the shape based on season and year:

ggplot(chic, aes(date, temp)) +
  geom_point(
    aes(
      color = season,
      shape = year
    ),
    size = 4,
    alpha = 0.2
  )

3. Aesthetics: aes(color/fill/alpha/size/shape)

Alternatively, all aesthetics can be grouped together (and are then applied to all geoms and stats):

ggplot(
  chic, 
  aes(date, 
      temp,
      color = season,
      shape = year)
  ) +
  geom_point(
    size = 4,
    alpha = 0.2
  )

3. Aesthetics: aes(group)

You can create subsets of the data by specifying a grouping variable via group:

ggplot(chic, aes(temp)) +
  stat_ecdf(
    aes(group = year),
    geom = "step"
  )

3. Aesthetics: aes(group)

You can create subsets of the data by specifying a grouping variable via group:

ggplot(chic, aes(temp)) +
  stat_ecdf(
    aes(color = year),
    geom = "step"
  )

4. Scales: scale_

One can use scale_*() to change properties of all the aesthetic dimensions mapped to the data.

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

• position via scale_x_*() and scale_y_*()

• colors via scale_color_*() and scale_fill_*()

• transparency via scale_alpha_*()

• sizes via scale_size_*()

• shapes via scale_shape_*() and scale_linetype_*()

... with extensions (*) such as

• continuous(), discrete(), reverse(), log10(), squrt(), date(), time() for axes
• continuous(), discrete(), manual(), gradient(), hue(), brewer() for colors and fills
• continuous(), discrete(), manual(), ordinal(), identity(), date() for transparencies
• continuous(), discrete(), manual(), ordinal(), identity(), area(), date() for sizes
• continuous(), discrete(), manual(), ordinal(), identity() for shapes and linetypes

4. Scales: scale_x_*() and scale_y_*()

For example, to change the titles of the axes...

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_x_date(
    name = NULL
  ) +
  scale_y_continuous(
    name = "Temperature (°F)"
  )

4. Scales: scale_x_*() and scale_y_*()

... and their properties such as the range, scaling, labels, and axis breaks:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_x_date(
    name = NULL, 
    limits = c(
      as.Date("1997-01-01"),
      as.Date("1999-12-31")
    )
  ) + 
  scale_y_log10(
    name = "Temperature (°F)",
    breaks = c(1, 10, 100),
    labels = scales::scientific
  )

4. Scales: scale_x_*() and scale_y_*()

With breaks and labels it's also possible to alter the labels without changing the data:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_x_date(
    name = NULL, 
    limits = c(
      as.Date("1997-01-01"),
      as.Date("1999-12-31")
    )
  ) + 
  scale_y_continuous(
    name = "How I feel...",
    breaks = c(0, 30, 60, 90),
    labels = c(
      '"Brrr, too cold!"',
      '"Still freezing."',
      '"Okay."',
      '"Uff, too hot!"'
    )
  )

4. Scales: scale_x_*() and scale_y_*()

Some people are annoyed by the extra spacing around the data but we can remove that:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_x_date(
    name = NULL,
    expand = c(0, 0)
  ) + 
  scale_y_continuous(
    name = "Temperature (°F)",
    expand = c(0, 0)
  )

4. Scales: scale_color_*() and scale_fill_*()

Similarly, we can change properties of the other aesthetics. for example get rid of the default colors:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_manual(
    values = c(
      "firebrick",
      "dodgerblue",
      "darkorchid",
      "goldenrod"
    )
  )

4. Scales: scale_color_*() and scale_fill_*()

... and change the title of the legend:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_manual( 
    values = c( 
      "firebrick",
      "dodgerblue",
      "darkorchid",
      "goldenrod"
    ),
    name = "Pretty sesons:"
  )

4. Scales: scale_color_*() and scale_fill_*()

... or remove the legend for a specific aesthetic:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_manual( 
    values = c( 
      "firebrick",
      "dodgerblue",
      "darkorchid",
      "goldenrod"
    ),
    guide = FALSE
  )

4. Scales: scale_shape_*()

... or remove the legend for a specific aesthetic:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season,
                 shape = year)) +
  scale_color_manual( 
    values = c( 
      "firebrick",
      "dodgerblue",
      "darkorchid",
      "goldenrod"
    ),
    guide = FALSE
  ) +
  scale_shape_discrete(
    solid = FALSE,
    name = "Year"
  )

4. Scales: scale_color_*() and scale_fill_*()

One can of course also use preset color palettes:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_brewer(
    palette = "Set2",
    guide = FALSE
  )

4. Scales: scale_color_*() and scale_fill_*()

One can of course also use preset color palettes:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_brewer(
    palette = "Set2",
    guide = FALSE
  )

4. Scales: scale_color_*() and scale_fill_*() — Color Palettes

5. Coordinate System: coord_*()

Coordinate systems combine the two position aesthetics (usually x and y) to produce a 2d position on the plot.

The meaning of the position aesthetics depends on the coordinate system used:

• Linear coordinate systems that preserve the shape of geoms:
  
  
  • coord_cartesian(): the default with two fixed perpendicular oriented axes
  • coord_flip(): a Cartesian coordinate system with flipped axes
  • coord_fixed(): a Cartesian coordinate system with a fixed aspect ratio

• Non-linear coordinate systems that likely change the shapes:
  
  
  • coord_map(): map projections
  • coord_polar(): a polar coordinate system
  • coord_trans(): arbitrary transformations to x and y positions

5. Coordinate System: coord_cartesian()

coord_*() functions allow you to zoom in a plot:

ggplot(chic, aes(date, temp)) +
  geom_line() +
  coord_cartesian(
    ylim = c(50, 70)
  )

5. Coordinate System: coord_cartesian()

In case you want to remove those data points, use scale_y_continuous(limits = c(min, max)):

ggplot(chic, aes(date, temp)) +
  geom_line() +
  coord_cartesian(
    ylim = c(50, 70)
  )

ggplot(chic, aes(date, temp)) +
  geom_line() +
  scale_y_continuous(
    limits = c(50, 70)
  )

5. Coordinate System: coord_cartesian()

In case you want to remove those data points, use scale_y_continuous(limits = c(min, max)):

ggplot(chic, aes(year, temp)) +
  geom_boxplot()  +
  coord_cartesian(
    ylim = c(50, 70)
  )

ggplot(chic, aes(year, temp)) +
  geom_boxplot()  +
  scale_y_continuous(
    limits = c(50, 70)
  )

5. Coordinate System: coord_cartesian()

coord_*() is also used to prevent clipping of points and correct alignment of axis ticks:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  scale_x_date(
    expand = c(0, 0)
  ) +
  coord_cartesian(ylim = c(0, 75))
ggplot(chic, aes(date, temp)) +
  geom_point() +
  scale_x_date(
    expand = c(0, 0)
  ) + 
  coord_cartesian(
    ylim = c(0, 75), 
    clip = "off"
  )

5. Coordinate System: coord_flip()

coord_flip() allows you to flip a Cartesian coordinate system:

ggplot(chic, aes(season, temp)) +
  geom_col() +
  coord_flip()

5. Coordinate System: coord_polar()

You can easily transform a rectangular coordinate system into a polar one:

ggplot(chic, aes(season, temp)) +
  stat_summary(fun.y = mean,
              geom = "bar")

ggplot(chic, aes(season, temp)) +
  stat_summary(fun.y = mean,
              geom = "bar") +
  coord_polar()

5. Coordinate System: coord_polar()

This way, we can create pie charts:

ggplot(chic, aes(factor(1), temp, 
                 fill = season)) +
  geom_col() +
  coord_polar() +
  scale_x_discrete(NULL, expand = c(0, 0)) +
  scale_y_continuous(NULL, expand = c(0, 0))

ggplot(chic, aes(factor(1), temp, 
                 fill = season)) +
  geom_col() +
  coord_polar(theta = "y") +
  scale_x_discrete(NULL, expand = c(0, 0)) +
  scale_y_continuous(NULL, expand = c(0, 0))

5. Coordinate System: coord_trans()

coord_*() functions allow you to zoom in a plot:

ggplot(chic, aes(o3, temp)) +
  geom_point() +
  coord_trans(x = "log2")

5. Coordinate System: coord_map()

Maps often come as polygons and can be plotted via geom_polygon():

(g_world <- 
  ggplot(map_data("world"),
         aes(long, lat, 
             group = group)) +
    geom_polygon(
      fill = "tan",
      color = "grey20"
    )
)

5. Coordinate System: coord_map()

Since maps are displaying spherical data, we must project the data via coord_map():

g_world +
  coord_map(xlim = c(-180,180))

g_world +
  coord_map("ortho")

6. Facets: facet_*()

Facetting generates small multiples each showing a different subset of the data:

Adapted from "ggplot2: Elegant graphics for data analysis" by Hadley Wickham

6. Facets: facet_wrap()

facet_wrap() splits the data into small multiples based on one grouping variable:

ggplot(chic, aes(temp, o3)) +
  geom_point(aes(color = year)) +
  facet_wrap(~ season)

6. Facets: facet_wrap()

facet_wrap() splits the data into small multiples based on one grouping variable:

ggplot(chic, aes(temp, o3)) +
  geom_point(aes(color = year)) +
  facet_wrap(~ season, 
             scales = "free")

6. Facets: facet_grid()

facet_grid() spans a grid of each combination of two grouping variables:

ggplot(chic, aes(temp, o3)) +
  geom_point(aes(color = year)) +
  facet_grid(season ~ year)

7. Visual Themes: theme() and theme_*()

• Themes control the display of all non-data elements of the plot
  

  • titles
  • labels
  • fonts
  • background
  • gridlines
  • legends
  • base size!

• Themes can be modified for each plot by calling theme() and element_ functions

• Themes can be used to give plots a consistent customized look by defining a custom theme using theme_update() or theme_set()
  

7. Visual Themes: theme()

To modify the theme of a plot use theme() in combination with element_*():

ggplot(chic, aes(date, temp)) +
  geom_point() +
  theme(
    axis.text = element_text(
      size = 15,
      face = "bold",
      color = "red"
    ),
    panel.grid.major.x = element_line(
      linetype = "dotted",
      color = "black"
    ),
    plot.background = element_rect(
      fill = "dodgerblue",
      color = "goldenrod"
    )
  )

7. Visual Themes: theme()

element_blank() can be used to remove certain theme elements:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  theme(
    axis.title.x = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  )

7. Visual Themes: theme()

Using the argument base_size() you can change the size of the text:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  theme_bw(base_size = 40)

7. Visual Themes: theme_*()

Use a built-in theme of {ggplot2}:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  theme_gray()

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  theme_classic()

7. Visual Themes: theme_*()

... or extension packages such as {ggthemes}:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  ggthemes::theme_gdocs() +
  ggthemes::scale_color_gdocs()

ggplot(chic, aes(date, temp)) +
  geom_point(color = "black") +
  ggthemes::theme_tufte()

7. Visual Themes: theme_*()

... or extension packages such as {hrbrthemes} and {tvthemes}:

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  hrbrthemes::theme_ft_rc(grid = "Y") +
  hrbrthemes::scale_color_ft()

ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  tvthemes::theme_simpsons() +
  tvthemes::scale_color_simpsons()

7. Visual Themes: theme_set() and theme_update()

theme_set() and theme_update() override settings completely or partly:

(g <- ggplot(chic, aes(date, temp)) +
  geom_point())

7. Visual Themes: theme_set() and theme_update()

theme_set() and theme_update() override settings completely or partly:

(g <- ggplot(chic, aes(date, temp)) +
  geom_point())
old <- theme_set(theme_grey())
g

7. Visual Themes: theme_set() and theme_update()

theme_set() and theme_update() override settings completely or partly:

(g <- ggplot(chic, aes(date, temp)) +
  geom_point())
old <- theme_set(theme_grey())
g
theme_update(
  panel.background = element_rect(
    fill = "tan",
    color = "black"
  )
) 
g

7. Visual Themes: theme_set() and theme_update()

theme_set() and theme_update() override settings completely or partly:

(g <- ggplot(chic, aes(date, temp)) +
  geom_point())
old <- theme_set(theme_grey())
g
theme_update(
  panel.background = element_rect(
    fill = "tan",
    color = "black"
  )
)
g
theme_set(old)
g

Working with Text: Title, Subtitle, Caption, and Tag

To quickly add a title, use ggtitle():

ggplot(chic, aes(date, temp)) +
  geom_point() +
  ggtitle("Temperatures in Chicago")

Working with Text: Title, Subtitle, Caption, and Tag

{ggplot2} has a built-in structure for title, subtitle, caption and tags via labs():

(g <- ggplot(chic, aes(date, temp)) +
  geom_point() +
  labs(
    x = "Year",
    y = "Temperature (°F)",
    title = "Temperatures in Chicago",
    subtitle = "Seasonal pattern of daily temperatures\nfrom 1997 to 2001",
    caption = "Data: NMMAPS",
    tag = "(a)"
  )
)

Working with Text: Title, Subtitle, Caption, and Tag

{ggplot2} has a built-in structure for title, subtitle, caption and tags:

g +
  theme(
    plot.title = element_text(
      size = 20,
      face = "bold",
      hjust = 1,
      margin = margin(15, 0, 15, 0)
    ),
    plot.caption = element_text(
      face = "italic"
    ),
    plot.tag.position = c(0.15, 0.75)
  )

Working with Text: annotate("text")

annotate() allows to add text inside the plot area:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  annotate(
    "text",
    x = as.Date("1998-07-01"),
    y = 10,
    label = "The Summer\nof 1998",
    size = 3,
    fontface = "bold",
    color = "firebrick"
  )

Working with Text: annotate("text")

The annotate() function comes from ggplot2 and is designed to use a so-called grob as input:

ggplot(chic, aes(date, temp)) +
  geom_point() +
  annotate(
    "text",
    x = as.Date("1998-07-01"),
    y = 10,
    label = "The Summer\nof 1998",
    size = 3,
    fontface = "bold",
    color = "firebrick"
  ) +
  facet_wrap(~ year, scales = "free_x")

Working with Text: annotation_custom(grob)

annotation_custom() allows to add text by relative positions which solves the problem:

text <- grid::grobTree(
  grid::textGrob(
    "Summertime!",
    x = 0.5, y = 0.1, hjust = 0.5,
    gp = grid::gpar(
      fontsize = 12,
      fontface = "bold",
      col = "firebrick"
    )
  )
) 
ggplot(chic, aes(date, temp)) +
  geom_point() +
  annotation_custom(text) +
  facet_wrap(~ year, scales = "free_x")

Working with Text: element_markdown() via {ggtext}

With the new {ggtext} package, it is possible to use Markdown and basic HTML within strings:

library(ggtext)
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_manual(
    name = NULL,
    values = c("#0072B2", "#009E73", "#FF8423", "#B22222"),
    labels = c(
      "<b style='color:#0072B2'>Spring:<br></b><i>Apr to Jun<br></i>",
      "<b style='color:#009E73'>Summer:</b><br><i>Jul to Sep<br></i>",
      "<b style='color:#FF8423'>Autumn:</b><br><i>Oct to Dec<br></i>",
      "<b style='color:#B22222'>Winter:</b><br><i>Jan to Mar<br></i>"
    )
  ) +
  theme(legend.text = element_markdown())

Working with Images: annotation_custom(grob)

The annotation_custom() function comes with ggplot2 and is designed to use a so-called grob as input:

img <- png::readPNG("./img/logo.png")
logo <- grid::rasterGrob(img, interpolate = T)
ggplot(chic, aes(date, temp)) +
  annotation_custom(
    logo,
    xmin = as.Date("1997-06-01"),
    xmax = as.Date("2000-06-01"),
    ymin = 0,
    ymax = 80
  ) +
  geom_point()

Working with Geometric Forms: annotate("rect")

Let's add some background indicating the years:

ggplot(chic, aes(date, temp)) +
  annotate(
    "rect",
    xmin = as.Date("1997-01-01"),
    xmax = as.Date("1997-12-31"),
    ymin = -Inf, ymax = Inf,
    color = NA, alpha = 0.2
  ) +
    annotate(
    "rect",
    xmin = as.Date("1999-01-01"),
    xmax = as.Date("1999-12-31"),
    ymin = -Inf, ymax = Inf,
    color = NA, alpha = 0.2
  ) +
  geom_point()

Working with Geometric Forms: annotate("rect") or geom_rect()?

If you plot many elements, use geom_rect():

rect <- tibble(
  xmin = as.Date(c("1997-01-01", "1999-01-01")),
  xmax = as.Date(c("1997-12-31", "1999-12-31")),
  ymin = rep(-Inf, 2),
  ymax = rep(Inf, 2)
) 
ggplot(chic) +
  geom_rect(
    data = rect,
    aes(xmin = xmin, xmax = xmax,
        ymin = ymin, ymax = ymax),
    color = NA,
    alpha = 0.2
  ) +
  geom_point(aes(date, temp))

Working with Geometric Forms: geom_abline(), geom_vline() and geom_hline()

We could also indicate new years by a vertical line:

lines <- tibble(
  xintercept = seq(
    as.Date("1997-01-01"),
    as.Date("2001-01-01"),
    length.out = 5)
) 
ggplot(chic, aes(date, temp)) +
  geom_point() +
  geom_vline(
    data = lines,
    aes(xintercept = xintercept))