A tutorial of ggplot2

0 Preparation

The following packages need to be installed:

# install CRAN packages
pkg_install =   c("ggplot2", "tibble", "tidyr", "forcats", "purrr", "prismatic", "corrr",
    "cowplot", "ggforce", "ggrepel", "ggridges", "ggsci", "ggtext", "ggthemes",
    "grid", "gridExtra", "patchwork", "rcartocolor", "scico", "showtext",
    "shiny", "plotly", "highcharter", "echarts4r", "hrbrthemes")
install.packages(pkg_install)

In which:

• ggplot2: part of the tidyverse, implementing the grammar of graphic;
• tidyverse:
  • tibble: mordern data frames;
  • dplyr: data wrangling;
  • tidyr: data cleaning;
  • forcats: handling factors;
• corrr: correlation matrices;
• cowplot: composing ggplots;
• ggforce: sina plots and other cool stuffs;
• ggrepel: nice text labeling;
• ggridges: rigde plots;
• ggsci: nice color palettes;
• ggtext: advanced text rendering;
• ggthemes: additional themes;
• grid: creating graphical objects;
• gridExtra: additional functions for grid graphics;
• patchwork: multiple panel plots;
• prismatic: minipulating colors;
• rcartocolor: great color palettes;
• scico: perceptional uniform palettes;
• showtext: custom fonts;
• shiny: interactive applications;
• charter, echarts4r, ggiraph, highcharter, plotly: interactive visualization.

We also need to install devtools:

# install from GitHub since not on CRAN
install.packages('devtools')
devtools::install_github("JohnCoene/charter")

1 The Dataset

I was using the dataset: “National Morbidity and Mortality Air Pollution Study (NMMAPS)”

Install the readr first:

install.packages('readr')

1.1 Import data

The :: here call the namespace and can be used to access a function without loading the package.

library(readr)

Warning: package 'readr' was built under R version 4.4.3

chic <- readr::read_csv("https://cedricscherer.com/data/chicago-nmmaps-custom.csv")

Rows: 1461 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): city, season, month
dbl  (6): temp, o3, dewpoint, pm10, yday, year
date (1): date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

view some data:

tibble::glimpse(chic)

Rows: 1,461
Columns: 10
$ city     <chr> "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic…
$ date     <date> 1997-01-01, 1997-01-02, 1997-01-03, 1997-01-04, 1997-01-05, …
$ temp     <dbl> 36.0, 45.0, 40.0, 51.5, 27.0, 17.0, 16.0, 19.0, 26.0, 16.0, 1…
$ o3       <dbl> 5.659256, 5.525417, 6.288548, 7.537758, 20.760798, 14.940874,…
$ dewpoint <dbl> 37.500, 47.250, 38.000, 45.500, 11.250, 5.750, 7.000, 17.750,…
$ pm10     <dbl> 13.052268, 41.948600, 27.041751, 25.072573, 15.343121, 9.3646…
$ season   <chr> "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "…
$ yday     <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ month    <chr> "Jan", "Jan", "Jan", "Jan", "Jan", "Jan", "Jan", "Jan", "Jan"…
$ year     <dbl> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1…

head(chic,10)

# A tibble: 10 × 10
   city  date        temp    o3 dewpoint  pm10 season  yday month  year
   <chr> <date>     <dbl> <dbl>    <dbl> <dbl> <chr>  <dbl> <chr> <dbl>
 1 chic  1997-01-01  36    5.66    37.5  13.1  Winter     1 Jan    1997
 2 chic  1997-01-02  45    5.53    47.2  41.9  Winter     2 Jan    1997
 3 chic  1997-01-03  40    6.29    38    27.0  Winter     3 Jan    1997
 4 chic  1997-01-04  51.5  7.54    45.5  25.1  Winter     4 Jan    1997
 5 chic  1997-01-05  27   20.8     11.2  15.3  Winter     5 Jan    1997
 6 chic  1997-01-06  17   14.9      5.75  9.36 Winter     6 Jan    1997
 7 chic  1997-01-07  16   11.9      7    20.2  Winter     7 Jan    1997
 8 chic  1997-01-08  19    8.68    17.8  33.1  Winter     8 Jan    1997
 9 chic  1997-01-09  26   13.4     24    12.1  Winter     9 Jan    1997
10 chic  1997-01-10  16   10.4      5.38 24.8  Winter    10 Jan    1997

3 The {ggplot2} package

A ggplot is built up from a few basic elements:

1. Data;
2. Geometries geom_: the geometric shape (hình dạng) that will represent the data;
3. Aesthetics aes_: aesthetics (thẩm mỹ) of the geometric or statistical objects, such as postition, color, size, shape, and transparency;
4. Scales scale_: map between the data and the aesthetics dimensions (ánh xạ từ dữ liệu đến đồ thị), such as data range to plot width or factor values to colors;
5. Statistical transformations stat_: statistical summaries (thống kê) of data, such as quantitles, fitted curves, and sums;
6. Coordinate system coord_: the transformation used for mapping data coordinates into the plane of the data rectangles (hệ tọa độ);
7. Facets facet_: the arrangement of the data into a grid of plots;
8. Visual themes theme(): the overall visual defaults of a plot, such as background, grids, axes, default typeface, sizes and colors (tông).

Each of above elements can be ignored, but can be also called multiple times.

4 A default ggplot

Load the package for ability to use the functionality:

library(ggplot2)

Warning: package 'ggplot2' was built under R version 4.4.3

# library(tidyverse) # can also be imported from the tidy-universe!

A default ggplot needs three things that you have to specify: the data, aesthetics, and a geometry.:

• starting to define a plot by using ggplot(data = df);

• if we want to plot (in most cases) 2 variables, we must add positional aesthetics aes(x = var1, y = var2);

Notice that data was mentioned outside the scope of aes(), while variables are being mentioned inside aes().

For instance:

(g <- ggplot(chic, aes(x = date, y = temp)))

Just a blank panel, because ggplot2 does not know how we plot data ~ we still need to provide geometry. ggplot2 allows us to store the ggobject to a variable inside the environment - in this case, g - which can be extended later on (by adding more layers). We can print out the plot to the R interactive by putting all inside the ().

We have different geometries to use (called geoms because each function usually starts with geom_). For e.g., if we want to plot a scatter plot.

g + geom_point()

also a line plot which our managers always like:

g + geom_line()

cool but the plot does not look optimal, we can also using mutiple layers of geometry, where the magic and fun start.

# it's the same if we write g + geom_line() + geom_point()
g + geom_point() + geom_line()

4.1 Change properties of geometries

Turn all points to large fire-red diamonds:

g + geom_point(color = 'firebrick', shape = 'diamond', size = 2)

Note

• ggplot2 can unsderstand when we use color, colour, as well as col;

• We can use the default color or hex, or even RGB/RGBA with rgb(). For example:

g + geom_point(color = "#b22222", shape = "diamond", size = 2)

g + geom_point(color = rgb(178, 34, 34, maxColorValue = 255), shape = "diamond", size = 2)

4.1 Replacing the default ggplot2 theme

Calling eg theme_bw() using theme_set(), all following plots will have same blank’n’white theme.

theme_set(theme_bw())

g + geom_point(color = 'firebrick')

theme() is also a useful function to modify all kinds of theme elements (texts, rectangles, and lines). Refer this page for the full list.

5 Axes

5.1 Change axis titles

Use labs() to assign character string for each lable.

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x = 'Year', y = 'Temperature (°F)')

Can also using xlab() and ylab():

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  xlab("Year") +
  ylab("Temperature (°F)")

Not only the degree symbol before F, but also the supper script:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = expression(paste("Temperature (", degree ~ F, ")"^"(Hey, why should we use metric units?!)")))

5.2 Increase space between axis and axis titles.

Overwrite the default element_text() within the theme() call:

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x = 'Year', y = 'Temperature (°F)') +
    theme(axis.title.x = element_text(vjust = 0, size = 30),
         axis.title.y = element_text(vjust = 2, size = 30))

vjust refer to vertical alignment. We can also change the distance by specifying the margin of both text elements.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title.x = element_text(margin = margin(t = 10), size = 15),
        axis.title.y = element_text(margin = margin(r = 10), size = 15))

t and r in the margin are top and right. Margin has 4 arguments: margin(t, r, b, l). A good way to remember the order of the margin sides is “t-r-ou-b-l-e”.

5.3 Change aesthetics of the axis titles

Again, we use theme() function and modify the axis.title and/or the subordinated elements axis.title.x and axis.title.y . Within element_text(), we can modify the default of size, color, and face.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title = element_text(size = 15, color = "firebrick",
                                  face = "italic"))

the face argument can be used to make the font bold, italic, or even bold.italic.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title.x = element_text(color = "sienna", size = 15, face = 'bold'),
        axis.title.y = element_text(color = "orangered", size = 15, face = 'bold.italic'))

You could also use a combination of axis.title and axis.title.y, since axis.title.x inherits the values from axis.title. Eg:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title = element_text(color = "sienna", size = 15),
        axis.title.y = element_text(color = "orangered", size = 15))

One can modify some properties for both axis titles and other only for one or properties for each on its own:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title = element_text(color = "sienna", size = 15, face = "bold"),
        axis.title.y = element_text(face = "bold.italic"))

5.4 Change aesthetics of axis text

Similar to the title, we can change the appearance of the axis text (number indeed) by using axis.text and/or the subordinated elements axis.text.x and axis.text.y.

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x= "Year", y = expression(paste("Temperature(",degree ~ F, ")"))) +
    theme(axis.text = element_text(color = "dodgerblue", size = 13),
         axis.text.x = element_text(face = 'italic'))

5.5 Rotate axis text

Specifying an angle help us to rotate any text elements. With hjust and vjust we can adjust the position of text afterwards horizontally (0 = left, 1 = right), and vertically (0 = top, 1 = bottom).

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x= "Year", y = expression(paste("Temperature(",degree ~ F, ")"))) +
    # 50 means 50 degrees, not % =)))
    theme(axis.text.x = element_text(angle = 50, vjust = 1, hjust = 1, size = 13))

5.6 Remove axis text & ticks

Rarely a reason to do this but this is how it works.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.ticks.y = element_blank(),
        axis.text.y = element_blank())

If you want to get rid of a theme element, the element is always element_blank.

5.7 Remove Axis Titles

We could again use element_blank() but it is way simpler to just remove the label in the labs() (or xlab()) call:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = NULL, y = "")

Note that NULL removes the element (similarly to element_blank()) while empty quotes "" will keep the spacing for the axis title and simply print nothing.

5.8 Limit axis range

Some time you want to take a closer look at some range of you data. You can do this without subsetting your data:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  ylim(c(0, 50))

Warning: Removed 777 rows containing missing values or values outside the scale range
(`geom_point()`).

Alternatively you can use scale_y_continuous(limits = c(0, 50)) (subsetting) or coord_cartesian(ylim = c(0, 50)). The former removes all data points outside the range while the second adjusts the visible area (zooming) and is similar to ylim(c(0, 50)) (subsetting).

5.9 Force plot to start at the origin

chic_high <- dplyr::filter(chic, temp > 25, o3 > 20)

ggplot(chic_high, aes(x = temp, y = o3)) +
  geom_point(color = "darkcyan") +
  labs(x = "Temperature higher than 25°F",
       y = "Ozone higher than 20 ppb") +
  expand_limits(x = 0, y = 0)

Using coord_cartesian(xlim = c(0,NA), ylim = c(0,NA)) will lead to the same result.

chic_high <- dplyr::filter(chic, temp > 25, o3 > 20)

ggplot(chic_high, aes(x = temp, y = o3)) +
  geom_point(color = "darkcyan") +
  labs(x = "Temperature higher than 25°F",
       y = "Ozone higher than 20 ppb") +
  coord_cartesian(xlim = c(0, NA), ylim = c(0, NA))

But we can also force it to literally start at the origin!

ggplot(chic_high, aes(x = temp, y = o3)) +
  geom_point(color = "darkcyan") +
  labs(x = "Temperature higher than 25°F",
       y = "Ozone higher than 20 ppb") +
  expand_limits(x = 0, y = 0) +
  coord_cartesian(expand = FALSE, clip = "off")

The argument clip = "off" in any coordinate system, always starting with coord_*, allows to draw outside of the panel area. Call it here to make sure that the tick marks at c(0, 0) are not cut.

5.10 Axes with same scaling

Use coord_equal() with default ratio = 1 to ensure the units are equally scaled on the x-axis and on the y-axis. We can set the aspect ratio of a plot with coord_fixed() or coord_equal(). Both use aspect = 1 (1:1) as a default.

ggplot(chic, aes(x = temp, y = temp + rnorm(nrow(chic), sd = 20))) +
  geom_point(color = "sienna") +
  labs(x = "Temperature (°F)", y = "Temperature (°F) + random noise") +
  xlim(c(0, 100)) + ylim(c(0, 150)) +
  coord_fixed()

Warning: Removed 59 rows containing missing values or values outside the scale range
(`geom_point()`).

Ratios higher than one make units on the y axis longer than units on the x-axis, and vice versa:

ggplot(chic, aes(x = temp, y = temp + rnorm(nrow(chic), sd = 20))) +
  geom_point(color = "sienna") +
  labs(x = "Temperature (°F)", y = "Temperature (°F) + random noise") +
  xlim(c(0, 100)) + ylim(c(0, 150)) +
  coord_fixed(ratio = 1/5)

Warning: Removed 57 rows containing missing values or values outside the scale range
(`geom_point()`).

5.11 Use a function to alter labels

In case you want to format (eg. adding % sign) without change the data. It’s like lambda in Python!

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = NULL) +
  scale_y_continuous(label = function(x) {return(paste(x, "Degrees Fahrenheit"))})

6 Titles

6.1 Add a title

We can add a title via ggtitle() function.

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = "firebrick") +
    labs(x = "Year", y = "Temperature (°F)") +
    ggtitle("Temperatures in Chicago")

Alternatively, we can use labs(), where we can add serveral arguments ~ metadata of the plot (a sub-title, a caption, and a tag):

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = "firebrick") +
    labs(x = "Year", y = "Temperature (°F)",
        title = "Temperatures in Chicago",
        subtitle = "Seasonal pattern of daily temperatures from 1997 to 2001",
        caption = "Data: NMMAPS",
        tag = "Fig 1")

6.2 Make title bold & add a space at the baseline

We want to, again, modify the properties of a theme element, we use theme() with it elements & conjunctions. These settings not only work for the title, but also for plot.subtitle, plot.caption, plot.tag, legend.title, legend.text, axis.title, and axis.text.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)",
       title = "Temperatures in Chicago") +
  theme(plot.title = element_text(face = "bold",
                                  margin = margin(10, 0, 10, 0),
                                  size = 14))

6.3 Adjust the position of titles

We can use the hjust and vjust for horizontal and vertical alighment:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = NULL,
       title = "Temperatures in Chicago",
       caption = "Data: NMMAPS") +
  theme(plot.title = element_text(hjust = 1, size = 16, face = "bold.italic"))

But since 2019, we can also use plot.title.position and plot.caption.position. This fit better for the case of long long axis that makes the alignment looks terrible.

The old way:

(g <- ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  scale_y_continuous(label = function(x) {return(paste(x, "Degrees Fahrenheit"))}) +
  labs(x = "Year", y = NULL,
       title = "Temperatures in Chicago between 1997 and 2001 in Degrees Fahrenheit",
       caption = "Data: NMMAPS") +
  theme(plot.title = element_text(size = 14, face = "bold.italic"),
        plot.caption = element_text(hjust = 0)))

The new one:

g + theme(plot.title.position = "plot",
          plot.caption.position = "plot")

6.4 Use a non-traditional font in your title

The fonts you can use are not limited by the ones provided by ggplot2, and the OS/fonts installed as well. We can use showtext for various types of font (TrueType, OpenType, Type 1, web fonts, etc.). The font_acc_google() and font_add() also work well, especially for Google fonts. But note that you must have the fonts installed, then re-load the IDE (Rstudio or VS Code) to use them.

library(showtext)

Warning: package 'showtext' was built under R version 4.4.3

Loading required package: sysfonts

Loading required package: showtextdb

font_add_google("Playfair Display", ## name of Google font
                "Playfair")  ## name that will be used in R
font_add_google("Bangers", "Bangers")

And then voila we can use them with, again, theme():

# From author's code, I've added `showtext_auto()`, then it worked!
# https://stackoverflow.com/questions/56072340/fonts-not-available-in-r-after-importing
showtext_auto()
ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)",
       title = "Temperatures in Chicago",
       subtitle = "Daily temperatures in °F from 1997 to 2001") +
  theme(plot.title = element_text(family = "Bangers", hjust = .5, size = 25),
        plot.subtitle = element_text(family = "Playfair", hjust = .5, size = 15))

We can also set a font for all elements of our plot globally:

font_add_google("Roboto Condensed", "Roboto Condensed")
theme_set(theme_bw(base_size = 12, base_family = "Roboto Condensed"))

6.5 Change spacing in multi-line text

We can use the lineheight to change the spacing between lines, as how we squeezed the lines together below:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  ggtitle("Temperatures in Chicago\nfrom 1997 to 2001") +
  theme(plot.title = element_text(lineheight = .95, size = 16))

7 Legends

We will map the variable season to the aesthetic color. ggplot adds legend by default when we do such thing.

ggplot(chic,
       aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)")

7.1 Turn off the legend

Use the theme(legend.position = 'none') to remove the legend:

ggplot(chic,
       aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.position = 'none')

Based on specific case, we can also use the guides(color = 'none') or scale_color_discrete(guide = 'none'). Below we remove the color legend, but retain the shape legend.

ggplot(chic,
       aes(x = date, y = temp,
           color = season, shape = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  guides(color = "none")

7.2 Remove legend titles

As learned before, we can use element_blank() to draw nothing.

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.title = element_blank())

Either using scale_color_discrete(name = NULL) or labs(color = NULL) would drive the same result.

7.3 Change legend position

Basically, we can control the position of legend using legend.position in theme. There are top, right (default), bottom, and left:

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.position = "top")

Furthermore, ggplot offers full control as we can specify legend position using “latitude” & “longtitude”. Below we also modified the rectangle’s background color to transparent.

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)",
       color = NULL) +
  theme(legend.position = c(.15, .15),
        legend.background = element_rect(fill = "transparent"))

Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.

Update

Update:

A numeric legend.position argument in theme() was deprecated in ggplot2 3.5.0.

ℹ Please use the legend.position.inside argument of theme() instead.

7.4 Change legend direction

You can switch the direction of legend as you like:

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)",
       color = NULL) +
  theme(legend.position = c(.15, .15),
        legend.background = element_rect(fill = "transparent")) +
  guides(color = guide_legend(direction = 'horizontal'))

7.5 Change style of the legend title

By modifying the element_text() of legend.title inside theme, we can change the style of legend title.

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.title = element_text(family = "Playfair",
                                    color = "chocolate",
                                    size = 14, face = "bold"))

7.6 Change legend title

The easiest way - labs():

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)",
       color = "Seasons\nindicated\nby colors:") +
  theme(legend.title = element_text(family = "Playfair",
                                    color = "chocolate",
                                    size = 14, face = "bold"))

The other ways are: scale_color_discretet(name = "title"), or `guides(color = guide_legend(“title”)).

7.7 Change order of legend keys

Most of the time we want the full control on the order of legend keys. From Credric tutorial we can only change it by modifying the level of variable:

chic$season <-
  factor(chic$season,
         levels = c("Winter", "Spring", "Summer", "Autumn"))

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)")

More discussion on the internet, for eg., here.

7.8 Change legend labels

This can be achived by providing information for label in scale_color_discrete():

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  scale_color_discrete(
    name = "Seasons:",
    labels = c("Mar—May", "Jun—Aug", "Sep—Nov", "Dec—Feb")
  ) +
  theme(legend.title = element_text(
    family = "Playfair", color = "chocolate", size = 14, face = 2
  ))

7.9 Change background boxes in the legend

Lies beside the labels are the keys. We can change their background color by modifying the element rectangle of theme legend.key:

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.key = element_rect(fill = "darkgoldenrod1"),
        # fill = NA, fill = "transparent" would remove bg.
        legend.title = element_text(family = "Playfair",
                                    color = "chocolate",
                                    size = 14, face = 2)) +
  scale_color_discrete("Seasons:")

7.10 Change size of legend symbols

The default size is a litle lost in the plot, we can increase it by override the aesthetic using the guides layer again:

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(legend.key = element_rect(fill = NA),
        legend.title = element_text(color = "chocolate",
                                    size = 14, face = 2)) +
  scale_color_discrete("Seasons:") +
  guides(color = guide_legend(override.aes = list(size = 6)))

7.11 Leave a layer of the legend

Duplicate legend happens when we use multiple geom for one data. Like this:

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  geom_rug()

We can turn those redundant ones off by show.legend = FALSE:

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  geom_rug(show.legend = FALSE)

7.12 Manually adding legend items

ggplot2 will not add legend unless you map aesthetic (color, size, etc) to a variable. But there are sometime we want it.

ggplot(chic, aes(x = date, y = o3)) +
  geom_line(color = "gray") +
  geom_point(color = "darkorange2") +
  labs(x = "Year", y = "Ozone")

We force legend by providing the color aesthetic mapping to geom:

ggplot(chic, aes(x = date, y = o3)) +
  geom_line(aes(color = "line")) +
  geom_point(aes(color = "points")) +
  labs(x = "Year", y = "Ozone") +
  scale_color_discrete("Type:")

Then use the scale_color_manual() to change the color, and override the legend aesthetic using guides() function:

ggplot(chic, aes(x = date, y = o3)) +
  geom_line(aes(color = "line")) +
  geom_point(aes(color = "points")) +
  labs(x = "Year", y = "Ozone") +
  scale_color_manual(name = NULL,
                     guide = "legend",
                     values = c("points" = "darkorange2",
                                "line" = "gray")) +
  guides(color = guide_legend(override.aes = list(linetype = c(1, 0),
                                                  shape = c(NA, 16))))

7.13 Use other legend styles

The guide_legend() would work for categorical variable only. It will us guide_colorbar() (or guide_colourbar()) by default for continuous variable.

ggplot(chic,
       aes(x = date, y = temp, color = temp)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)", color = "Temperature (°F)")

But guide_legend() still can help you force legend to show discrete colors:

ggplot(chic,
       aes(x = date, y = temp, color = temp)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)", color = "Temperature (°F)") +
  guides(color = guide_legend())

We can also use binned scale:

ggplot(chic,
       aes(x = date, y = temp, color = temp)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)", color = "Temperature (°F)") +
  guides(color = guide_bins())

or binned scales as discrete colorbars:

ggplot(chic,
       aes(x = date, y = temp, color = temp)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)", color = "Temperature (°F)") +
  guides(color = guide_colorsteps())

8 Backgrounds & grid lines

Several ways to change the overall look of your plot as depicted above (see working with themes). But you can also change a particular element of the plotting panel.

8.1 Change the panel background color

By simply specifying the element_rectangle of panel.background inside theme:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "#1D8565", size = 2) +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(panel.background = element_rect(
    fill = "#64D2AA", color = "#64D2AA", linewidth = 2)
  )

Remember that there is panel.border layer on top of the background (and data), which is also an element_rectangle. So we should use a transparent color here not to hide the data.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "#1D8565", size = 2) +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(panel.border = element_rect(
    fill = "#64D2AA99", color = "#64D2AA", linewidth = 2)
  )

8.2 Change grid lines

There are 2 type of grid lines and you can change their characteristics using panel.grid.major and panel.grid.minor.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(panel.grid.major = element_line(color = "gray10", linewidth = .5),
        panel.grid.minor = element_line(color = "gray70", linewidth = .25))

We can further make it complex by specifying all 4 different levels:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(panel.grid.major = element_line(linewidth = .5, linetype = "dashed"),
        panel.grid.minor = element_line(linewidth = .25, linetype = "dotted"),
        panel.grid.major.x = element_line(color = "red1"),
        panel.grid.major.y = element_line(color = "blue1"),
        panel.grid.minor.x = element_line(color = "red4"),
        panel.grid.minor.y = element_line(color = "blue4"))

My taste is always remove them:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(panel.grid.minor = element_blank())

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(panel.grid = element_blank())

8.3 Change spacing of grid lines

We can also change the spacing behaviour:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  scale_y_continuous(breaks = seq(0, 100, 10),
                     minor_breaks = seq(0, 100, 2.5))

8.4 Change the plot background color

Similar to the plotting panel, we can change the whole plot background color:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(plot.background = element_rect(fill = "gray60",
                                       color = "gray30", linewidth = 2))

By specifying the same color for both plot and panel, you can achive a unified plot color.

9 Margins

Sometime we need more space between the plot border to the plot panel/area - so called the margin. We can use plot.margin in this circumstance:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(plot.background = element_rect(fill = "gray60"),
        plot.margin = margin(t = 1, r = 3, b = 1, l = 8, unit = "cm"))

        # can also use plot.margin = unit(c(1, 3, 1, 8), "cm"))

10 Multi-panel plots

ggplot2 offers 2 handy functions for multiple-small visualization: facet_grid() and facet_wrap().

10.1 Create a small multiples based on two variables

In case of 2 variables, facet_grid does the job.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "orangered", alpha = .3) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  labs(x = "Year", y = "Temperature (°F)") +
  facet_grid(year ~ season)

  # remember year ~ season != season ~ year

10.2 Create a small mutilples based on one variables

facet_wrap create a facet from single a variable, the appearance of subplots is controlled by ncol and nrow.

g <-
  ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = "chartreuse4", alpha = .3) +
    labs(x = "Year", y = "Temperature (°F)") +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

g + facet_wrap(~ year)

1 rows matrix:

g + facet_wrap(~ year, nrow = 1)

or whatever we like”

g + facet_wrap(~ year, ncol = 3) + theme(axis.title.x = element_text(hjust = .15))

10.3 Allow axes to roam free

Sometime you want the x and y axis to roam free according to data. This is not a good idea as it makes plots are quite un-comparable. But there it is:

g + facet_wrap(~ year, nrow = 2, scales = "free")

10.4 Use facet_wrap with two variables

The function facet_wrap is more versatile:

g + facet_wrap(year ~ season, nrow = 4, scales = "free_x")

It gives you more control of the plot design. In contrast, facet_grid while also have roam free ability, but just column-wise or row-wise:

g + facet_grid(year ~ season, scales = "free_x")

10.5 Modify the strip text

Strip texts and strip text boxes are the title of each subplot, you can modify them using theme:

g + facet_wrap(~ year, nrow = 1, scales = "free_x") +
  theme(strip.text = element_text(face = "bold", color = "chartreuse4",
                                  hjust = 0, size = 20),
        strip.background = element_rect(fill = "chartreuse3", linetype = "dotted"))

These 2 functions are adapted by Cedric from this answer of Claus Wilke, author of ggtext, allowing to hightlight specific labels:

library(ggtext)

Warning: package 'ggtext' was built under R version 4.4.3

library(purrr) ## for %||%

element_textbox_highlight <- function(..., hi.labels = NULL, hi.fill = NULL,
                                      hi.col = NULL, hi.box.col = NULL, hi.family = NULL) {
  structure(
    c(element_textbox(...),
      list(hi.labels = hi.labels, hi.fill = hi.fill, hi.col = hi.col, hi.box.col = hi.box.col, hi.family = hi.family)
    ),
    class = c("element_textbox_highlight", "element_textbox", "element_text", "element")
  )
}

element_grob.element_textbox_highlight <- function(element, label = "", ...) {
  if (label %in% element$hi.labels) {
    element$fill <- element$hi.fill %||% element$fill
    element$colour <- element$hi.col %||% element$colour
    element$box.colour <- element$hi.box.col %||% element$box.colour
    element$family <- element$hi.family %||% element$family
  }
  NextMethod()
}

Use it for all strip texts:

g + facet_wrap(year ~ season, nrow = 4, scales = "free_x") +
  theme(
    strip.background = element_blank(),
    strip.text = element_textbox_highlight(
      family = "Playfair", size = 12, face = "bold",
      fill = "white", box.color = "chartreuse4", color = "chartreuse4",
      halign = .5, linetype = 1, r = unit(5, "pt"), width = unit(1, "npc"),
      padding = margin(5, 0, 3, 0), margin = margin(0, 1, 3, 1),
      hi.labels = c("1997", "1998", "1999", "2000"),
      hi.fill = "chartreuse4", hi.box.col = "black", hi.col = "white"
    )
  )

Or hightlight one among others:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(aes(color = season == "Summer"), alpha = .3) +
  labs(x = "Year", y = "Temperature (°F)") +
  facet_wrap(~ season, nrow = 1) +
  scale_color_manual(values = c("gray40", "firebrick"), guide = "none") +
  theme(
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
    strip.background = element_blank(),
    strip.text = element_textbox_highlight(
      size = 12, face = "bold",
      fill = "white", box.color = "white", color = "gray40",
      halign = .5, linetype = 1, r = unit(0, "pt"), width = unit(1, "npc"),
      padding = margin(2, 0, 1, 0), margin = margin(0, 1, 3, 1),
      hi.labels = "Summer", hi.family = "Bangers",
      hi.fill = "firebrick", hi.box.col = "firebrick", hi.col = "white"
    )
  )

10.6 Create a panel of different plots

Extension is all you need, and in this case is patchwork by Thomas Lin Pedersen:

p1 <- ggplot(chic, aes(x = date, y = temp,
                       color = season)) +
        geom_point() +
        geom_rug() +
        labs(x = "Year", y = "Temperature (°F)")

p2 <- ggplot(chic, aes(x = date, y = o3)) +
        geom_line(color = "gray") +
        geom_point(color = "darkorange2") +
        labs(x = "Year", y = "Ozone")

library(patchwork)

Warning: package 'patchwork' was built under R version 4.4.3

p1 + p2

Make them vertically aligned by “dividing” them:

p1 / p2

And more fun :)

(g + p2) / p1

An alternative is cowplot by Claus Wilke:

library(cowplot)

Warning: package 'cowplot' was built under R version 4.4.3

Attaching package: 'cowplot'

The following object is masked from 'package:patchwork':

    align_plots

plot_grid(plot_grid(g, p1), p2, ncol = 1)

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And the gridExtra as well.

library(gridExtra)

Warning: package 'gridExtra' was built under R version 4.4.3

grid.arrange(g, p1, p2,
             layout_matrix = rbind(c(1, 2), c(3, 3)))

More complex layouts can be defined using patchwork:

layout <- "
AABBBB#
AACCDDE
##CCDD#
##CC###
"

p2 + p1 + p1 + g + p2 + plot_layout(design = layout)

11. Colors

In ggplot2, both color and fill can be:

1. specified as single color, or
2. assigned to variables.

Those variables that are inside the aesthetic have mapping encoded, and those are outside are unrelated properties.

ggplot(chic, aes(year)) +
  geom_bar(aes(fill = season), color = "grey", linewidth = 2) +
  labs(x = "Year", y = "Observations", fill = "Season:")

11.1 Specify single colors

We can specify single color for a geom:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "steelblue", size = 2) +
  labs(x = "Year", y = "Temperature (°F)")

And if provide both, color (outline) and fill (fill):

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(shape = 21, size = 2, stroke = 1,
             color = "#3cc08f", fill = "#c08f3c") +
  labs(x = "Year", y = "Temperature (°F)")

Refer then PDF of R color. You can also use hex, RGB or RGBA (via rgb(red, green, blue, alpha)).

11.2 Asign colors to variables

In ggplot2, colors that are assigned to a variables are modified via scale_color_* or scale_fill_*. You should understand what kind of variable you are dealing with (categorical or continuous) to effectively encode those (sequential, deverging, qualitative, etc).

11.3 Qualitative variables

Qualitative can be nominal, ordinal, or binary. By defalt the categorical color paletted looks like this:

(ga <- ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)", color = NULL))

11.3.1 Manually select qualitative colors

You can manually assign a set of colors to each category of qualitative colors (number of color has to match number of category) via scale_*_manual() (color, colour, or fill):

ga + scale_color_manual(values = c("dodgerblue4",
                                   "darkolivegreen4",
                                   "darkorchid3",
                                   "goldenrod1"))

11.3.2 Use built-in qualitative color palettes

There are scale_*_brewer() implemented the ColorBrewer palettes already:

ga + scale_color_brewer(palette = "Set1")

11.3.3 Use qualitative colors palettes from extension packages

There are several packages are created for this, and we can check it at this collection of Emil Hvitfeldt. For eg, paletteer or ggthemes:

library(ggthemes)

Warning: package 'ggthemes' was built under R version 4.4.3

Attaching package: 'ggthemes'

The following object is masked from 'package:cowplot':

    theme_map

ga + scale_color_tableau()

ggsci provide the scientific journal and sci-fi themed color palettes:

library(ggsci)

Warning: package 'ggsci' was built under R version 4.4.3

g1 <- ga + scale_color_aaas()
g2 <- ga + scale_color_npg()

library(patchwork)
(g1 + g2) * theme(legend.position = "top")

11.4 Quantitiave variables

This is default function for sequential color scheme for continuous variables:

gb <- ggplot(chic, aes(x = date, y = temp, color = temp)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)", color = "Temperature (°F):")

gb + scale_color_continuous()

The scale_*_gradient() would do the same:

gb + scale_color_gradient()

And the scale_*_gradient2() would do the divergent color scheme:

mid <- mean(chic$temp)  ## midpoint

gb + scale_color_gradient2(midpoint = mid)

11.4.1 Manually set a sequential color scheme

By providing information to the argument of scale_*_gradient():

gb + scale_color_gradient(low = "darkkhaki",
                          high = "darkgreen")

How about scale_*_gradient2():

gb + scale_color_gradient2(midpoint = mid, low = "#dd8a0b",
                           mid = "grey92", high = "#32a676")

11.4.2 The beautiful Viridis color palette

The viridis color palettes will make your chart:

• looks more pretty;
• well prints in grey scale;
• easier to read by colorblindess. (refer dichromat package)

and it is built in function in ggplot2:

p1 <- gb + scale_color_viridis_c() + ggtitle("'viridis' (default)")
p2 <- gb + scale_color_viridis_c(option = "inferno") + ggtitle("'inferno'")
p3 <- gb + scale_color_viridis_c(option = "plasma") + ggtitle("'plasma'")
p4 <- gb + scale_color_viridis_c(option = "cividis") + ggtitle("'cividis'")

library(patchwork)
(p1 + p2 + p3 + p4) * theme(legend.position = "bottom")

as well as discrete colors:

ga + scale_color_viridis_d(guide = "none")

11.4.3 Extension packages

There are several packages are created for this, and we can check it at this collection of Emil Hvitfeldt. For example, the rcartocolors ports the CARTOcolors to ggplot2:

library(rcartocolor)

Warning: package 'rcartocolor' was built under R version 4.4.3

g1 <- gb + scale_color_carto_c(palette = "BurgYl")
g2 <- gb + scale_color_carto_c(palette = "Earth")

(g1 + g2) * theme(legend.position = "bottom")

The second one is scico, provides the access to scientific color maps developed by Fabio Crameri.

library(scico)

Warning: package 'scico' was built under R version 4.4.3

g1 <- gb + scale_color_scico(palette = "berlin")
g2 <- gb + scale_color_scico(palette = "hawaii", direction = -1)

(g1 + g2) * theme(legend.position = "bottom")

11.5 Modify color palettes afterwards

Why should we do that? Current we know that the ggplot2 can only hanle one color and one fill scale, this funtionality, which the package had already provide after_scale(), can help us flag an evaluation mapping between these two after data had been scaled.

Below example is an usage of clr_negate() from the package primastic:

library(prismatic)

Warning: package 'prismatic' was built under R version 4.4.3

ggplot(chic, aes(date, temp, color = temp)) +
  geom_point(size = 5) +
  geom_point(aes(color = temp,
                 color = after_scale(clr_negate(color))),
             size = 2) +
  scale_color_scico(palette = "hawaii", guide = "none") +
  labs(x = "Year", y = "Temperature (°F)")

Warning: Duplicated aesthetics after name standardisation: colour

There are also lot of fun work with the package: clr_negate(), clr_lighten(), clr_darken() and clr_desaturate().

library(prismatic)

ggplot(chic, aes(date, temp)) +
  geom_boxplot(
    aes(color = season,
        fill = after_scale(clr_desaturate(clr_lighten(color, .6), .6))),
    linewidth = 1
  ) +
  scale_color_brewer(palette = "Dark2", guide = "none") +
  labs(x = "Year", y = "Temperature (°F)")

Read more on the control aesthetic evaluation.

12. Themes

12.1 Change the overall plotting themes

By default ggplot2 provides eight themes, but there are several packages provide additional themes. For example ggthemes. We can simply have The Economist plotting style with theme_economist() và scale_color_economist():

library(ggthemes)

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() +
  labs(x = "Year", y = "Temperature (°F)") +
  ggtitle("Ups and Downs of Chicago's Daily Temperatures") +
  theme_economist() +
  scale_color_economist(name = NULL)

Another style that follow the famous author Edward Tufte of the book “The Visual Display of Quantitative Information”:

library(dplyr)

Attaching package: 'dplyr'

The following object is masked from 'package:gridExtra':

    combine

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

chic_2000 <- filter(chic, year == 2000)

ggplot(chic_2000, aes(x = temp, y = o3)) +
  geom_point() +
  labs(x = "Temperature (°F)", y = "Ozone") +
  ggtitle("Temperature and Ozone Levels During the Year 2000 in Chicago") +
  theme_tufte()

Another packages, hrbrthemes by Bob Rudis:

library(hrbrthemes)

Warning: package 'hrbrthemes' was built under R version 4.4.3

ggplot(chic, aes(x = temp, y = o3)) +
  geom_point(aes(color = dewpoint), show.legend = FALSE) +
  labs(x = "Temperature (°F)", y = "Ozone") +
  ggtitle("Temperature and Ozone Levels in Chicago")

12.2 Change the font of all text elelments

Incredibly easy, using base_family:

g <- ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)",
       title = "Temperatures in Chicago")

g + theme_bw(base_family = "Playfair")

12.3 Change the size of all text elements

theme_bw also comes with several base_* argument, here for the text size:

g + theme_bw(base_size = 30, base_family = "Roboto Condensed")

12.4 Change the size of all lines and rect elements

g + theme_bw(base_line_size = 1, base_rect_size = 1)

12.5 Create your own theme

You can control for an entire session using theme_set as in theme_set(theme_bw()), the default is called theme_gray:

You can view the code inside a theme for modification of your own:

theme_gray

function (base_size = 11, base_family = "", base_line_size = base_size/22, 
    base_rect_size = base_size/22) 
{
    half_line <- base_size/2
    t <- theme(line = element_line(colour = "black", linewidth = base_line_size, 
        linetype = 1, lineend = "butt"), rect = element_rect(fill = "white", 
        colour = "black", linewidth = base_rect_size, linetype = 1), 
        text = element_text(family = base_family, face = "plain", 
            colour = "black", size = base_size, lineheight = 0.9, 
            hjust = 0.5, vjust = 0.5, angle = 0, margin = margin(), 
            debug = FALSE), axis.line = element_blank(), axis.line.x = NULL, 
        axis.line.y = NULL, axis.text = element_text(size = rel(0.8), 
            colour = "grey30"), axis.text.x = element_text(margin = margin(t = 0.8 * 
            half_line/2), vjust = 1), axis.text.x.top = element_text(margin = margin(b = 0.8 * 
            half_line/2), vjust = 0), axis.text.y = element_text(margin = margin(r = 0.8 * 
            half_line/2), hjust = 1), axis.text.y.right = element_text(margin = margin(l = 0.8 * 
            half_line/2), hjust = 0), axis.text.r = element_text(margin = margin(l = 0.8 * 
            half_line/2, r = 0.8 * half_line/2), hjust = 0.5), 
        axis.ticks = element_line(colour = "grey20"), axis.ticks.length = unit(half_line/2, 
            "pt"), axis.ticks.length.x = NULL, axis.ticks.length.x.top = NULL, 
        axis.ticks.length.x.bottom = NULL, axis.ticks.length.y = NULL, 
        axis.ticks.length.y.left = NULL, axis.ticks.length.y.right = NULL, 
        axis.minor.ticks.length = rel(0.75), axis.title.x = element_text(margin = margin(t = half_line/2), 
            vjust = 1), axis.title.x.top = element_text(margin = margin(b = half_line/2), 
            vjust = 0), axis.title.y = element_text(angle = 90, 
            margin = margin(r = half_line/2), vjust = 1), axis.title.y.right = element_text(angle = -90, 
            margin = margin(l = half_line/2), vjust = 1), legend.background = element_rect(colour = NA), 
        legend.spacing = unit(2 * half_line, "pt"), legend.spacing.x = NULL, 
        legend.spacing.y = NULL, legend.margin = margin(half_line, 
            half_line, half_line, half_line), legend.key = NULL, 
        legend.key.size = unit(1.2, "lines"), legend.key.height = NULL, 
        legend.key.width = NULL, legend.key.spacing = unit(half_line, 
            "pt"), legend.text = element_text(size = rel(0.8)), 
        legend.title = element_text(hjust = 0), legend.ticks.length = rel(0.2), 
        legend.position = "right", legend.direction = NULL, legend.justification = "center", 
        legend.box = NULL, legend.box.margin = margin(0, 0, 0, 
            0, "cm"), legend.box.background = element_blank(), 
        legend.box.spacing = unit(2 * half_line, "pt"), panel.background = element_rect(fill = "grey92", 
            colour = NA), panel.border = element_blank(), panel.grid = element_line(colour = "white"), 
        panel.grid.minor = element_line(linewidth = rel(0.5)), 
        panel.spacing = unit(half_line, "pt"), panel.spacing.x = NULL, 
        panel.spacing.y = NULL, panel.ontop = FALSE, strip.background = element_rect(fill = "grey85", 
            colour = NA), strip.clip = "inherit", strip.text = element_text(colour = "grey10", 
            size = rel(0.8), margin = margin(0.8 * half_line, 
                0.8 * half_line, 0.8 * half_line, 0.8 * half_line)), 
        strip.text.x = NULL, strip.text.y = element_text(angle = -90), 
        strip.text.y.left = element_text(angle = 90), strip.placement = "inside", 
        strip.placement.x = NULL, strip.placement.y = NULL, strip.switch.pad.grid = unit(half_line/2, 
            "pt"), strip.switch.pad.wrap = unit(half_line/2, 
            "pt"), plot.background = element_rect(colour = "white"), 
        plot.title = element_text(size = rel(1.2), hjust = 0, 
            vjust = 1, margin = margin(b = half_line)), plot.title.position = "panel", 
        plot.subtitle = element_text(hjust = 0, vjust = 1, margin = margin(b = half_line)), 
        plot.caption = element_text(size = rel(0.8), hjust = 1, 
            vjust = 1, margin = margin(t = half_line)), plot.caption.position = "panel", 
        plot.tag = element_text(size = rel(1.2), hjust = 0.5, 
            vjust = 0.5), plot.tag.position = "topleft", plot.margin = margin(half_line, 
            half_line, half_line, half_line), complete = TRUE)
    ggplot_global$theme_all_null %+replace% t
}
<bytecode: 0x0000017c7f5104a0>
<environment: namespace:ggplot2>

We can literally modify everything in the theme:

theme_custom <- function (base_size = 12, base_family = "Roboto Condensed") {
  half_line <- base_size/2
  theme(
    line = element_line(color = "black", linewidth = .5,
                        linetype = 1, lineend = "butt"),
    rect = element_rect(fill = "white", color = "black",
                        linewidth = .5, linetype = 1),
    text = element_text(family = base_family, face = "plain",
                        color = "black", size = base_size,
                        lineheight = .9, hjust = .5, vjust = .5,
                        angle = 0, margin = margin(), debug = FALSE),
    axis.line = element_blank(),
    axis.line.x = NULL,
    axis.line.y = NULL,
    axis.text = element_text(size = base_size * 1.1, color = "gray30"),
    axis.text.x = element_text(margin = margin(t = .8 * half_line/2),
                               vjust = 1),
    axis.text.x.top = element_text(margin = margin(b = .8 * half_line/2),
                                   vjust = 0),
    axis.text.y = element_text(margin = margin(r = .8 * half_line/2),
                               hjust = 1),
    axis.text.y.right = element_text(margin = margin(l = .8 * half_line/2),
                                     hjust = 0),
    axis.ticks = element_line(color = "gray30", linewidth = .7),
    axis.ticks.length = unit(half_line / 1.5, "pt"),
    axis.ticks.length.x = NULL,
    axis.ticks.length.x.top = NULL,
    axis.ticks.length.x.bottom = NULL,
    axis.ticks.length.y = NULL,
    axis.ticks.length.y.left = NULL,
    axis.ticks.length.y.right = NULL,
    axis.title.x = element_text(margin = margin(t = half_line),
                                vjust = 1, size = base_size * 1.3,
                                face = "bold"),
    axis.title.x.top = element_text(margin = margin(b = half_line),
                                    vjust = 0),
    axis.title.y = element_text(angle = 90, vjust = 1,
                                margin = margin(r = half_line),
                                size = base_size * 1.3, face = "bold"),
    axis.title.y.right = element_text(angle = -90, vjust = 0,
                                      margin = margin(l = half_line)),
    legend.background = element_rect(color = NA),
    legend.spacing = unit(.4, "cm"),
    legend.spacing.x = NULL,
    legend.spacing.y = NULL,
    legend.margin = margin(.2, .2, .2, .2, "cm"),
    legend.key = element_rect(fill = "gray95", color = "white"),
    legend.key.size = unit(1.2, "lines"),
    legend.key.height = NULL,
    legend.key.width = NULL,
    legend.text = element_text(size = rel(.8)),
    legend.text.align = NULL,
    legend.title = element_text(hjust = 0),
    legend.title.align = NULL,
    legend.position = "right",
    legend.direction = NULL,
    legend.justification = "center",
    legend.box = NULL,
    legend.box.margin = margin(0, 0, 0, 0, "cm"),
    legend.box.background = element_blank(),
    legend.box.spacing = unit(.4, "cm"),
    panel.background = element_rect(fill = "white", color = NA),
    panel.border = element_rect(color = "gray30",
                                fill = NA, linewidth = .7),
    panel.grid.major = element_line(color = "gray90", linewidth = 1),
    panel.grid.minor = element_line(color = "gray90", linewidth = .5,
                                    linetype = "dashed"),
    panel.spacing = unit(base_size, "pt"),
    panel.spacing.x = NULL,
    panel.spacing.y = NULL,
    panel.ontop = FALSE,
    strip.background = element_rect(fill = "white", color = "gray30"),
    strip.text = element_text(color = "black", size = base_size),
    strip.text.x = element_text(margin = margin(t = half_line,
                                                b = half_line)),
    strip.text.y = element_text(angle = -90,
                                margin = margin(l = half_line,
                                                r = half_line)),
    strip.text.y.left = element_text(angle = 90),
    strip.placement = "inside",
    strip.placement.x = NULL,
    strip.placement.y = NULL,
    strip.switch.pad.grid = unit(0.1, "cm"),
    strip.switch.pad.wrap = unit(0.1, "cm"),
    plot.background = element_rect(color = NA),
    plot.title = element_text(size = base_size * 1.8, hjust = .5,
                              vjust = 1, face = "bold",
                              margin = margin(b = half_line * 1.2)),
    plot.title.position = "panel",
    plot.subtitle = element_text(size = base_size * 1.3,
                                 hjust = .5, vjust = 1,
                                 margin = margin(b = half_line * .9)),
    plot.caption = element_text(size = rel(0.9), hjust = 1, vjust = 1,
                                margin = margin(t = half_line * .9)),
    plot.caption.position = "panel",
    plot.tag = element_text(size = rel(1.2), hjust = .5, vjust = .5),
    plot.tag.position = "topleft",
    plot.margin = margin(rep(base_size, 4)),
    complete = TRUE
  )
}

Then use it:

theme_set(theme_custom())

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() + labs(x = "Year", y = "Temperature (°F)") + guides(color = "none")

12.6 Update the current theme

A ggplot is extendable, so is theme - using theme_update:

theme_custom <- theme_update(panel.background = element_rect(fill = "gray60"))

ggplot(chic, aes(x = date, y = temp, color = season)) +
  geom_point() + labs(x = "Year", y = "Temperature (°F)") + guides(color = "none")

13. Lines

theme_custom <- theme_update(
  panel.background = element_rect(fill = "white"),
  panel.grid.major = element_line(linewidth = .5),
  panel.grid.minor = element_blank()
)

13.1 Add horizontal or vertical lines to a plot

We might want to plot a horizontal or vertical line across the plot to highlight a threshold, or an event. This can be done using geom_vline() or geom_hline():

ggplot(chic, aes(x = date, y = temp, color = o3)) +
  geom_point() +
  geom_hline(yintercept = c(0, 73)) +
  labs(x = "Year", y = "Temperature (°F)")

More customization on the lines:

g <- ggplot(chic, aes(x = temp, y = dewpoint)) +
  geom_point(color = "dodgerblue", alpha = .5) +
  labs(x = "Temperature (°F)", y = "Dewpoint")

g +
  geom_vline(aes(xintercept = median(temp)), linewidth = 1.5,
             color = "firebrick", linetype = "dashed") +
  geom_hline(aes(yintercept = median(dewpoint)), linewidth = 1.5,
             color = "firebrick", linetype = "dashed")

A sloping line also can be added using geom_abline():

reg <- lm(dewpoint ~ temp, data = chic)

g +
  geom_abline(intercept = coefficients(reg)[1],
              slope = coefficients(reg)[2],
              color = "darkorange2",
              linewidth = 1.5) +
  labs(title = paste0("y = ", round(coefficients(reg)[2], 2),
                      " * x + ", round(coefficients(reg)[1], 2)))

13.2 Add a line within a plot

One might need to limited the length of line, just for a certain range. This is when geom_linerange() comes to help:

g +
  ## vertical line
  geom_linerange(aes(x = 50, ymin = 20, ymax = 55),
                 color = "steelblue", linewidth = 2) +
  ## horizontal line
  geom_linerange(aes(xmin = -Inf, xmax = 25, y = 0),
                 color = "red", linewidth = 1)

13.3 Add curved lines and arrows to a plot

14. Text

14.1 Add labels to your data

14.2 Add text annotations

14.3 Use markdown and HTML rendering for annotations

15. Coordinates

15.1 Flip a plot

15.2 Flip an axis

15.3 Reverse an axis

15.4 Transform an axis

15.5 Circularize a plot

16. Chart Types

16.1 Alternatives to a box plot

16.1.1 Plot of points

16.1.2 Jitter the points

16.1.3 Violin plots

16.1.4 Combine violin with jitter

16.1.5 Combine violin with box plots

16.2 Create rug representation to a plot

16.3 Create a correlation matrix

16.4 Create a contour plot

16.5 Create a heatmap of counts

16.6 Create a ridge plot

17. Ribbons (AUC, CI, etc.)

18. Smoothings

18.1 Add a LOESS or GAM smoothing

18.2 Add a linear fit

18.3 Specify the formula for smoothing

19. Interactive Plots

19.1 Combine ggplot2 with shiny

19.2 Plot.ly via plotly and ggplot2

19.3 ggiraph and ggplot2

19.4 Highcharts via highcharter

19.5 Echarts via echarts4r

19.6 Chart.js via charter

20. Remarks, Tipps & Resources

References

• Source: https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/;
• Top 50 ggplot2 graphs: https://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html;
• ggplot2 extension gallery: https://exts.ggplot2.tidyverse.org/gallery/;
• How to extend ggplot2: https://cran.r-project.org/web/packages/ggplot2/vignettes/extending-ggplot2.html.