2 Installation

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

3 Introduction

ggplot2 uses a grammar of graphics which defines the rules of structuring mathematic and aesthetic elements to build graphs layer-by-layer.

To create a ggplot2 we call ggplot() supplying a data frame with the variables to plot with data and aesthetic mappings between variables in the data and visual properties of the objects in the graph with mapping = aes() (e.g., position, color of points or lines).

Then we use + to add layers of graphical components to the graph. Layers consist of geoms, stats, scales, coords, facets and themes. For example, we add objects to the graph with geom_*() functions (e.g, geom_point() for points, geom_line() for lines). We can also add color scales (e.g., scale_colour_brewer(), faceting specifications (e.g., facet_wrap()), and coordinate systems (e.g., coord_flip()).

To save a plot, we use ggsave().

https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/

https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/

4 Basic ggplot

Dataset mpg {ggplot2} contains fuel economy data from 1999 to 2008 for 38 popular models of cars

  • displ engine displacement, in litres
  • hwy: highway miles per gallon
head(mpg)
library(ggplot2)
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + geom_point()

5 Scatterplot

Dataset mtcars {datasets} contains fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models)

  • wt: weight (1000 lbs)
  • mpg: miles/(US) gallon
  • cyl: number of cylinders
  • am Transmission (0 = automatic, 1 = manual)
head(mtcars)
# Call ggplot() and supply data. This creates a blank canvas
ggplot(data = mtcars)

# Scatterplot
ggplot(data = mtcars) + geom_point(aes(x = wt, y = mpg))

# Change color of the points to blue. Color outside aes()
ggplot(data = mtcars) + geom_point(aes(x = wt, y = mpg), color = "blue")

# Use a different color for each value of am. Color inside aes(). Map variable am to color
mtcars$am <- factor(mtcars$am)
ggplot(data = mtcars) + geom_point(aes(x = wt, y = mpg, color = am))

# Use a different shape for each value of am. Shape inside aes(). Map variable am to shape
mtcars$am <- factor(mtcars$am)
ggplot(data = mtcars) + geom_point(aes(x = wt, y = mpg, shape = am))

# Change the size aesthetic (bubble chart)
ggplot(data = mtcars) + geom_point(aes(x = wt, y = mpg, size = cyl))
## Warning: Using size for a discrete variable is not advised.

# Change the transparency aesthetic to see all the points
ggplot(data = mtcars) + geom_point(aes(x = wt, y = mpg, alpha = cyl))
## Warning: Using alpha for a discrete variable is not advised.

# Change the size aesthetic and set transparency all points to 0.3
ggplot(data = mtcars) + geom_point(aes(x = wt, y = mpg, size = cyl), alpha = 0.3)
## Warning: Using size for a discrete variable is not advised.

6 Aesthetic mappings

Aesthetics mapping vs parameters setting

  • If we map aesthetics to variables inside the aes() function, the aesthetic will vary as the variable varies. For example, mapping x = time causes the position of the plotted data to vary with values of variable time. Similarly, mapping color = group causes the color of objects to vary with values of variable group.
  • If we set aesthetics to a constant outside the aes() function, the aesthetic will be applied to all objects in the graph.

Aesthetics mapping

  • geom_*() functions are used to plot objects in the graph (e.g., geom_point() for points, geom_line() for lines, geom_bar()for bars with bases on the x-axis)

  • Aesthetic mappings are a way of mapping variables in the data to particular visual properties (aesthetics) of the objects in the graph.

  • Aesthetics are specified inside aes(). Each layer inherits the aesthetics aes() specified inside of ggplot(). If new aesthetics are specified in a layer this will override the aesthetics specified in ggplot().

  • Aesthetics vary by geom_*(). Different geom_*() functions have some aesthetics as required and others as optional. For example, geom_point() requires both x and y, the minimal specification for a scatterplot. geom_point() also accepts aesthetic shape to define the shapes of points while geom_bar() does not accept shape.

Commonly used aesthetics are:

  • x: Map variable to a position on the x-axis
  • y: Map variable to a position on the y-axis
  • color (or colour): Map variable to the color of an object (e.g., point) (compare to fill below)
  • fill: Map variable to the fill color of an object
  • shape: Map variable to an object shape (e.g., in scatterplots)
  • size: Map variable to an object size
  • alpha: Map variable to the transparency of objects (value between 0: transparent and 1: opaque)
  • linetype: Map variable to the linetype of an object outline (solid, dashed, dotted, etc.)
  • group: Map variable to a group (each group on a separate line)

We can learn about aesthetics by typing vignette("ggplot2-specs").

7 Exercises ggplot2 - aesthetics

https://www.paulamoraga.com/book-r/99-problems-ggplot2-aesthetics.html

8 Lines

set.seed(12345)
d <- data.frame(x = 1:10, y = rnorm(10))
ggplot(data = d, aes(x = x, y = y)) + geom_line()

9 Histogram

Histograms depict the distribution of a continuous variable. geom_histogram() cuts the continuous variable mapped to x into bins, and count the number of values within each bin (default is 30 bins)

Dataset diamonds {ggplot2} contains the prices and other attributes of almost 54000 diamonds

  • carat weight of the diamond (0.2 - 5.01)
head(diamonds)
ggplot(diamonds, aes(carat)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

10 Barplot

Barplots are used to display the frequencies of categorical variables.

Dataset mpg {ggplot2} contains a subset of the fuel economy data

  • class “type” of car
  • drv type of drive train, where f = front-wheel drive, r = rear wheel drive, 4 = 4wd
  • hwy highway miles per gallon
head(mpg)
# Number of cars in each class
ggplot(mpg, aes(x = class)) + geom_bar(color = 'blue')

# Number of cars in each class. Bars coloured by type
ggplot(mpg, aes(class)) + geom_bar(aes(fill = drv))

11 Boxplot

Boxplots are useful for comparing whole distributions of a continuous variable between groups. geom_boxplot() creates boxplots of the variable mapped to y for each group defined by the values of the x variable.

ggplot(mpg, aes(class, hwy)) + geom_boxplot()

12 Titles and labels

We can use labs() to give labels for all aesthetics and titles. We can also use xlab() and ylab() to give labels to x-axis and y-axis, and ggtitle() to give a title to the graph.

ggplot(mpg, aes(x = displ, y = hwy, color = class)) + geom_point() +
  labs(title = "Fuel Efficiency by Engine Power",
       subtitle = "Fuel economy data from 1999 and 2008 for 38 popular models of cars",
       caption = "This is a caption",
       x = "Engine power (litres displacement)",
       y = "Fuel Efficiency (miles per gallon)",
       color = "Car Type")

Quick and easy ways to deal with long labels

https://www.andrewheiss.com/blog/2022/06/23/long-labels-ggplot/

13 Time plots

To plot time series we need to have the time variable in Date format. We can check the class of the time variable with str() or class() and use as.Date() to convert it to Date format if necessary. ggplot2 recognizes the date format and uses the appropriate x-axis labels. We can also customize the labels with scale_x_date()

set.seed(12345)
day <- seq(as.Date("2010/1/1"), as.Date("2011/12/31"), "week")
value <- rnorm(n = length(day), mean = 100, sd = 1)
day
##   [1] "2010-01-01" "2010-01-08" "2010-01-15" "2010-01-22" "2010-01-29"
##   [6] "2010-02-05" "2010-02-12" "2010-02-19" "2010-02-26" "2010-03-05"
##  [11] "2010-03-12" "2010-03-19" "2010-03-26" "2010-04-02" "2010-04-09"
##  [16] "2010-04-16" "2010-04-23" "2010-04-30" "2010-05-07" "2010-05-14"
##  [21] "2010-05-21" "2010-05-28" "2010-06-04" "2010-06-11" "2010-06-18"
##  [26] "2010-06-25" "2010-07-02" "2010-07-09" "2010-07-16" "2010-07-23"
##  [31] "2010-07-30" "2010-08-06" "2010-08-13" "2010-08-20" "2010-08-27"
##  [36] "2010-09-03" "2010-09-10" "2010-09-17" "2010-09-24" "2010-10-01"
##  [41] "2010-10-08" "2010-10-15" "2010-10-22" "2010-10-29" "2010-11-05"
##  [46] "2010-11-12" "2010-11-19" "2010-11-26" "2010-12-03" "2010-12-10"
##  [51] "2010-12-17" "2010-12-24" "2010-12-31" "2011-01-07" "2011-01-14"
##  [56] "2011-01-21" "2011-01-28" "2011-02-04" "2011-02-11" "2011-02-18"
##  [61] "2011-02-25" "2011-03-04" "2011-03-11" "2011-03-18" "2011-03-25"
##  [66] "2011-04-01" "2011-04-08" "2011-04-15" "2011-04-22" "2011-04-29"
##  [71] "2011-05-06" "2011-05-13" "2011-05-20" "2011-05-27" "2011-06-03"
##  [76] "2011-06-10" "2011-06-17" "2011-06-24" "2011-07-01" "2011-07-08"
##  [81] "2011-07-15" "2011-07-22" "2011-07-29" "2011-08-05" "2011-08-12"
##  [86] "2011-08-19" "2011-08-26" "2011-09-02" "2011-09-09" "2011-09-16"
##  [91] "2011-09-23" "2011-09-30" "2011-10-07" "2011-10-14" "2011-10-21"
##  [96] "2011-10-28" "2011-11-04" "2011-11-11" "2011-11-18" "2011-11-25"
## [101] "2011-12-02" "2011-12-09" "2011-12-16" "2011-12-23" "2011-12-30"
value
##   [1] 100.58553 100.70947  99.89070  99.54650 100.60589  98.18204 100.63010
##   [8]  99.72382  99.71584  99.08068  99.88375 101.81731 100.37063 100.52022
##  [15]  99.24947 100.81690  99.11364  99.66842 101.12071 100.29872 100.77962
##  [22] 101.45579  99.35567  98.44686  98.40229 101.80510  99.51835 100.62038
##  [29] 100.61212  99.83769 100.81187 102.19683 102.04919 101.63245 100.25427
##  [36] 100.49119  99.67591  98.33795 101.76773 100.02580 101.12851  97.61964
##  [43]  98.93973 100.93714 100.85445 101.46073  98.58690 100.56740 100.58319
##  [50]  98.69320  99.45961 101.94769 100.05359 100.35166  99.32902 100.27795
##  [57] 100.69117 100.82380 102.14507  97.65306 100.14959  98.65747 100.55330
##  [64] 101.58996  99.41312  98.16762 100.88814 101.59349 100.51685  98.70433
##  [71] 100.05462  99.21535  98.95065 102.33051 101.40271 100.94260 100.82626
##  [78]  99.18846 100.47625 101.02126 100.64538 101.04314  99.69563 102.47711
##  [85] 100.97122 101.86710 100.67204  99.69205 100.53652 100.82487  99.03610
##  [92]  99.14492 101.88695  99.60818  99.01937 100.68733  99.49496 102.15772
##  [99]  99.40020  99.30545 100.22393  98.84378 100.42242  98.67524 100.14108
d <- data.frame(day, value)
(g <- ggplot(d, aes(x = day, y = value)) + geom_line())

Format on the x-axis

We can use the scale_x_date() function to customize the format of the time variable displayed on the x-axis.

Breaks

We can also control the breaks and minor breaks to display with arguments date_breaks and date_minor_breaks.

g

g + scale_x_date(date_breaks = "1 month", date_labels = "%m")

g + scale_x_date(date_minor_breaks = "2 day")

14 Exercises ggplot2 - axes

https://www.paulamoraga.com/book-r/99-problems-ggplot2-axes.html

15 Themes

ggplot2 provides a few complete themes that enable to change the overall look of the graph. These themes control elements of the graph not related to the data such as background color, size of fonts, gridlines and color of labels.

We can add a theme to a specific graph or we can use theme_set() to make all plots with the same theme. For example, we can set theme_set(theme_bw()) to create all graphs with the dark-on-light theme.

g <- ggplot(mtcars, aes(wt, mpg)) + geom_point()


In conjunction with the theme system, the element_*() functions specify the display of how non-data components of the plot are drawn:

  • element_blank(): draws nothing and assigns no space
  • element_rect(): borders and backgrounds
  • element_line(): lines
  • element_text(): text

Angle of x-axis labels

We can modify the angle of the x-axis labels with element_text().

set.seed(12345)
day <- seq(as.Date("2010/1/1"), as.Date("2011/12/31"), "week")
value <- rnorm(n = length(day), mean = 100, sd = 1)
d <- data.frame(day, value)
(g <- ggplot(d, aes(x = day, y = value)) + geom_line())

# hjust: horizontal justification in [0, 1]
g + theme(axis.text.x = element_text(angle = 60, hjust = 1))

16 Annotations

g + annotate(geom = "text",  x = as.Date("2011-09-17"), y = 103, label="Annotation \n here") +
    annotate(geom = "point", x = as.Date("2011-12-17"), y = 103, size = 10, shape = 21, fill = "transparent") +
    geom_hline(yintercept = 100, color = "orange", size = .5)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

17 Exercises ggplot2 - theme

https://www.paulamoraga.com/book-r/99-problems-ggplot2-theme.html

18 Facets

We can use thefacet_wrap() and facet_grid() functions to split the data into subsets and create multiple plots (panels).

  • https://ggplot2-book.org/facet.html

  • facet_wrap() wraps a 1d ribbon of plots into a multirow panel of plots.

  • facet_grid(): produces a 2d grid of panels defined by variables which form the rows and columns.

  • facet_wrap() wraps a 1d ribbon of plots into a multirow panel of plots. Number of rows and columns can be specified with nrow and ncol.
# facet_wrap() creates a ribbon of plots using cut
ggplot(diamonds, aes(x = carat, y = price)) + geom_point() + 
  facet_wrap(~cut)

facet_grid() allows to specify which variables are used to split the data along rows and columns. Put the row-splitting variable, then ~ and then the column-splitting variable. The character . specifies no faceting along that dimension

# facet_grid() splits using clarity along rows and cut along columns
ggplot(diamonds, aes(x = carat, y = price)) + geom_point() + 
  facet_grid(clarity ~ cut)

Argument scales can be used to set scales shared across all facets (the default, "fixed"), or vary across rows ("free_x"), columns ("free_y"), or both rows and columns ("free").

ggplot(mpg, aes(displ, hwy)) + geom_point() +
  facet_wrap(vars(class), scales = "fixed")

ggplot(mpg, aes(displ, hwy)) + geom_point() +
  facet_wrap(vars(class), scales = "free")

19 Multiple ggplots in the same graphic

patchwork makes it simple to combine separate ggplots into the same graphic. Alternatively functions are gridExtra::grid.arrange() and cowplot::plot_grid().

# install.packages("devtools")
devtools::install_github("thomasp85/patchwork")
library(patchwork)

p1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
p2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
p1 + p2

p3 <- ggplot(mtcars) + geom_smooth(aes(disp, qsec))
p4 <- ggplot(mtcars) + geom_bar(aes(carb))

(p1 | p2 | p3) / p4

20 Saving plots

To save a plot produced with ggplot2, we can use the ggsave() function.

# Last plot displayed is saved by default
ggsave("plot.pdf")

# Store plot in an R object and use the plot argument to specify which plot to save instead of the last
g <- ggplot(diamonds, aes(x = carat, y = price)) + geom_point()
ggsave("plot.png", plot = g)

Alternatively, we can save the plot by specifying a device driver (e.g., png, pdf), printing the plot, and then shutting down the device with dev.off().

# Open plot device, print plot, and close device
png("plot.png")
ggplot(diamonds, aes(x = carat, y = price)) + geom_point()
dev.off()