Plotting data, part 1

with ggplot2

Aurélien Ginolhac

University of Luxembourg

Friday, the 2^th of May, 2025

ggplot2

About this lecture

Learning objectives

Learn the basic grammar of graphics
Understand how it is implemented in ggplot2
- Input data structures as data.frame/tibble
- Mapping columns to display features (aesthetics)
- Types of graphics (geometries)
- Multiple and repeating graphics (facets)
- Transforming plots (scales)
- Using different coordinate systems
- Customizing graphs with themes
Make quick exploratory plots of your multidimensional data.

Introduction

ggplot2

Stands for grammar of graphics plot version 2
Inspired by Leland Wilkinson work on the grammar of graphics in 2005.

Idea: split a graph into layers

Such as axis, curve(s), labels.
3 elements are required: data, aesthetics, geometry \(\geqslant 1\)

ggplot2

ggplot2 layers

Basic example

Data

A	B	C	D
2	3	4	a
1	2	1	a
4	5	15	b
9	10	80	b

Aesthetics

x = A
y = C
shape = D

Geometry

dot / point

Scaling

\(x = \frac{A-min(A)}{range(A)}*width\)

\(y = \frac{C-min(C)}{range(C)}*height\)

Result

Data scaled

A	B	shape
25	11	circle
0	0	circle
75	53	square
200	300	square

What if we want to split into panels circles and squares?

Faceting

Split by shape

Redundancy

shape and facets provide the same information.
The shape aesthetic is free for another variable.

Faceting can also carry a message

Combine layers but not with pipes!

Warning

ggplot2 layers are combined with +, not %>% nor |>.

This introduces a break in the workflow. (ggplot1 would have been fine)

`ggplot` lines combined with `+`

library(ggplot2)
swiss |>
  ggplot(aes(x = Education, 
             y = Examination)) +
  geom_point() +
  scale_colour_brewer()

If not, the error is explicit

swiss |>
  ggplot(aes(x = Education, 
             y = Examination)) |>
  geom_point() +
  scale_colour_brewer()

Error in `geom_point()`:
! `mapping` must be created by `aes()`.
ℹ Did you use `%>%` or `|>` instead of `+`?

Build a plot by layers

swiss

             Fertility Agriculture Examination Education Catholic
Courtelary        80.2        17.0          15        12     9.96
Delemont          83.1        45.1           6         9    84.84
Franches-Mnt      92.5        39.7           5         5    93.40
Moutier           85.8        36.5          12         7    33.77
Neuveville        76.9        43.5          17        15     5.16
Porrentruy        76.1        35.3           9         7    90.57
Broye             83.8        70.2          16         7    92.85
Glane             92.4        67.8          14         8    97.16
Gruyere           82.4        53.3          12         7    97.67
Sarine            82.9        45.2          16        13    91.38
Veveyse           87.1        64.5          14         6    98.61
Aigle             64.1        62.0          21        12     8.52
Aubonne           66.9        67.5          14         7     2.27
Avenches          68.9        60.7          19        12     4.43
Cossonay          61.7        69.3          22         5     2.82
Echallens         68.3        72.6          18         2    24.20
Grandson          71.7        34.0          17         8     3.30
Lausanne          55.7        19.4          26        28    12.11
La Vallee         54.3        15.2          31        20     2.15
Lavaux            65.1        73.0          19         9     2.84
Morges            65.5        59.8          22        10     5.23
Moudon            65.0        55.1          14         3     4.52
Nyone             56.6        50.9          22        12    15.14
Orbe              57.4        54.1          20         6     4.20
Oron              72.5        71.2          12         1     2.40
Payerne           74.2        58.1          14         8     5.23
Paysd'enhaut      72.0        63.5           6         3     2.56
Rolle             60.5        60.8          16        10     7.72
Vevey             58.3        26.8          25        19    18.46
Yverdon           65.4        49.5          15         8     6.10
Conthey           75.5        85.9           3         2    99.71
Entremont         69.3        84.9           7         6    99.68
Herens            77.3        89.7           5         2   100.00
Martigwy          70.5        78.2          12         6    98.96
Monthey           79.4        64.9           7         3    98.22
St Maurice        65.0        75.9           9         9    99.06
Sierre            92.2        84.6           3         3    99.46
Sion              79.3        63.1          13        13    96.83
Boudry            70.4        38.4          26        12     5.62
La Chauxdfnd      65.7         7.7          29        11    13.79
Le Locle          72.7        16.7          22        13    11.22
Neuchatel         64.4        17.6          35        32    16.92
Val de Ruz        77.6        37.6          15         7     4.97
ValdeTravers      67.6        18.7          25         7     8.65
V. De Geneve      35.0         1.2          37        53    42.34
Rive Droite       44.7        46.6          16        29    50.43
Rive Gauche       42.8        27.7          22        29    58.33
             Infant.Mortality
Courtelary               22.2
Delemont                 22.2
Franches-Mnt             20.2
Moutier                  20.3
Neuveville               20.6
Porrentruy               26.6
Broye                    23.6
Glane                    24.9
Gruyere                  21.0
Sarine                   24.4
Veveyse                  24.5
Aigle                    16.5
Aubonne                  19.1
Avenches                 22.7
Cossonay                 18.7
Echallens                21.2
Grandson                 20.0
Lausanne                 20.2
La Vallee                10.8
Lavaux                   20.0
Morges                   18.0
Moudon                   22.4
Nyone                    16.7
Orbe                     15.3
Oron                     21.0
Payerne                  23.8
Paysd'enhaut             18.0
Rolle                    16.3
Vevey                    20.9
Yverdon                  22.5
Conthey                  15.1
Entremont                19.8
Herens                   18.3
Martigwy                 19.4
Monthey                  20.2
St Maurice               17.8
Sierre                   16.3
Sion                     18.1
Boudry                   20.3
La Chauxdfnd             20.5
Le Locle                 18.9
Neuchatel                23.0
Val de Ruz               20.0
ValdeTravers             19.5
V. De Geneve             18.0
Rive Droite              18.2
Rive Gauche              19.3

Build a plot by layers

swiss |>
  ggplot()

Build a plot by layers

swiss |>
  ggplot() +
  aes(x = Education)

Build a plot by layers

swiss |>
  ggplot() +
  aes(x = Education) +
  aes(y = Examination)

Build a plot by layers

swiss |>
  ggplot() +
  aes(x = Education) +
  aes(y = Examination) +
  geom_point()

Build a plot by layers

swiss |>
  ggplot() +
  aes(x = Education) +
  aes(y = Examination) +
  geom_point() +
  theme_bw(18)

Palmer penguins

install with

install.packages("palmerpenguins")

A more interesting example

library(palmerpenguins)

A more interesting example

library(palmerpenguins)
penguins

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

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot()

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g)

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex)

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point()

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13)

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE)

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER")

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13))

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13)) +
  labs(x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex")

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13)) +
  labs(x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA))

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13)) +
  labs(x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA)) +
  theme(plot.caption = element_text(hjust = 0, face = "italic"))

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13)) +
  labs(x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA)) +
  theme(plot.caption = element_text(hjust = 0, face = "italic")) +
  theme(plot.caption.position = "plot")

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13)) +
  labs(x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA)) +
  theme(plot.caption = element_text(hjust = 0, face = "italic")) +
  theme(plot.caption.position = "plot") +
  facet_wrap(vars(species))

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13)) +
  labs(x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA)) +
  theme(plot.caption = element_text(hjust = 0, face = "italic")) +
  theme(plot.caption.position = "plot") +
  facet_wrap(vars(species)) +
  scale_x_continuous(guide = guide_axis(n.dodge = 2))

A more interesting example

library(palmerpenguins)
penguins |>
  ggplot() +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  aes(color = sex) +
  geom_point() +
  theme_bw(base_family = "Roboto Condensed", base_size = 13) +
  scale_color_manual(values = c("darkorange", "cyan4"), na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER") +
  theme(plot.subtitle = element_text(size = 13)) +
  labs(x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA)) +
  theme(plot.caption = element_text(hjust = 0, face = "italic")) +
  theme(plot.caption.position = "plot") +
  facet_wrap(vars(species)) +
  scale_x_continuous(guide = guide_axis(n.dodge = 2)) +
  scale_y_continuous(labels = scales::label_comma())

Geometric objects define the plot type to be drawn

`geom_point()`

`geom_violin()`

`geom_line()`

`geom_histogram()`

`geom_bar()`

`geom_density()`

Tip

Have a look at the cheatsheet or the documentation for more possibilities.

Core layers

Other layers

They are present, it works because they have sensible default:

Theme is theme_grey
Coordinate is cartesian
Statistic is identity
Facets are disabled

3 layers are enough

Data
Aesthetics mapping data to plot component
Geometry at least one

Your first plot

library(palmerpenguins)

Your first plot

library(palmerpenguins)
library(ggplot2)

Your first plot

library(palmerpenguins)
library(ggplot2)
ggplot(data = penguins)

Your first plot

library(palmerpenguins)
library(ggplot2)
ggplot(data = penguins) +
  geom_point(mapping = aes(x = bill_length_mm,
                           y = bill_depth_mm,
                           colour = species))

Mapping aesthetics

Requirements

aes() map columns/variables data to aesthetics
Specific geometries (geom) have different expectations:
- univariate, one x or y for flipped axes
- bivariate, x and y like scatterplot
Continuous or Discrete variables
- Continuous for color ➡️ gradient
- Discrete for color ➡️ qualitative

geom_point() requires both x and y coordinates

ggplot(penguins) +
  geom_point(aes(x = bill_length_mm,
                 y = bill_depth_mm))

Same as previous slide
Without colour mapping

Unmapped parameters

geom_point() accepts additional arguments such as the colour
Define them to a fixed value without mapping

ggplot(penguins) +
  geom_point(aes(x = bill_length_mm,
                 y = bill_depth_mm),
             colour = "black")

Important

Parameters defined outside the aesthetics aes() are applied to all data.

Mapped parameters

Require two conditions:

Being defined inside the aesthetics aes()
Refer to one of the column data, here: mistake

ggplot(penguins) +
  geom_point(aes(x = bill_length_mm,
                 y = bill_depth_mm,
                 colour = country))

Error in FUN(X[[i]], ...): object 'country' not found

Passing the unknown column as string as a different effect:

ggplot(penguins) +
  geom_point(aes(x = bill_length_mm,
                 y = bill_depth_mm,
                 colour = "country"))

This is hardly useful, but we shall see an application later, stick to the 2 mapping rules: - Inside aes() and refer to a valid table column.

Mapping aesthetics correctly

In `aes()` and refer to a data column

ggplot(penguins) +
  geom_bar(aes(y = species,
               fill = sex))

species and sex are 2 valid columns in penguins

Advantages:

The legend 🟥/🟦 for free
Missing data are highlighted in grey ⬜️
Using y axis for categories eases reading

How not using a string for mapping is useful?

Fair question

Could we pass an expression?
Which penguins are above 4 kg?
Use body_mass_g > 4000 that returns a boolean to find out

ggplot(penguins) +
  geom_bar(aes(y = species,
               fill = body_mass_g > 4000))

The expression was evaluated in penguins context Obvious that Gentoo are bigger than the 2 other species

Inheritance of arguments across layers

Compare the two following (great example of a Simpson’s paradox):

ggplot(penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm)) +
  geom_point(aes(colour = species)) +
  geom_smooth(method = "lm", formula = 'y ~ x')

ggplot(penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm,
           colour = species)) +
  geom_point() +
  geom_smooth(method = "lm", formula = 'y ~ x')

Important

aesthetics in ggplot() are passed on to all geometries.
aesthetics in geom_*() are specific (and can overwrite inherited)

Try it

Map the island variable to a shape aesthetics for both dots and linear models
All dots (circles / triangles / squares) with:
- A size of 5
- A transparency of 30% (alpha = 0.7)

Aim plot:

05:00

Answer:

ggplot(penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm,
           shape = island,
           colour = species)) +
  geom_point(size = 5, alpha = 0.7) +
  geom_smooth(method = "lm",
              formula = 'y ~ x')

Joining observations

set.seed(212) # tidyr::crossing generate combinations
tib <- tibble(crossing(x = letters[1:4], 
                       g = factor(1:2)), 
              y = rnorm(8))

Suppose we want to connect dots by g

Should be the job of geom_line()

tib

# A tibble: 8 × 3
  x     g          y
  <chr> <fct>  <dbl>
1 a     1     -0.239
2 a     2      0.677
3 b     1     -2.44 
4 b     2      1.24 
5 c     1     -0.327
6 c     2      0.154
7 d     1      1.04 
8 d     2     -0.780

Invisible aesthetic: grouping

Naive

ggplot(tib, aes(x, y, colour = g)) +
  geom_line() + 
  geom_point(size = 4)

Correct

ggplot(tib, aes(x, y, colour = g)) +
  geom_line(aes(group = g)) +
  geom_point(size = 4)

Forcing connection

ggplot(tib, aes(x, y, colour = g)) +
  geom_line(aes(group = 1)) +
  geom_point(size = 4)

Labels

ggplot(penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm,
           shape = island,
           colour = species)) +
  geom_point() +
  geom_smooth(method = "lm",
              formula = 'y ~ x') +
  labs(title = "Bill ratios of Palmer penguins",
       caption = "Horst AM, Hill AP, Gorman KB (2020)",
       subtitle = "Split per species / island",
       shape = "Islands",
       x = "cumen length (mm)",
       y = "cumen depth (mm)")

Statistics / geometries are interchangeable

ggplot(penguins) +
  geom_bar(aes(y = species))

ggplot(penguins) +
  stat_count(aes(y = species))

Warning

Feels more natural since visual
But just a preference
Most code in the wild use geom

Let `ggplot2` doing the stat for you

stat_count could be omitted since default

ggplot(penguins, aes(x = species)) +
  geom_bar(stat = "count")

stat_count acts on the mapped var like dplyr::count()

count(penguins, species)

# A tibble: 3 × 2
  species       n
  <fct>     <int>
1 Adelie      152
2 Chinstrap    68
3 Gentoo      124

Or do it yourselft, but with `geom_col()`

If you give counts, change the stat

count(penguins, species) |>
  ggplot(aes(x = species, y = n)) +
  geom_bar(stat = "identity")

`geom_col()` has the default identity

count(penguins, species) |>
  ggplot(aes(x = species, y = n)) +
  geom_col()

The stat function allows computation, like proportions

Classic counting

ggplot(penguins, aes(y = species)) +
  geom_bar(aes(x = stat(count)))

Now compute proportions
Bonus: get x scale in % using scales

ggplot(penguins, aes(y = species)) +
  geom_bar(aes(x = stat(count) / sum(count))) +
  scale_x_continuous(labels = scales::label_percent())

Flexibility in the asthetics for flipping axes

`geom_bar()` requires `x` OR `y`

penguins |>
  # horizontal brings readability
  ggplot(aes(y = species)) +
  geom_bar()

Cleanup plot

penguins |>
  ggplot(aes(y = species)) +
  geom_bar() +
  labs(y = NULL) +
  scale_x_continuous(expand = expansion(mult = c(0, .1)))

No extra space on the left, but keep 10% on the right

Annoying to see those 3 bars in disorder

Reorder the categorical variable (`forcats`)

Using the function fct_infreq()

penguins |>
  ggplot(aes(y = fct_infreq(species))) +
  geom_bar() +
  scale_x_continuous(expand = expansion(mult = c(0, .1))) +
  labs(title = "Palmer penguins species",
       y = NULL) +
  theme_minimal(14) +
  # nice trick from T. Pedersen
  theme(panel.ontop = TRUE,
        # better to hide the horizontal grid lines
        panel.grid.major.y = element_blank())

See the new article FAQ about reordering.

Geometries catalogue

Histograms

penguins |>
ggplot(aes(x = body_mass_g,
           fill = species)) +
  geom_histogram(bins = 35,
                 alpha = 0.6, 
                 position = "identity")

Default bin value is 30 and will be printed out as a message
Default is stack for the position. Here we overlay with "identity" and use transparency

Density plots

penguins |>
ggplot(aes(x = body_mass_g,
           fill = species,
           colour = species)) +
  geom_density(alpha = 0.6)

Use both colour and fill mapped to the same variable for cosmetic purposes

Barplots: bar positions

Default: `position = "stack"`

ggplot(penguins) +
  geom_bar(aes(y = species, 
               fill = island))

Dodging `island`: side by side

ggplot(penguins) +
  geom_bar(aes(y = species, fill = island),
           position = "dodge")

But global width per species is preserved

Preserve single bar (same width)

ggplot(penguins) +
  geom_bar(aes(y = species,
           fill = island),
           position = position_dodge2(preserve = "single")) +
  labs(y = NULL)

Stacked barchart for proportions

penguins |>
  drop_na(sex) |>  # from tidyr
  ggplot() +
  geom_bar(aes(y = species,
               fill = sex),
           position = "fill") +
  geom_vline(xintercept = 0.5, 
             linetype = "dashed",
             colour = "grey30") +
  scale_x_continuous(labels = scales::label_percent(),
                     position = "top",
                     expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme_classic(16) # larger font sizes

Makes comparison of sex-ratio much easier

Boxplot, a continuous `y` by a categorical `x`

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

geom_boxplot() is assessing that:

body_mass_g is continuous
species is categorical/discrete

Boxplot, dodging by default

Filter out `NA` to avoid this category

penguins |>  # alternative to tidyr::drop_na()
  filter(!is.na(sex)) |>
  ggplot() +
  geom_boxplot(aes(y = body_mass_g,
                   x = species,
                   fill = sex))

Better: violin and jitter

Show the data

penguins |>
  filter(!is.na(sex)) |>
  # define aes here for both geometries
  ggplot(aes(y = body_mass_g,
             x = species,
             fill = sex,
             # for violin contours and dots
             colour = sex
  )) +        # very transparent filling
  geom_violin(alpha = 0.1, trim = FALSE) +
  geom_point(position = position_jitterdodge(dodge.width = 0.9),
             alpha = 0.5,
             # don't need dots in legend
             show.legend = FALSE)

Even better: beeswarm

ggplot extension `ggbeeswarm`

library(ggbeeswarm)
penguins |>
  filter(!is.na(sex)) |>
  ggplot(aes(y = body_mass_g,
             x = species,
             colour = sex)) +
  geom_quasirandom(dodge.width = 1) +
  theme_bw(14)

Coding mistake

What is wrong with the above code?

(Hint: think about inherited aesthetics)

penguins |>
  ggplot() +
  geom_point(aes(x = bill_length_mm, 
                 y = body_mass_g)) +
  geom_smooth(method = "lm")

Error in `geom_smooth()`:
! Problem while computing stat.
ℹ Error occurred in the 2nd layer.
Caused by error in `compute_layer()`:
! `stat_smooth()` requires the following missing aesthetics: x and y.

Inheritance of `aesthetics` in main `ggplot()`

penguins |>
  ggplot(aes(x = bill_length_mm, 
             y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

Control the dots plotting order

ggplot2 outputs dots as they appear in the input data

tibble(x = LETTERS[1:3],
       y = x) |>
  ggplot(aes(x, y)) +
  geom_point(aes(colour = x),
             show.legend = FALSE,
             size = 100, alpha = 0.9) +
  scale_color_brewer(palette = "Dark2") +
  theme_classic(20)

tibble(x = LETTERS[1:3], y = x) |>
  arrange(desc(x)) |>
  ggplot(aes(x, y)) +
  geom_point(aes(colour = x),
             show.legend = FALSE,
             size = 100, alpha = 0.9) +
  scale_color_brewer(palette = "Dark2") +
  theme_classic(20)

Before we stop

You learned to:

Apprehend Graphics as a language
Embrace the layer system
Link data columns to aesthetics
Discover geometries

Further reading 📚

ggplot2 book by H. Wickham (free access)
NEW official FAQ, axes, facets, custom, annot, reorder, bars
R for Data Science by H. Wickham / G. Gromelund (free access)
TidyTuesday challenges
Vizualisation gallery by Cédric Scherer
plotting tutorial by Cédric Scherer

Acknowledgments 🙏 👏

Hadley Wickham
Thomas Lin Pedersen, great webinar Plotting everything with ggplot2:
- part 1, ggplot2 API
- part 2, extending ggplot2
Thinkr (Diane Beldame, Vincent Guyader, Colin Fay)
Allison M. Horst, Alison P. Hill and Kristen B. Gorman
DreamRs (Victor Perrier, Fanny Meyer
Romain Lesurm Christophe Dervieux
Cédric Scherer

Thank you for your attention!

Plotting data, part 1

ggplot2

About this lecture

Introduction

Idea: split a graph into layers

ggplot2

ggplot2 layers

Basic example

Data

Aesthetics

Geometry

Scaling

Result

Data scaled

Faceting

Split by shape

Redundancy

Faceting can also carry a message

Combine layers but not with pipes!

ggplot lines combined with +

Build a plot by layers

Build a plot by layers

Build a plot by layers

Build a plot by layers

Build a plot by layers

Build a plot by layers

Palmer penguins

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

A more interesting example

Geometric objects define the plot type to be drawn

geom_point()

geom_violin()

geom_line()

geom_histogram()

geom_bar()

geom_density()

Core layers

Other layers

3 layers are enough

Your first plot

Your first plot

Your first plot

Your first plot

Mapping aesthetics

Requirements

Unmapped parameters

Mapped parameters

Mapping aesthetics correctly

In aes() and refer to a data column

How not using a string for mapping is useful?

Fair question

Inheritance of arguments across layers

Try it

Joining observations

Invisible aesthetic: grouping

Naive

Correct

Forcing connection

Labels

Statistics / geometries are interchangeable

Let ggplot2 doing the stat for you

Or do it yourselft, but with geom_col()

If you give counts, change the stat

geom_col() has the default identity

The stat function allows computation, like proportions

`ggplot` lines combined with `+`

`geom_point()`

`geom_violin()`

`geom_line()`

`geom_histogram()`

`geom_bar()`

`geom_density()`

In `aes()` and refer to a data column

Let `ggplot2` doing the stat for you

Or do it yourselft, but with `geom_col()`

`geom_col()` has the default identity

`geom_bar()` requires `x` OR `y`

Reorder the categorical variable (`forcats`)

Default: `position = "stack"`

Dodging `island`: side by side

Boxplot, a continuous `y` by a categorical `x`

Filter out `NA` to avoid this category

ggplot extension `ggbeeswarm`

Inheritance of `aesthetics` in main `ggplot()`