Plotting data

ggplot, part 1

Roland Krause

MADS6

Tuesday, 5 November 2024

Introduction

Learning objectives

Learn and apply 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)

Followed by a second lecture

Introduction

ggplot2

Stands for grammar of graphics plot v2
Inspired by Leland Wilkinson work on the grammar of graphics in 2005.

Graphs are split into layers

Separate axis, curve(s), labels.
3 elements are required:
- data,
- aesthetics,
- geometry $\geqslant 1$

Creating a plot

Data

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

Aesthetics function

x = A

y = C

shape = D

Scaling to physical units $x = \frac{A-min(A)}{range(A)} \times width$

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

$shape = f_{s}(D)$

Geometry

Scatter plot

Data drawn as points

Mapped data

x	y	shape
25	11	circle
0	0	circle
75	53	square
200	300	square

Result

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

Faceting

Split by shape, aka trellis or lattice plots

Redundancy

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

Familiar country shape and data

Combining layers

All `ggplot` layers are functions

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

Warning

ggplot2 layers are combined with +!
The magrittr pipe %>% or the base pipe |> will not work!
This introduces a break in the workflow.

Using the pipe has an explicit error

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

Error:
! Cannot add <ggproto> objects together.
ℹ Did you forget to add this object to a <ggplot> object?

Palmer penguins

install.packages("palmerpenguins")
library(palmerpenguins)

Capabilities in ggplot

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>

Capabilities in ggplot

ggplot(data = penguins)

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm, 
      y = body_mass_g, 
      color = sex)

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm, 
      y = body_mass_g, 
      color = sex) +
  geom_point()

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm, 
      y = body_mass_g, 
      color = sex) +
  geom_point() +
  scale_color_manual(values = c("darkorange", "cyan4"), 
                     na.translate = FALSE)

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g, 
      color = sex) +
  geom_point() +
  scale_color_manual(values = c("darkorange", "cyan4"), 
                     na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass")

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g, 
      color = sex) +
  geom_point() +
  scale_color_manual(values = c("darkorange", "cyan4"), 
                     na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       x = "Flipper length (mm)",
       y = "Body mass (g)")

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g, 
      color = sex) +
  geom_point() +
  scale_color_manual(values = c("darkorange", "cyan4"), 
                     na.translate = FALSE) +
  labs(title = "Penguin flipper and body mass",
       x = "Flipper length (mm)",
       y = "Body mass (g)")

description <- "Dimensions for male/female Adelie, Chinstrap and Gentoo Penguins\nat Palmer Station LTER"

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g, 
      color = sex) +
  geom_point() +
  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 = description, 
       x = "Flipper length (mm)",
       y = "Body mass (g)")

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g, 
      color = sex) +
  geom_point() +
  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 = description, 
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex")

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g,
      color = sex) +
  geom_point() +
  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 = description,
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(plot.subtitle = element_text(size = 13),
        axis.title   = element_text(size = 11))

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g,
      color = sex) +
  geom_point() +
  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 = description,
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(plot.subtitle = element_text(size = 13),
        axis.title   = element_text(size = 11)) +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA))

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm, y = body_mass_g, color = sex) +
  geom_point() +
  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 = description,
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(plot.subtitle = element_text(size = 13),
        axis.title   = element_text(size = 11)) +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA)) +
  theme(plot.caption = element_text(hjust = 0, face = "italic"),
        plot.caption.position = "plot") +
  facet_wrap(vars(species))

Capabilities in ggplot

ggplot(data = penguins) +
  aes(x = flipper_length_mm, y = body_mass_g, color = sex) +
  geom_point() +
  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 = description,
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin sex") +
  theme(plot.subtitle = element_text(size = 13),
        axis.title   = element_text(size = 11)) +
  theme(legend.position = "bottom",
        legend.background = element_rect(fill = "white", color = NA)) +
  theme(plot.caption = element_text(hjust = 0, face = "italic"),
        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_line()`

`geom_bar()`

`geom_violin()`

`geom_histogram()`

`geom_density()`

Layers

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

Three layers are sufficient

Data
Aesthetics mapping data to plot component
Geometry at least one

Your first plot

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

My first plot

ggplot(data = penguins) +
  geom_point(mapping = aes(x = bill_length_mm,
                           y = bill_depth_mm,
                           colour = "green"))

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

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"))

Mapping rules

In aes()

refer to a valid 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

Why no string for mapping?

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

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

The expression was evaluated in penguins context.

Inheritance of arguments across layers

Compare the code and the results

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')

Note

aesthetics in ggplot() are passed on to allgeometries.

Note

aesthetics in geom_*() are specific (and can overwrite inherited)

Simpson’s paradox

Statistical correlation depending on stratification.

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

Complete data set $R^2 = $ 0.055

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

Adelie	Gentoo	Chinstrap
0.153	0.414	0.427

Your turn!

Use the classroom practical.
Install palmerpenguins package if you haven’t yet
Use the penguins data set and plot bill_length_mm, bill_depth_mm and species.
Map the variable island to the aesthetics shape.
Add a regression line using a linear model.
All dots (circles / triangles / squares) with:
- A size of 5
- A transparency of 30% (alpha = 0.7)

Goal

15:00

Answer:

ggplot(penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm,
           colour = species)) +
  geom_point(aes(shape = island),
             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 colors

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

Warning

Should be the job of geom_line()

Invisible aesthetic: grouping

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

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

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))

::: callout-tip fragment ### Collective aesthetics - Feels more natural since visual - But just a preference - Most code in the wild use geom_bar()

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

:::

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

Count yourself `geom_col()`

Change the stat for counts or …

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

use geom_col() - 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)))

See list in help pages

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

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

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 = c(0, NA))

Annoying to see those 3 bars in disorder

Reorder the categorical variable (`forcats`)

Using the function `fct_infreq()`

library(forcats)
penguins |>
  ggplot(aes(y = fct_infreq(species) |> fct_rev())) + 
  geom_bar() +
  scale_x_continuous(expand = c(0, NA)) +
  labs(title = "Palmer penguins species",
       y = NULL) +
  theme_minimal(14) +
  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 = 0.7, 
                 position = "identity")

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

Density plots

penguins |>
ggplot(aes(x = body_mass_g,
           fill = species,
           colour = species)) +
  geom_density(alpha = 0.7)

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

Overlay density and histogram

Naive approach: scale issue

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(bins = 30) +
  geom_density(colour = "red")

Solution: scale histogram to density one

ggplot(penguins, aes(x = body_mass_g)) +
  geom_histogram(bins = 30,
                 aes(y = stat(density))) + #<<
  geom_density(colour = "red")

Barplot, categories

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

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") + #<<
  scale_x_continuous(labels = scales::label_percent(),
                     position = "top",
                     expand = c(0, 0)) +
  labs(x = NULL, y = NULL) +
  theme_classic(16)

Pie charts involved another coordinate system

ggplot(penguins) +
  geom_bar(aes(y = species,
           fill = island),
           position = "fill") +
  labs(x = NULL, y = NULL) +
  coord_polar() #<<

penguins |> 
  ggplot() +
  geom_bar(aes(y = species,
           fill = island)) +
  labs(x = NULL, y = NULL) +
  coord_polar() #<<

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

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

Note

geom_boxplot() is assessing that:

body_mass_g is continuous
species is categorical/discrete

Boxplot, dodging by default

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

Filter out NA to avoid additional category for missing data.

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),
#  geom_point(position = position_dodge2(width = 0.9 , preserve = "total", padding = 0.005),
   
             alpha = 0.5,
             # don't need dots in legend
             show.legend = FALSE)

Coding mistake

What is wrong with this code?

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)

Before we stop

You learned to:

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

Acknowledgments

Aurélien Ginolhac
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 *
Romain Lesur (pagedown), Christophe Dervieux
Hadley Wickham

Further reading

ggplot2 book by H. Wickham (free access)
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

Plotting data

Introduction

Learning objectives

Introduction

ggplot2

Graphs are split into layers

Creating a plot

Result

Faceting

Split by shape, aka trellis or lattice plots

Redundancy

Familiar country shape and data

Combining layers

All ggplot layers are functions

Using the pipe has an explicit error

Palmer penguins

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Capabilities in ggplot

Geometric objects define the plot type to be drawn

geom_point()

geom_line()

geom_bar()

geom_violin()

geom_histogram()

geom_density()

Layers

Core layers

Other layers

Three layers are sufficient

Your first plot

My first plot

Mapping aesthetics

Unmapped parameters

Mapped parameters

Mapping aesthetics correctly

In aes() and refer to a data column

Why no string for mapping?

Inheritance of arguments across layers

Simpson’s paradox

Your turn!

Goal

Joining observations

Invisible aesthetic: grouping

Labels

Statistics / geometries are interchangeable

Let ggplot2 doing the stat for you

stat_count could be omitted since default

Count yourself geom_col()

The stat() function allows computation, like proportions

Classic counting

Flexibility in the asthetics for flipping axes

geom_bar() requires x OR y

Cleanup plot

Reorder the categorical variable (forcats)

Using the function fct_infreq()

Geometries catalogue

Histograms

Density plots

Overlay density and histogram

Naive approach: scale issue

Solution: scale histogram to density one

Barplot, categories

Default: position = "stack"

Dodging island: side by side

Preserve single bar

Stacked barchart for proportions

Pie charts involved another coordinate system

Boxplot, a continuous y by a categorical x

All `ggplot` layers are functions

`geom_point()`

`geom_line()`

`geom_bar()`

`geom_violin()`

`geom_histogram()`

`geom_density()`

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

Let `ggplot2` doing the stat for you

`stat_count` could be omitted since default

Count yourself `geom_col()`

The `stat()` function allows computation, like proportions

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

Reorder the categorical variable (`forcats`)

Using the function `fct_infreq()`

Default: `position = "stack"`

Dodging `island`: side by side

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

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