Scatter plots

Introduction to Data Visualization with ggplot2

Rick Scavetta

Founder, Scavetta Academy

48 geometries

geom_*
abline	contour	dotplot	jitter	pointrange	ribbon	spoke
area	count	errorbar	label	polygon	rug	step
bar	crossbar	errorbarh	line	qq	segment	text
bin2d	curve	freqpoly	linerange	qq_line	sf	tile
blank	density	hex	map	quantile	sf_label	violin
boxplot	density2d	histogram	path	raster	sf_text	vline
col	density_2d	hline	point	rect	smooth

Common plot types

Plot type	Possible Geoms
Scatter plots	points, jitter, abline, smooth, count

Scatter plots

Each geom can accept specific aesthetic mappings, e.g. geom_point():

Essential
x,y

ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width)) + 
  geom_point()

Scatter plots

Each geom can accept specific aesthetic mappings, e.g. geom_point():

Essential	Optional
x,y	alpha, color, fill, shape, size, stroke

ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width,
                 col = Species)) + 
  geom_point()

Geom-specific aesthetic mappings

# These result in the same plot! 
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + 
  geom_point()

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + 
  geom_point(aes(col = Species))

Control aesthetic mappings of each layer independently:

head(iris, 3) # Raw data

Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1  setosa          5.1         3.5          1.4         0.2
2  setosa          4.9         3.0          1.4         0.2
3  setosa          4.7         3.2          1.3         0.2

iris %>%
  group_by(Species) %>% 
  summarise_all(mean) -> iris.summary

iris.summary # Summary statistics

# A tibble: 3 x 5
  Species    Sepal.Length Sepal.Width Petal.Length Petal.Width
  <fct>             <dbl>       <dbl>        <dbl>       <dbl>
1 setosa             5.01        3.43         1.46       0.246
2 versicolor         5.94        2.77         4.26       1.33 
3 virginica          6.59        2.97         5.55       2.03

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) +
  # Inherits both data and aes from ggplot() 
  geom_point() + 
  # Different data, but inherited aes
  geom_point(data = iris.summary, shape = 15, size = 5)

Shape attribute values

Example

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + 
  geom_point() +
  geom_point(data = iris.summary, shape = 21, size = 5, 
             fill = "black", stroke = 2)

On-the-fly stats by ggplot2

See the second course for the stats layer.
Note: Avoid plotting only the mean without a measure of spread, e.g. the standard deviation.

position = "jitter"

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + 
  geom_point(position = "jitter")

geom_jitter()

A short-cut to geom_point(position = "jitter")

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + 
  geom_jitter()

Don't forget to adjust alpha

Combine jittering with alpha-blending if necessary

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + 
  geom_jitter(alpha = 0.6)

Hollow circles also help

shape = 1 is a. hollow circle.
Not necessary to also use alpha-blending.

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + 
  geom_jitter(shape = 1)

Let's practice!

Introduction to Data Visualization with ggplot2