Data Visualization - Part Three
Miao Yu
2024-01-29
2d bin counts plot
A 2d bin counts plot divides the plane into regular hexagons, counts
the number of cases in each hexagon, and then (by default) maps the
number of cases to the hexagon fill. It is used to resolve the
“overplotting” problem, similar to using
position = "jitter" when doing the scatter plot.
For example, in the loans_full_schema data set, if we
hope to plot interest_rate against
debt_to_income using a scatter plot, it looks like
this:
ggplot(loans_full_schema) +
geom_point(aes(x = debt_to_income, y = interest_rate))
This graph is not very informative since many points overlap with
each other (overplotting). To make things more clear, we can use the
geom_bin_2d() function to create a 2d bin counts plot.
ggplot(loans_full_schema) +
geom_bin_2d(aes(x = debt_to_income, y = interest_rate))
In the graph above, the colors represent the counts (equivalently density) in each square bin. It is clear that we have more data points at low interest rate between 5% to 13% combined with low debt_to_income ratio between 0% to 20%.
Since there are relatively few points for a debt-to-income ratio of higher than 100%. We can filter our data and make the plot more detailed:
ggplot(loans_full_schema) +
geom_bin_2d(aes(x = debt_to_income, y = interest_rate)) +
xlim(0, 100)
Customize scales (breaks and labels)
Scales refer to the x- and y-ticks and their labels on axes. There are a few functions that can customize the scale. Let’s take the following graph as an example:
ggplot(loans_full_schema) +
geom_point(aes(x = debt_to_income, y = annual_income))
We see that the scales on x-axis are 0, 100, 200, 300, 400 and the scales on y-axis are 0, 500000, 1000000, 1500000 and 2000000.
Scale control functions
Let’s first learn how to change the position of scales using
scale_x_continuous and scale_y_continuous
functions.
ggplot(loans_full_schema) +
geom_point(aes(x = debt_to_income,
y = annual_income)) +
scale_x_continuous(breaks = seq(0, 450, 50)) +
scale_y_continuous(breaks = seq(0, 2000000, 250000))
By defining the breaks argument inside
scale_x_continuous or scale_y_continuous
function one can define all positions of scales.
Customize scales (Cont’d)
We can also customize the labels of scales.
ggplot(loans_full_schema) +
geom_point(aes(x = debt_to_income, y = annual_income)) +
scale_x_continuous(labels = NULL) +
scale_y_continuous(labels = NULL)
Here labels = NULL removes all labels on the
corresponding scale. Or we can define them by ourselves.
ggplot(loans_full_schema) +
geom_point(aes(x = debt_to_income/100, y = annual_income)) +
scale_x_continuous(name = "debt to income ratio", labels = scales::percent, limits = c(0, 1)) +
scale_y_continuous(labels = scales::dollar) 
We can also customize the label names here with the name
argument, and customize the limits with the limits
argument. Some useful scale options are scale::percent,
scale::dollar and scale::comma to change the
format of scales.
Customize scales (Cont’d)
In many data sets, one numeric variable may span a few orders of
magnitudes (for example, household income from $1,000 to $1,000,000). If
we use continuous_scale, the graph does not show details
very well. In that case we need to change our scale to log scale
(plotting the logarithm of variable).
For data exploration, it is common that one use log10 scales:
ggplot(loans_full_schema) +
geom_bin_2d(aes(x = debt_to_income/100, y = annual_income)) +
scale_x_continuous(name = "debt to income ratio", labels = scales::percent, limits = c(0, 1)) +
scale_y_log10(limits = c(5000, 2500000), labels = scales::dollar) +
labs(title = "LendingClub Loan Data",
x = "Debt to Income Ratio (in percentage)",
y = "Annual Income (in US dollar)") +
theme(plot.title = element_text(hjust = 0.5, size = rel(1.5), margin = margin(15,15,15,15)),
axis.title = element_text(size = rel(1.4)),
axis.title.x = element_text(margin = margin(10,5,5,5)),
axis.title.y = element_text(margin = margin(5,10,5,5)),
axis.text = element_text(size = rel(1.4)))
The functions scale_y_log10 and
scale_x_log10 converts y-axis or x-axis into log10 scale,
respectively.
Use preset themes
There are eight preset themes offered in ggplot, that
gives different settings in axes, grid and background appearance. They
are:
We can change the theme by calling the theme functions:
ggplot(mpg, aes(displ, hwy)) +
geom_point(aes(color = class)) +
geom_smooth() +
theme_classic()
Facets
Using “Facets” is another way to add additional variables into a graph.
Facets divide a plot into subplots based on the values of one or more discrete variables.
When creating subplots based on values of a single categorical variable, one should use
facet_wrap(). As below is an example.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2) +
labs(title = "Vehicle Fuel Economy Data by Vehicle Class",
x = "Engine Displacement (liter)",
y = "Highway Mile per Gallon") +
theme(plot.title = element_text(hjust = 0.5, size = rel(1.5), margin = margin(15,15,15,15)),
axis.title = element_text(size = rel(1.2)),
axis.title.x = element_text(margin = margin(10,5,5,5)),
axis.title.y = element_text(margin = margin(5,10,5,5)),
axis.text = element_text(size = rel(1.2)))
The facet_wrap() function wraps subplots into a
2-dimensional array. This is generally a better use of screen space
because most displays are roughly rectangular.
In the code above, ~ class is called a
formula in R. We will study it later. For now you just
need to know that ~ <VARIABLE_NAME> is needed as the
first argument of facet_wrap function.
In the graph above, we still plot engine
displacement vs highway mpg, but only plot
grouped data for each class in every subplot. By doing
this, we clearly see where each group is - better than plotting them
altogether.
The facet_grid function
When creating subplots based on values of two categorical
variables, one should use facet_grid():
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ class) + # A formula with two variables
labs(title = "Fuel Economy Data by Vehicle Class and Drive Train Type",
x = "Engine Displacement (liter)",
y = "Highway Mile per Gallon") +
theme(plot.title = element_text(hjust = 0.5, size = rel(1.5), margin = margin(15,15,15,15)),
axis.title = element_text(size = rel(1.2)),
axis.title.x = element_text(margin = margin(10,5,5,5)),
axis.title.y = element_text(margin = margin(5,10,5,5)),
axis.text = element_text(size = rel(1.2)))
In this case, a grid of subplots is created and the x- and y-axis of
the grid corresponds to the values of drv and
class, respectively.
For example, in the subplot at the top right corner, it plots
displ against hwy for data points with a
class value of suv, and a drv value of
4, which corresponds to 4-wheel drive suvs.
facet_grid() can be used to study the relationship
between four variables (two numeric and two categorical). When the data
set is large and complicated, it can be very useful to provide some
insights for us.