Setup
Make the tidyverse available.
Solution
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Grammar of Graphics
A basic graph has three elements.
- The data, a dataframe
- A mapping to describe the relationships between visual attributes
and variables in the dataframe.
- A geom to define the type of graphic.
We will use the builtin dataframe iris for some basic example. Run
glimpse to look at the contents.
Solution
## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
## $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
## $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…
Starting
Create a very simple ggplot object,p, and try to display it. In the
call to ggplot, just specify that the data is the iris dataframe. Then
display the object by typing its name
Solution
p = ggplot(data = iris)
p
No complaint, but no results.
Add a simple mapping
Creat an ggplot object p2. Specify that Sepal.length is mapped to
x.
Display p2.
Solution
p2 = ggplot(data = iris,mapping = (aes(x = Sepal.Length)))
p2
We can see that the variable Sepal.Length has been assigned to the
horizontal axis, but we still don’t have a usable result. We need to add
a geom to ask for some kind of graph. We’ll start with a histogram and
build on p2, but we’ll keep p2 to try other geoms.
Create p2a by adding geom_histogram().
Display p2a.
Solution
p2a = p2 + geom_histogram()
p2a
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
Density
A density plot is a good alternative to a histogram. Create p2b using
geom_density().
Solution
p2b = p2 + geom_density()
p2b
Adding a Rug
We can add on a second geom, a rug, to either of these. geom_rug()
places a “bristle” where each actual value occurs.
Do this to create and display p2a_rug and p2b_rug.
Solution
p2a_rug = p2a + geom_rug()
p2a_rug
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
p2b_rug = p2b + geom_rug()
p2b_rug
Exercise
Play with the bins argument for the histogram you just developed.
Pick a value which seems good to you.
Solution
p2a = p2 + geom_histogram(bins = 20)
p2a
p2a = p2 + geom_histogram(bins = 40)
p2a
Exercise
Play with the adjust parameter of the density geom with either p2a or
p2b_rug to get a value you like. The default value is 1. Smaller values
give you more detail while larger values give you a smoother plot.
Solution
p2b = p2 + geom_density(adjust = .5)
p2b
p2b = p2 + geom_density(adjust = 2)
p2b
A Relationship
We can look at the relationship between Sepal.Length and the
categorical Species in a few different ways. One simple way is to
include a mapping of color to Species.
Exercise
Do this for the histogram. Use an aes() in the geom. Outside of the
aes, set fill = “white”.
Solution
p2a = p2 + geom_histogram(aes(color = Species),fill = "white")
p2a
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
Exercise
Do the same for the density.
Solution
p2b = p2 + geom_density(aes(color = Species),fill = "white")
p2b
Which of these graphs is easier to interpret?
Exercise
An alternative is to create separate graphs using facet_wrap().
Do this for the histogram and p2a or p2b, setting ncol = 1.
Solution
p2a = p2 + geom_histogram(aes(color = Species),fill = "white") +
facet_wrap(~Species, ncol = 1)
p2a
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
Another Relationship
Create a scatterplot, p3, of Sepal.Width and Sepal.Length.
Solution
p3 = ggplot(data = iris,aes(x = Sepal.Width, y = Sepal.Length))
p3a = p3 + geom_point()
p3a
Exercise
It looks like there may be overplotting. Try geom_jitter()
instead.
Solution
p3 = ggplot(data = iris,aes(x = Sepal.Width, y = Sepal.Length))
p3a = p3 + geom_jitter()
p3a
Exercise
Let’s throw Species into the mix. Add it with a mapping to color in
the aes in geom_jitter().
Solution
p3 = ggplot(data = iris,aes(x = Sepal.Width, y = Sepal.Length))
p3a = p3 + geom_jitter(aes(color = Species))
p3a
Use facet_wrap()
In addition to color use facet_wrap().
p3a + facet_wrap(~Species)
Exercise
Do that with ncol = 1.
Solution
p3a + facet_wrap(~Species,ncol = 1)
