Basics of ggplot

Using ggplot2

In order to get our hands dirty, we will first have to load ggplot2. To do this, and to access the datasets, help pages, and functions that we will use in this assignment, we will load the so-called tidyverse by running this code:

library(tidyverse)

If you run this code and get an error message “there is no package called ‘tidyverse’”, you’ll need to first install it, then run library() once again. To install packages in R, we utilize the simple function install.packages(). In this case, we would write:

install.packages("tidyverse")
library(tidyverse)

Once we’re up and running, we’re ready to dive into some basic exercises. ggplot2 works by specifying the connections between the variables in the data and the colors, points, and shapes you see on the screen. These logical connections are called aesthetic mappings or simply aesthetics.

library(ggplot2)
... + geom_point() # Produces scatterplots
... + geom_bar() # Bar plots
.... + geom_boxplot() # boxplots
... #

Mappings Link Data to Things You See

library(gapminder)

## Warning: package 'gapminder' was built under R version 4.2.3

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.2.3

gapminder::gapminder

## # A tibble: 1,704 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # ℹ 1,694 more rows

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point()

Above we’ve loaded a different dataset and have started to explore a particular relationship. Before putting in this code yourself, try to intuit what might be going on.

Any ideas?

Here’s a breakdown of everything that happens after the p<- ggplot() call:

• data = gapminder tells ggplot to use gapminder dataset, so if variable names are mentioned, they should be looked up in gapminder
• mapping = aes(...) shows that the mapping is a function call. There is a deeper logic to this that I will disucss below, but it’s easiest to simply accept that this is how you write it. Put another way, the mapping = aes(...) argument links variables to things you will see on the plot.
• aes(x = gdpPercap, y = lifeExp) maps the GDP data onto x, which is a known aesthetic (the x-coordinate) and life expectancy data onto y
  • x and y are predefined names that are used by ggplot and friends

It turns out there’s a reason for doing all of this:

“The greatest value of a picture is when it forces us to notice what we never expected to see.”” — John Tukey

In the plot you made above, one group of points seems to fall outside of the linear trend. These cars have a higher mileage than you might expect. How can you explain these cars?

Let’s hypothesize that the cars are hybrids. One way to test this hypothesis is to look at the class value for each car. The class variable of the mpg dataset classifies cars into groups such as compact, midsize, and SUV. If the outlying points are hybrids, they should be classified as compact cars or, perhaps, subcompact cars (keep in mind that this data was collected before hybrid trucks and SUVs became popular).

You can add a third variable, like class, to a two dimensional scatterplot by mapping it to an aesthetic. An aesthetic is a visual property of the objects in your plot. Aesthetics include things like the size, the shape, or the color of your points. You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Since we already use the word “value” to describe data, let’s use the word “level” to describe aesthetic properties. Thus, we are interested in exploring class as a level.

You can convey information about your data by mapping the aesthetics in your plot to the variables in your dataset. For example, you can map the colors of your points to the class variable to reveal the class of each car. To map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside aes(). ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known as scaling. ggplot2 will also add a legend that explains which levels correspond to which values.

Let’s explore our previously saved p in greater detail. As with Exercise 1, we’ll add a layer. This says how some data gets turned into concrete visual aspects.

p + geom_point()
p + geom_smooth()

Note: Both of the above geom’s use the same mapping, where the x-axis represents gdpPercap and the y-axis represents lifeExp. You can find this yourself with some ease. But the first one maps the data to individual points, the other one maps it to a smooth line with error ranges.

We get a message that tells us that geom_smooth() is using the method = ‘gam’, so presumably we can use other methods. Let’s see if we can figure out which other methods there are.

?geom_smooth
p + geom_point() + geom_smooth() + geom_smooth(method = ...) + geom_smooth(method = ...)
p + geom_point() + geom_smooth() + geom_smooth(method = ...) + geom_smooth(method = ..., color = "red")

You may start to see why ggplot2’s way of breaking up tasks is quite powerful: the geometric objects can all reuse the same mapping of data to aesthetics, yet the results are quite different. And if we want later geoms to use different mappings, then we can override them – but it isn’t necessary.

Consider the output we’ve explored thus far. One potential issue lurking in the data is that most of it is bunched to the left. If we instead used a logarithmic scale, we should be able to spread the data out better.

p + geom_point() + geom_smooth(method = "lm") + scale_x_log10()

Try it: (OPTIONAL) Describe what the scale_x_log10() does. Why is it a more evenly distributed cloud of points now? (2-3 sentences.)

Nice. We’re starting to get somewhere. But, you might notice that the x-axis now has scientific notation. Let’s change that.

library(scales)
p + geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10(labels = scales::dollar)
p + geom_point() +
  geom_smooth(method = "lm") +
  scale_x_log10(labels = scales::...)

Try it: (OPTIONAL) What does the dollar() call do? How can you find other ways of relabeling the scales when using scale_x_log10()?

?dollar()

The Recipe

1. Tell the ggplot() function what our data is.
2. Tell ggplot() what relationships we want to see. For convenience we will put the results of the first two steps in an object called p.
3. Tell ggplot how we want to see the relationships in our data.
4. Layer on geoms as needed, by adding them on the p object one at a time.
5. Use some additional functions to adjust scales, labels, tickmarks, titles.

• e.g. scale_, labs(), and guides() functions

As you start to run more R code, you’re likely to run into problems. Don’t worry — it happens to everyone. I have been writing code in numerous languages for years, and every day I still write code that doesn’t work. Sadly, R is particularly persnickity, and its error messages are often opaque.

Start by carefully comparing the code that you’re running to the code in these notes. R is extremely picky, and a misplaced character can make all the difference. Make sure that every ( is matched with a ) and every ” is paired with another “. Sometimes you’ll run the code and nothing happens. Check the left-hand of your console: if it’s a +, it means that R doesn’t think you’ve typed a complete expression and it’s waiting for you to finish it. In this case, it’s usually easy to start from scratch again by pressing ESCAPE to abort processing the current command.

One common problem when creating ggplot2 graphics is to put the + in the wrong place: it has to come at the end of the line, not the start.

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp, color = 'yellow'))
p + geom_point() + scale_x_log10()

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp, ...))
p + geom_point(...) + scale_x_log10()

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point() + geom_smooth(color = "orange", se = FALSE, size = 8, method = "lm") + scale_x_log10()

p + geom_point(alpha = 0.3) +
  geom_smooth(method = "gam") +
  scale_x_log10(labels = scales::dollar) +
  labs(x = "GDP Per Capita", y = "Life Expectancy in Years",
       title = "Economic Growth and Life Expectancy",
       subtitle = "Data Points are country-years",
       caption = "Source: Gapminder")

library(scales)
p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp, color = continent, fill = continent))
p + geom_point()
p + geom_point() + scale_x_log10(labels = dollar)
p + geom_point() + scale_x_log10(labels = dollar) + geom_smooth()

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = continent)) + geom_smooth() + scale_x_log10()

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = continent)) +
  geom_smooth(mapping = aes(color = continent, fill = continent)) +
  scale_x_log10() +
  geom_smooth(mapping = aes(color = continent), method = "gam")