R Bootcamp - Day 5

more ggplot2

Jay Hesselberth

RNA Bioscience Initiative | CU Anschutz

2024-10-21

Class 4 & 5 outline

• Introduce ggplot2 & today’s data sets (Exercise 1)
• Understand the basics of ggplot2 (Exercise 2, 3)
• Geom functions (Exercise 4-8)
• Geom_point properties (Exercise 9)
• Position adjustments (Exercise 10)
• Coordinate and Scale Functions (Exercise 11)
• Zooming into a plot (Exercise 12)

• Faceting (Exercise 13)
• Themes (Exercise 14)
• Labels & Legends (Exercise 15)
• Adding lines to plots (Exercise 16)
• Making multi-panel figures (Exercise 17)
• Saving a plot (Exercise 18)

shape, size, fill, color, and transparency - Exercise 9

R has 25 built in shapes that are identified by numbers.

Some are similar: 0, 15, and 22 are all squares, but interact differently with color and fill aesthetics.

Hollow shapes have a border determined by color, solid shapes (15-18) are filled with color, an the filled shapes (21-24) have color border and fill inside.

Get a diamonds subset.

diamonds_subset <- sample_n(diamonds, 1000)

Note that aesthetics can also be defined within a geom.

This is useful if you use two different geoms that share an aesthetic.

ggplot(
  diamonds_subset,
  aes(
    x = carat,
    y = price
  )
) +
  geom_point(
    aes(
      fill = cut,
      size = depth
    ),
    alpha = 0.8,
    shape = 24,
    color = "white"
  )

Position adjustments - Exercise 10

Position adjustments determine how to arrange geoms that would otherwise occupy the same space.

A stacked bar chart.

ggplot(
  diamonds,
  aes(
    x = cut,
    fill = clarity
  )
) +
  geom_bar()

Dodged bars are easier to read (proportions are clearer)

ggplot(
  diamonds,
  aes(
    x = cut,
    fill = clarity
  )
) +
  geom_bar(position = "dodge")

Coordinate and Scale Functions - Exercise 11

• The coordinate system determines how the x and y aesthetics combine to position elements in the plot. The default coordinate system is Cartesian (coord_cartesian()), which can be tweaked with coord_map(), coord_fixed(), coord_flip(), and coord_trans(), or completely replaced with coord_polar().

• Scales control the details of how data values are translated to visual properties. There are 20+ scale functions. We will look at one; the ggplot2 cheatsheet is your friend for the rest.

Logarithmic axes - 1

Note the difference between axis labels in these two examples.

ggplot(
  diamonds_subset,
  aes(carat, price)
) +
  geom_point() +
  coord_trans(x = "log10")

Logarithmic axes - 2

ggplot(
  diamonds_subset,
  aes(carat, price)
) +
  geom_point() +
  scale_x_log10()

Flipping coordinate system (swapping x and y)

ggplot(
  diamonds,
  aes(x = cut)
) +
  geom_bar()

Now flip the axis.

ggplot(
  diamonds,
  aes(x = cut)
) +
  geom_bar() +
  coord_flip()

Brief aside: ggplot can handle on-the-fly data transformations.

Here we log-transform carat and convert USD to CAD.

ggplot(
  diamonds_subset,
  aes(
    x = log10(carat),
    y = price * 1.32
  )
) +
  geom_point()

Zooming into a plot - Exercise 12

We might want to change the limits of x or y axes to zoom in.

ggplot(
  diamonds_subset,
  aes(carat, price)
) +
  geom_point(alpha = 0.5) +
  xlim(0, 2) +
  ylim(0, 5000)

Faceting to plot subsets of data into separate panels - Exercise 13

“Facets” are a powerful tool to subdivide a plot based on the values of one or more discrete variables.

A density plot we’ve seen before.

Which variables can we use to subdivide the data?

ggplot(
  diamonds,
  aes(
    x = carat,
    fill = cut
  )
) +
  geom_density(alpha = 0.8)

Faceted by cut

ggplot(
  diamonds,
  aes(
    x = log(price),
  )
) +
  geom_density(
    fill = "blue",
    color = "blue",
    alpha = 0.2
  ) +
  facet_wrap(
    ~cut,
    nrow = 1
  )

Let’s also use facet_grid() to facet by two variables.

Faceted by clarity and cut.

ggplot(
  diamonds,
  aes(
    x = log(price)
  )
) +
  geom_density(
    fill = "blue",
    color = "blue",
    alpha = 0.2
  ) +
  facet_grid(
    clarity ~ cut
  )

Scatter plot with facets.

#|
ggplot(
  diamonds,
  aes(
    x = carat,
    y = price,
    color = cut
  )
) +
  geom_point(alpha = .05) +
  facet_wrap(
    ~cut,
    nrow = 1
  )

Themes - Exercise 14

Themes can significantly affect the appearance of your plot. Thanksfully, there are a lot to choose from.

Scatter plot with default theme.

ggplot(
  diamonds_subset,
  aes(
    x = carat,
    y = price,
    color = cut
  )
) +
  geom_point(alpha = 0.8)

Change the theme with theme_bw().

ggplot(
  diamonds_subset,
  aes(
    x = carat,
    y = price,
    color = cut
  )
) +
  geom_point(alpha = 0.8) +
  theme_bw()

My go-to is cowplot::theme_cowplot().

It implements much of the advice in the “Dataviz” book, i.e.. YOUR LABELS ARE TOO SMALL.

ggplot(
  data = diamonds_subset,
  mapping = aes(
    x = carat,
    y = price,
    color = cut
  )
) +
  geom_point(alpha = 0.8) +
  theme_cowplot()

We’re not going to cover it, but you can also customize pre-existing themes.

Labels & Legends - Exercise 15

Use labs() to add / change plot labels.

ggplot(
  data = diamonds,
  mapping = aes(
    x = fct_reorder(cut, log(price)),
    y = log(price)
  )
) +
  geom_boxplot(fill = "lightblue") +
  labs(
    x = "Cut",
    y = "Price (log)",
    color = "Cut",
    title = "Distribution of diamond prices by cut",
    subtitle = "If you liked it then you shoulda put a ring on it",
    caption = "Source: diamonds dataset from ggplot2"
  ) +
  theme_cowplot()

Additional points

How to add a line to a plot? (Exercise 16)

p <- ggplot(
  diamonds_subset,
  aes(
    x = carat,
    y = price,
    color = cut
  )
) +
  geom_point(alpha = 0.8) +
  theme_cowplot()

p + geom_line()

Also try:

p + geom_hline(aes(yintercept = 5000))
p + geom_vline(aes(xintercept = 2))
p + geom_smooth(method = lm)
p + geom_abline(aes(intercept = 0.5, slope = 5000))

How to combine multiple plots into a figure? (Exercise 17)

# `plot_grid()` is from `cowplot`
plot_grid(
  p, p, p, p,
  labels = c(
    "A", "B", "C", "D"
  ),
  nrow = 2
)

We have 4 legends - can they be condensed?

Yes, but it is not exactly straightforward.

need to scroll below

# fetch the legend for `p1`
legend <- get_legend(
  p + theme(legend.position = "bottom")
)

p <- p + theme(legend.position = "none")

# first `plot_grid` builds the panels
panels <- plot_grid(
  p, p, p, p,
  labels = c(
    "A", "B", "C", "D"
  ),
  nrow = 2
)

# second `plot_grid` adds the legend to the panels
plot_grid(
  panels,
  legend,
  ncol = 1,
  rel_heights = c(1, .1)
)

Saving plots (Exercise 18)

Saves last plot as 5’ x 5’ file named “plot_final.png” in working directory.

Matches file type to file extension.

ggsave(here("img/plot_final.png"), width = 5, height = 5)