R Bootcamp - Day 4

ggplot2

Jay Hesselberth

RNA Bioscience Initiative | CU Anschutz

2025-09-06

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)

ggplot2

ggplot2 is based on the “grammar of graphics”, the idea that you can build every graph from the same components: a data set, a coordinate system, and “geoms” - visual marks that represent data points.

Today’s datasets

In this class, we will use a data set from ggplot2: diamonds contains thousands of gem prices and qualities.

There are many interesting data sets you can install as R packages for learning to manipulate and plot data:

• babynames
• gapminder
• palmerpenguins

Getting familiar with the data - Exercise 1

summary(diamonds)

     carat               cut        color        clarity          depth      
 Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065   Min.   :43.00  
 1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258   1st Qu.:61.00  
 Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194   Median :61.80  
 Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171   Mean   :61.75  
 3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066   3rd Qu.:62.50  
 Max.   :5.0100                     I: 5422   VVS1   : 3655   Max.   :79.00  
                                    J: 2808   (Other): 2531                  
     table           price             x                y         
 Min.   :43.00   Min.   :  326   Min.   : 0.000   Min.   : 0.000  
 1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710   1st Qu.: 4.720  
 Median :57.00   Median : 2401   Median : 5.700   Median : 5.710  
 Mean   :57.46   Mean   : 3933   Mean   : 5.731   Mean   : 5.735  
 3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540   3rd Qu.: 6.540  
 Max.   :95.00   Max.   :18823   Max.   :10.740   Max.   :58.900  
                                                                  
       z         
 Min.   : 0.000  
 1st Qu.: 2.910  
 Median : 3.530  
 Mean   : 3.539  
 3rd Qu.: 4.040  
 Max.   :31.800  
                 

Also examine:

?diamonds
head(diamonds)
glimpse(diamonds)
View(diamonds)

The syntax of ggplot()

ggplot() builds plots piece by piece.

ggplot divides a plot into three different fundamental parts:

plot = data + coordinate-system + geometry.

aesthetics map variables in the data to visual properties of the geom like size, color, and x and y locations.

Making a plot step-by-step (Exercise 2)

Initialize a plot with data.

ggplot(data = diamonds)

Next, specify the coordinate system.

ggplot(
  data = diamonds,
  mapping = aes(
    x = carat,
    y = price
  )
)

Add a geom (geom_point).

ggplot(
  data = diamonds,
  mapping = aes(
    x = carat,
    y = price
  )
) +
  geom_point()

Map aesthetics to other variables.

ggplot(
  data = diamonds,
  mapping = aes(
    x = carat,
    y = price,
    color = cut,
    size = carat
  )
) +
  geom_point()

Reduce overplotting by adjusting the transparency of points.

ggplot(
  data = diamonds,
  mapping = aes(
    x = carat,
    y = price,
    color = cut,
    size = carat
  )
) +
  geom_point(alpha = 0.2)

Looking under the hood of ggplot (Exercise 3)

p <- ggplot(
  data = diamonds,
  mapping = aes(
    x = carat,
    y = price,
    color = cut,
    size = carat
  )
) +
  geom_point(alpha = 0.2)

summary(p)

Looking under the hood of ggplot (Exercise 3)

data: carat, cut, color, clarity, depth, table, price, x, y, z
  [53940x10]
mapping:  x = ~carat, y = ~price, colour = ~cut, size = ~carat
faceting: <ggproto object: Class FacetNull, Facet, gg>
    compute_layout: function
    draw_back: function
    draw_front: function
    draw_labels: function
    draw_panels: function
    finish_data: function
    init_scales: function
    map_data: function
    params: list
    setup_data: function
    setup_params: function
    shrink: TRUE
    train_scales: function
    vars: function
    super:  <ggproto object: Class FacetNull, Facet, gg>
-----------------------------------
geom_point: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity 

ggplot is powerfully simple for making complex plots

ggplot(
  diamonds,
  aes(x = carat)
) +
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Note you can drop the data and mapping specifications, as ggplot expects these as the first two arguments. See ?ggplot.

Why can’t I just do this?

hist(diamonds$carat)

You can. But the advantage of ggplot is that it is equally “simple” to make basic and complex plots.

The underlying grammar lets you exquisitely customize the appearance of your plot and easily generate reproducible & publishable figures.

Creating more complex plots

Geom functions

• Use a geom function to represent data points, use the geom aesthetic properties to represent variables.
• Each function returns a plot layer.
• There are many geoms in ggplot that are specific to plots with 1, 2, or 3 variables

Geom functions for one variable - Exercise 4

Make a bar plot.

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

Update the bar plot aesthetics.

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

Change to a density plot.

ggplot(
  data = diamonds,
  mapping = aes(x = carat)
) +
  geom_density()

Color the density plot.

ggplot(
  data = diamonds,
  mapping = aes(x = carat)
) +
  geom_density(
    fill = "tomato1"
  )

Plot subsets by mapping fill to cut

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

Use ggridges to plot staggered subsets.

https://wilkelab.org/ggridges/

ggplot(
  data = diamonds,
  mapping = aes(
    x = carat,
    y = cut,
    fill = cut
  )
) +
  # geom from `ggridges`
  geom_density_ridges()

Picking joint bandwidth of 0.0647

Geom functions for two variables

With two variables, depending on the nature of the data, you can have different kinds of geoms:

• discrete x, continuous y
• continuous x, continuous y
• continuous bivariate
• & others (check out the cheatsheet!)

discrete x, continuous y - Exercise 5

Make a column plot.

ggplot(
  data = diamonds,
  mapping = aes(
    x = cut,
    y = price
  )
) +
  geom_col()

Same data with a box plot.

ggplot(
  data = diamonds,
  mapping = aes(
    x = cut,
    y = price
  )
) +
  geom_boxplot()

Box plot, with fill color by cut.

ggplot(
  data = diamonds,
  mapping = aes(
    x = cut,
    y = price,
    fill = cut
  )
) +
  geom_boxplot()

What about this plot is not ideal? (hint: how many ways is cut represented?)

Violin plot with fill color by cut.

ggplot(
  data = diamonds,
  mapping = aes(
    x = cut,
    y = price,
    fill = cut
  )
) +
  geom_violin()

continuous x, continuous y - Exercise 6

Subset diamonds to see points more clearly.

diamonds_subset <- diamonds |>
  sample_n(size = 1000)

Make a scatter plot.

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

Now add a smoothing line.

ggplot(
  data = diamonds_subset,
  mapping = aes(
    x = carat,
    y = price,
    color = cut
  )
) +
  geom_smooth()

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Here we can combine geoms to see points & the fit

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

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Class 4 End

continuous bivariate - Exercise 7

ggplot(
  data = diamonds_subset,
  mapping = aes(
    x = carat,
    y = price
  )
) +
  geom_point()

ggplot(
  data = diamonds_subset,
  mapping = aes(
    x = carat,
    y = price
  )
) +
  geom_hex()

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.

Note that aesthetics can also be defined within a geoms.

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

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

Dodged bars are easier to read (proportions are clearer)

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

Coordinate and Scale Functions - Exercise 11

We won’t go into these functions too much today, but here is a brief overview:

• 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(
  data = diamonds,
  mapping = aes(x = cut)
) +
  geom_bar()

Now flip the axis.

ggplot(
  data = diamonds,
  mapping = 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(
  data = 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)

Warning: Removed 275 rows containing missing values or values outside the scale range
(`geom_point()`).

You can also use coord_cartesian(xlim, ylim)

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.

Density plot we’ve seen before. Which variables can we use to subdivide the data?

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

Faceted by cut

ggplot(
  data = diamonds,
  mapping = aes(x = log(price), fill = cut)
) +
  geom_density(color = "black") +
  facet_wrap(~cut, nrow = 1)

Scatter plot with facets.

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

Change the theme with theme_bw().

ggplot(
  data = diamonds_subset,
  mapping = 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, e.g. 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 = cut,
    y = log(price),
    fill = cut
  )
) +
  geom_boxplot() +
  labs(
    x = "Cut",
    y = "Price (log)",
    color = "Cut",
    title = "Distribution of diamond prices by cut",
    subtitle = "Data come from a random sample of 1000 diamonds",
    caption = "Source: diamonds dataset from ggplot2"
  ) +
  theme_cowplot()

Additional points

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

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

p + geom_line()

p + geom_hline(aes(yintercept = 5000))

p + geom_vline(aes(xintercept = 2))

p + geom_smooth(method = lm)

`geom_smooth()` using formula = 'y ~ x'

p + geom_abline(aes(intercept = 0.5, slope = 5000))

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

plot1 <- p
plot2 <- p + geom_hline(aes(yintercept = 5000))
plot3 <- p + geom_vline(aes(xintercept = 2))
plot4 <- p + geom_abline(aes(intercept = 0.5, slope = 5000))

all_plots <- plot_grid(
  plot1,
  plot2,
  plot3,
  plot4,
  labels = c("A", "B", "C", "D"),
  nrow = 2
)
all_plots

# we have 4 legends, which is too many - can they be removed?
# Yes, but it is not exactly straightforward
legend <- get_legend(plot1 + theme(legend.position = "bottom"))

Warning in get_plot_component(plot, "guide-box"): Multiple components found;
returning the first one. To return all, use `return_all = TRUE`.

plot1 <- p + theme(legend.position = "none")
plot2 <- p +
  geom_hline(aes(yintercept = 5000)) +
  theme(legend.position = "none")
plot3 <- p + geom_vline(aes(xintercept = 2)) + theme(legend.position = "none")
plot4 <- p +
  geom_abline(aes(intercept = 0.5, slope = 5000)) +
  theme(legend.position = "none")

all_plots <- plot_grid(
  plot1,
  plot2,
  plot3,
  plot4,
  labels = c("A", "B", "C", "D"),
  nrow = 2
)
plot_final <- plot_grid(all_plots, legend, ncol = 1, rel_heights = c(1, .1))
plot_final

More information on using plot_grid (from package cowplot) is here

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)