Lecture 10: Styling ggplot2

Styling ggplot

• We should have a decent idea at this point of how to put a ggplot2 graph together
• But how do we make it look good?

Today!

• We’ll talk about how to set aesthetic attributes along every axis
• As well as overall styling and theming
• Highlighting, putting graphs together, making them interactive, annotating…
• Think about this stuff. Your graphs will look unprofessional if you don’t, and I’ll start grading down all-default styling or missed opportunities for improvement.

Aesthetic Characteristics

• As you’d expect, we’ll be setting the values of certain aesthetic properties
• Which ones apply depend on the geometry. See this page. Some common ones:
• color, linetype, fill, size, shape (and soon: linewidth)
• Text aesthetics: family (font), fontface, hjust, vjust (see extrafont package to use all your system fonts)

Decorating Axes vs. Decorating Geometries

• When a characteristic is set inside of an aes(), it becomes an axis and must be mapped to a variable name
• Outside of an aes() (and in the geometry), it’s a setting applied to the entire geometry

Aesthetic Characteristics

# We've seen this before
mtcars <- mtcars %>%
  mutate(Transmission = factor(am, labels = c('Automatic','Manual')))
ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission)) + 
  geom_point()

Aesthetic Characteristics

ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission, 
                   size = wt, shape = Transmission)) + 
  geom_point()

Aesthetic Characteristics

ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission, 
                   size = wt)) + 
  geom_point(shape = 10)

Aesthetic Characteristics

ggplot(mtcars, aes(x = Transmission, fill = factor(cyl))) + 
  geom_bar(position = 'dodge', linetype = 'dashed', color = 'black')

Coordinates

• Everything behind the geometries can be manipulated as well
• For example, we can change the coordinate system (usually to coord_flip it for bar graphs)

Coordinates

library(ggalt)
mtcars2 <- mtcars %>% group_by(Transmission) %>% summarize(count = n())
ggplot(mtcars2, aes(x = Transmission,y=count)) + geom_lollipop()

Coordinates

# geom_lollipop in particular has a 'horizontal' option anyway
# so this is just for demonstration
ggplot(mtcars2, aes(x = Transmission,y=count)) + 
  geom_lollipop(color = 'red', size = 2) + coord_flip()

Axes

• We can control axis text with axis.text and the ticks we mark on the scale
• element_text provides text objects

ggplot(mtcars2, aes(x = Transmission,y=count)) + 
  geom_lollipop(color = 'red', size = 2) + coord_flip() + 
  labs(x = '', y = '') + 
  scale_y_continuous(breaks = c(10,20), limits = c(0,20)) + 
  theme(axis.text.x = element_text(size = 12,family='serif'),
        axis.text.y = element_text(size = 16,family='serif', angle = 10))

Axes

Axes

• Order for discrete axes can be changed with limits

ggplot(mtcars2, aes(x = Transmission,y=count)) + 
  geom_lollipop(color = 'red', size = 2) + coord_flip() +
  scale_x_discrete(limits = c("Manual", "Automatic"))

Theming

• Pretty much all elements of presentation that aren’t in a geometry can be controlled with theme()
• The options are endless! See help(theme)
• We set different aspects of the theme using element_ functions like element_text(), element_line(), element_rect() which take aesthetic settings like size, color, etc.

Axes

• Change axis and tick lines with axis.line and element_line
• Most everything can be changed generally (axis.line or even line) or specifically (axis.ticks.x)

ggplot(mtcars2, aes(x = Transmission,y=count)) + 
  geom_lollipop(color = 'red', size = 2) + coord_flip() + 
  labs(x = '', y = '') + 
  scale_y_continuous(breaks = c(10,20), limits = c(0,20)) + 
  theme(axis.line = element_line(color = 'red'),
        axis.ticks.x = element_line(size = 5))

Axes

Background

• Work on the panel to change what goes behind that geometry! element_rect might come up!
• Pretty much ANY element can be eliminated with element_blank()
• This gonna be ugly

ggplot(mtcars2, aes(x = Transmission,y=count)) + 
  geom_lollipop(color = 'red', size = 2) + coord_flip() + 
  theme(panel.background = element_rect(color = 'blue', fill = 'yellow'),
        panel.grid.major.x = element_line(color = 'green'),
        panel.grid.major.y = element_blank())

Background

Legends

• We showed a bit last time how legends can be changed.
• Note in element_text (and elsewhere, like annotation or geom_text), hjust and vjust align text horizontally and vertically. Set 0/1 for L/R or Top/Bottom
• legend.position is in portion of the frame. .8 = 80% to the right.

ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission)) + geom_point() +
  theme(legend.text = element_text(hjust = .5, family = 'serif'),
        legend.title = element_text(hjust = 1, family = 'serif', face = 'bold'),
        legend.background = element_rect(color = 'black', fill = 'white'),
        legend.position = c(.8,.7))

Legends

Themes

• The theme is immensely flexible. We didn’t even cover everything, like the plot title/caption
• You can save your theme() settings as an object to use repeatedly as a house style
• Or use a prepackaged one! You can even tack on more theme() customization afterwards.
• Many theme_ settings you can tack on. I only use a few…
• (many many others… want to look like FiveThirtyEight or The Economist? Check ggthemes)

Prepackaged Themes: Default

ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission)) + geom_point()

Prepackaged Themes: Classic

ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission)) + geom_point() +
  theme_classic()

Prepackaged Themes: void (for things like flowcharts)

ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission)) + geom_point() +
  theme_void()

Prepackaged Themes: tufte (in ggthemes)

library(ggthemes)
ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission)) + geom_point() +
  theme_tufte()

Prepackaged Themes: theme_pubr (in ggpubr), my go-to

ggplot(mtcars, aes(x = mpg, y = hp, color = Transmission)) + geom_point() +
  theme_pubr()

General Theming Notes

• How can we think about theming generally?
• We want, as always, to drive focus towards our story
• This means not making the theme distracting it, but using it to drive attention to what we want
• Need focus on the x-axis labels? Make em bold!
• Often we don’t need distracting background color (as there is in the default theme). Usually get rid of it!

Other Styling

• Let’s move on to some other ways we can build focus and clarity
• Highlight and annotations!

Highlighting

• Highlight certain values and gray others with the gghighlight package

library(gghighlight); data(gapminder, package = 'gapminder')
ggplot(gapminder, aes(x = year, y = lifeExp, color=country)) + geom_line(size = 1.5) + 
  labs(x = NULL, y = "Life Expectancy", title = "North America Only") + 
  scale_x_continuous(limits=c(1950,2015),
                     breaks = c(1950,1970,1990,2010))+
  gghighlight(country %in% c('United States','Canada','Mexico'),
              unhighlighted_params = aes(size=.1), 
              label_params=list(direction='y',nudge_x=10)) + 
  theme_minimal(base_family='serif') 

Highlighting

Highlighting

• Or do it manually with scale_X_manual()

gapminder %>% mutate(color_name = ifelse(country %in% c('United States','Canada','Mexico'), as.character(country), 'Other')) %>%
ggplot(aes(x = year, y = lifeExp, group = country, color=color_name, size = color_name, alpha = color_name)) + geom_line() + 
  geom_text(aes(label = ifelse(year == 2007 & color_name != 'Other', color_name,'')), 
                           hjust = 0, size = 13/.pt) +
  labs(x = NULL, y = "Life Expectancy", title = "North America Only") + 
  scale_x_continuous(limits=c(1950,2025),
                     breaks = c(1950,1970,1990,2010))+
  scale_color_manual(values = c('red','forestgreen','gray','blue')) + 
  scale_size_manual(values = c(1.5,1.5,.1,1.5)) +
  scale_alpha_manual(values = c(1,1,.2,1)) +
  theme_minimal(base_family='serif') + 
  guides(color = 'none', size = 'none', alpha = 'none')

Highlighting

Annotation

• There are also many options for annotating our data
• For direct data point annotation, especially of line graphs, you can do this with some data manipulation and geom_text (see previous slide)
• For putting a block of text I recommend ggannotate
• I also recommend looking into ggtext which allows rich-text formatting of all text on the graph, but we won’t be covering it here.

Direct Line Labels

p <- gapminder %>%
  group_by(country) %>%
  mutate(country_label = ifelse(year == max(year), as.character(country), NA_character_)) %>%
  filter(country %in% c('United States','Canada','Mexico')) %>%
  ggplot(aes(x = year, y = lifeExp, color=country)) + geom_line(size = 1.5) + 
  scale_x_continuous(limits = c(1950,2025))+ theme_classic()+guides(color='none')+
  geom_text(aes(x = year + 2,label = country_label), hjust = 0)
p

Overall Annotation

• You can use the regular ggplot2 annotate() function, but this takes some work
• ggannotate will let you work hands-on with it, and it gives you code at the end
• Also works for adding lines and arrows
• The geom_mark functions from ggforce are good for shape highlights (here)
• Excel-esque but the end result is more rigorous
• Install with remotes::install_github ("mattcowgill/ggannotate"), not install.packages()

ggannotate

• Let’s see what happens when we run this…

# Remember we created p a few slides ago
ggannotate::ggannotate(p)

ggannotate

• We get code chunks to add to our graph to get our annotations

Export

• Alright, we’ve crafted our perfect graph
• What do we do now?
• ggsave() will save our result to file (or we can Export Image in the Plots frame of RStudio)
• But we have some more options still!

Sticking Graphs Together

• If you have multiple plots that go together, consider using patchwork to put them all into one
• With the library loaded, you can just add plots together and they get stitched!
• Let’s stitch together some of the plots from this presentation
• + to stick together, | to put “beside”, - to move to next column, / for the next row

Patchwork

library(patchwork)
# Named and saved these earlier
(p1 | p2 - p3)/p4

Practice

• We may not have time today to practice much of this
• But I want you to take a basic graph, a real basic one, like this:
• ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point()
• And just see how you can make it look. Mess around with it
• The only goal is to try stuff out.