Beautiful graphics with ggplot2

What is ggplot2?

• an R package for data visualization

• stands for “grammar of graphics”

Why ggplot2?

• Popular (well-supported, great community)

• Open source (like all of R)

• Easy to use (after a learning curve)

• Aesthetically pleasing

• Built for multi-variate data

• Reproducible figures

• You can make anything!

Goals for this wokrshop

Teach you enough that you know how to teach yourself more!

Goals for today

Teach you enough that you know how to teach yourself more!

• Introduce “grammar of graphics” structure

• Practice!
  • samples with built-in data
  • preparing data
  • samples with (more interesting?) data

• Send you away with a list of resouces (including these slides)

Code structure:

Grammar of graphics

ggplot(data = diamonds, aes(x = carat, y = price, color = clarity)) +
  geom_point() +
  facet_grid(color ~ cut)

Let’s try it!

1. Download scripts from the following site: https://is.gd/IYoXwA

2. Open RStudio.

3. In RStudio, open “install_packages.R”. Highlight the text and click “Run”.

4. Still in RStudio, open “workshop_script.R”. We’ll work from this for the rest of the presentation.

5. Load packages with the “library()” commands at the top of the script.

Okay, now let’s try it!

First, we need data.

Let’s use the built-in R dataset, “diamonds”.

Data

ggplot(data = diamonds)

Aesthetics: aes()

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

Geometric object: geom_

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

Add more aesthetics…

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

Why didn’t we do this?

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

We could get this instead:

ggplot(data = diamonds, 
       aes(x = carat, y = price)) +
  geom_point(color = "magenta")

Our original plot

(with a small difference:)

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

Add more geoms:

ggplot(data = diamonds, 
       aes(x = carat, y = price)) +
  geom_point(aes(color = clarity)) +
  geom_smooth()

Change geom appearance:

ggplot(data = diamonds, 
       aes(x = carat, y = price)) +
  geom_point(aes(color = clarity)) +
  geom_smooth(color = "black", size = 0.8, linetype = 2)

Make it look nicer:

ggplot(data = diamonds, 
       aes(x = carat, y = price)) +
  geom_point(aes(color = clarity)) +
  geom_smooth(color = "black", size = 0.8, linetype = 2) +
  theme_few()

Try facets!

ggplot(data = diamonds, 
       aes(x = carat, y = price)) +
  geom_point(aes(color = clarity)) +
  geom_smooth(color = "black", size = 0.8, linetype = 2) +
  facet_wrap(~cut)

Two-dimensional facets:

ggplot(data = diamonds, aes(x = carat, y = price, color = clarity)) +
  geom_point(size = 0.5) +
  facet_grid(color~cut)

Try out color scales

ColorBrewer is useful and popular:

ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point(aes(color = clarity)) +
  theme_few() + 
  scale_color_brewer(type = "qual", palette = "Set2")

Different plot types?

ggplot(data = diamonds, 
       aes(x = clarity, y = price)) +
  geom_violin() 

Try an extension:

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

Choose colors: scale_color_…

ggplot(data = diamonds, 
       aes(x = price, y = cut, color = cut, fill = cut)) +
  geom_density_ridges(alpha = 0.8, scale = 5) +
  scale_fill_viridis(option = "A", discrete = TRUE) +
  scale_color_viridis(option = "A", discrete = TRUE) + 
  theme_few()

Another color scale:

ggplot(data = diamonds, 
       aes(x = price, y = cut, fill = cut)) +
  geom_density_ridges(alpha = 1, scale = 5) +
  scale_fill_manual(values = wes_palette("Darjeeling")) +
  theme_few()

Sometimes we need to transform data.

kable(head(iris))

Wide or long?

iris_long <- melt(iris, id.vars = ("Species"))
kable(head(iris_long))

Plot the data:

ggplot(iris_long, aes(x = Species, y = value, fill = variable)) +
  geom_bar(stat = 'identity', width = 1) +
  theme_bw()

Change coordinates

ggplot(iris_long, 
       aes(x = Species, y = value, color = variable, fill = variable)) +
  geom_bar(stat = 'identity', width = 1) +
  coord_polar(theta = 'x') +
  theme_bw()

A more interesting dataset:

kable(head(gapminder))

A scatter plot:

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Who’s the outlier?

Who’s the outlier?

gapminder %>% filter(gdpPercap > 60000)

## # A tibble: 5 x 6
##   country continent  year lifeExp    pop gdpPercap
##    <fctr>    <fctr> <int>   <dbl>  <int>     <dbl>
## 1  Kuwait      Asia  1952  55.565 160000 108382.35
## 2  Kuwait      Asia  1957  58.033 212846 113523.13
## 3  Kuwait      Asia  1962  60.470 358266  95458.11
## 4  Kuwait      Asia  1967  64.624 575003  80894.88
## 5  Kuwait      Asia  1972  67.712 841934 109347.87

Deal with overplotting:

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_hex()

Another way to avoid overplotting:

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_density2d(aes(color = ..level..), bins = 20) +
  scale_color_viridis()

A little more complicated:

p <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = continent, size = pop), alpha = 0.8) +
  scale_x_continuous(trans = 'log') +
  facet_wrap(~year) +
  scale_color_brewer(type = "Qual", palette = "Accent") +
  theme_hc(bgc = 'darkunica') +
  theme(text = element_text(size = 9))

A little more complicated:

p

Let’s make a map!

Prepare data:

country_df <- map_data('world') %>%
  rename("country" = "region") 
country_df$country[country_df$country == "USA"] <- "United States"

#Take the mean across all years for each country:
gapminder_means <- gapminder %>% 
  group_by(country, continent) %>%
  summarise(lifeExp = mean(lifeExp),
            pop = mean(pop),
            gdpPercap = mean(gdpPercap))

plot_dat <- left_join(gapminder_means, country_df, by = "country")

Make the map:

ggplot(plot_dat) +
  geom_polygon(aes(x = long, y = lat, fill = lifeExp, group = group)) +
  scale_fill_viridis(option = "A") + 
  coord_quickmap() +
  theme_few()

More plot ideas?

• What questions could we ask with this data?

• How could we visually answer those questions?

Where to learn more:

• Stack Overflow/Google
• Books:
  • R for Data Science: http://r4ds.had.co.nz/program-intro.html
  • Ggplot2: Elegant graphics for data analysis: https://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403 (may also be available for free at github).
  • Data visualization for social science: http://socviz.co/

Where to learn more:

• Papers:
  • Wickham grammar of graphics: http://byrneslab.net/classes/biol607/readings/wickham_layered-grammar.pdf
  • Wickham tidy data: https://www.jstatsoft.org/article/view/v059i10
• Tutorials, cheatsheets, websites:
  • https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
  • http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
  • http://www.ggplot2-exts.org/gallery/

Where to learn more:

• Twitter:
  • @hadleywickham, @ClausWilke, @JennyBryan, @RLadiesGlobal, @rstudiotips

Questions?

Adrienne Marshall mars7850@vandals.uidaho.edu