21 Appendix: ggplot tips

Getting Started

ggplot2 and the Grammar of Graphics behind it is an incredible resource for data visualization.

Looking for extra information on making the perfect visualization? Consider these helpful resources, and see below for extra Learning Checks!

21.0.1 Resources

21.0.2 Our Packages

Be sure to load these packages before proceeding!

library(dplyr) # for data wrangling
library(ggplot2) # for data visualization

21.0.3 Our Data

These exercises use the diamonds dataset from the ggplot2 package.

# Import the diamonds dataset from the ggplot2 package!
diamonds = ggplot2::diamonds
# Let's glimpse it!
diamonds %>% glimpse()
## Rows: 53,940
## Columns: 10
## $ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…
## $ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…
## $ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …
## $ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…
## $ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…
## $ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…
## $ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…
## $ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…
## $ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…



21.1 Flipping Axes

Learning Check 1

Question

Sometimes, the names of categories won’t fit well. We can try the following. Compare these two plots. What did we do?

library(ggplot2)
diamonds = ggplot2::diamonds
# Plot 1
gg1 = gg1 = ggplot(data = diamonds, mapping = aes(x = cut, y = price, 
                                      group = cut, fill = cut)) +
  geom_boxplot()
# Plot2
ggplot(data = diamonds, mapping = aes(x = cut, y = price, 
                                      group = cut, fill = cut)) +
  geom_boxplot() + 
  coord_flip()
[View Answer!]

We used coord_flip() to flip the coordinates of the x and y axis, which gives our cut labels more room!




21.2 Legend Positioning

Learning Check 8

Question

Sometimes, the legend doesn’t fit well. We can try this:

What happens when you change legend.position from "right" to "bottom" to "left" to "top"?

library(ggplot2)
diamonds = ggplot2::diamonds
ggplot(data = diamonds, mapping = aes(x = cut, y = price,
  group = cut, fill = cut)) +
  geom_boxplot() + coord_flip() +
  theme(legend.position = "bottom")
ggplot(data = diamonds, mapping = aes(x = cut, y = price,
  group = cut, fill = cut)) +
  geom_boxplot() + coord_flip() +
  theme(legend.position = "right")
ggplot(data = diamonds, mapping = aes(x = cut, y = price, 
  group = cut, fill = cut)) +
  geom_boxplot() + coord_flip() +
  theme(legend.position = "left")
ggplot(data = diamonds, mapping = aes(x = cut, y = price, 
  group = cut, fill = cut)) +
  geom_boxplot() + coord_flip() +
  theme(legend.position = "top")
[View Answer!]

These four values for legend.position will relocate the fill legend (and any other legends) to be at the top, bottom, left, or right of the visual!

21.3 Breaking Up a Visual

Finally, we might want to break up our visual into multiple parts. We can use facet_wrap to do this, but how exactly does it work? Try the learning check below.

Learning Check 10

Question

What changed in the code below, and what did it result in?

library(ggplot2)
diamonds = ggplot2::diamonds
ggplot(data = diamonds, mapping = aes(x = price, fill = cut)) +
  geom_histogram(color = "white") + 
  facet_wrap(~cut) + # must be categorical variable
  labs(x = "Price (USD)",
       y = "Frequency of Price (Count)",
       title = "US Diamond Sales")
[View Answer!]

This visual split up our histograms into separate panels (making it much more readable), and easier to compare distributions. We write facet_wrap(~ before the variable name (eg. cut) to specify that we want to split up the data by the values of cut. This sorts our rows of data into 5 different piles (since there are 5 different categories in cut) and makes a panel out of each.