The ggplot2 Plotting System - Part 2

• An implementation of the Grammar of Graphics by Leland Wilkinson
• Grammar of graphics represents and abstraction of graphics ideas/objects
• Think “verb”, “noun”, “adjective” for graphics
• Allows for a “theory” of graphics on which to build new graphics and graphics objects

• A data frame
• aesthetic mappings: how data are mapped to color, size
• geoms: geometric objects like points, lines, shapes.
• facets: for conditional plots.
• stats: statistical transformations like binning, quantiles, smoothing.
• scales: what scale an aesthetic map uses (example: male = red, female = blue).
• coordinate system

• When building plots in ggplot2 (rather than using qplot) the “artist’s palette” model may be the closest analogy
• Plots are built up in layers
  • Plot the data
  • Overlay a summary
  • Metadata and annotation

• Mouse Allergen and Asthma Cohort Study
• Baltimore children (age 5-17)
• Persistent asthma, exacerbation in past year
• Does BMI (normal vs. overweight) modify the relationship between PM\(_{2.5}\) and asthma symptoms?

library(ggplot2)
qplot(logpm25, NocturnalSympt, data = maacs, facets = . ~ bmicat, 
      geom = c("point", "smooth"), method = "lm")

head(maacs)

  logpm25        bmicat NocturnalSympt logno2_new
1  1.5362 normal weight              1      1.299
2  1.5905 normal weight              0      1.295
3  1.5218 normal weight              0      1.304
4  1.4323 normal weight              0         NA
5  1.2762    overweight              8      1.108
6  0.7139    overweight              0      0.837

g <- ggplot(maacs, aes(logpm25, NocturnalSympt))
summary(g)

data: logpm25, bmicat, NocturnalSympt, logno2_new [554x4]
mapping:  x = logpm25, y = NocturnalSympt
faceting: facet_null() 

g <- ggplot(maacs, aes(logpm25, NocturnalSympt))
print(g)

Error: No layers in plot

g <- ggplot(maacs, aes(logpm25, NocturnalSympt))
g + geom_point()

g + geom_point() + geom_smooth()
g + geom_point() + geom_smooth(method = "lm")

g + geom_point() + facet_grid(. ~ bmicat) + geom_smooth(method = "lm")

• Labels: xlab(), ylab(), labs(), ggtitle()
• Each of the “geom” functions has options to modify
• For things that only make sense globally, use theme()
  • Example: theme(legend.position = "none")
• Two standard appearance themes are included
  • theme_gray(): The default theme (gray background)
  • theme_bw(): More stark/plain

g + geom_point(color = "steelblue", size = 4, alpha = 1/2)
g + geom_point(aes(color = bmicat), size = 4, alpha = 1/2)

g + geom_point(aes(color = bmicat)) + labs(title = "MAACS Cohort") + 
  labs(x = expression("log " * PM[2.5]), y = "Nocturnal Symptoms")

g + geom_point(aes(color = bmicat), size = 2, alpha = 1/2) + 
  geom_smooth(size = 4, linetype = 3, method = "lm", se = FALSE)

g + geom_point(aes(color = bmicat)) + theme_bw(base_family = "Times")

testdat <- data.frame(x = 1:100, y = rnorm(100))
testdat[50,2] <- 100  ## Outlier!
plot(testdat$x, testdat$y, type = "l", ylim = c(-3,3))

g <- ggplot(testdat, aes(x = x, y = y))
g + geom_line()

g + geom_line() + ylim(-3, 3)
g + geom_line() + coord_cartesian(ylim = c(-3, 3))

• How does the relationship between PM\(_{2.5}\) and nocturnal symptoms vary by BMI and NO\(_2\)?
• Unlike our previous BMI variable, NO\(_2\) is continuous
• We need to make NO\(_2\) categorical so we can condition on it in the plotting
• Use the cut() function for this

## Calculate the tertiles of the data
cutpoints <- quantile(maacs$logno2_new, seq(0, 1, length = 4), na.rm = TRUE)

## Cut the data at the tertiles and create a new factor variable
maacs$no2tert <- cut(maacs$logno2_new, cutpoints)

## See the levels of the newly created factor variable
levels(maacs$no2tert)

[1] "(0.378,1.2]" "(1.2,1.42]"  "(1.42,2.55]"

## Setup ggplot with data frame
g <- ggplot(maacs, aes(logpm25, NocturnalSympt))

## Add layers
g + geom_point(alpha = 1/3) + 
  facet_wrap(bmicat ~ no2tert, nrow = 2, ncol = 4) + 
  geom_smooth(method="lm", se=FALSE, col="steelblue") + 
  theme_bw(base_family = "Avenir", base_size = 10) + 
  labs(x = expression("log " * PM[2.5])) + 
  labs(y = "Nocturnal Symptoms") + 
  labs(title = "MAACS Cohort")

• ggplot2 is very powerful and flexible if you learn the “grammar” and the various elements that can be tuned/modified
• Many more types of plots can be made; explore and mess around with the package (references mentioned in Part 1 are useful)