ggplot2_2_chapter1.pdf

chapter 1

1.1 Stats with geoms

• Called from within a geom
• Called independently

p <- ggplot(iris, aes(x = Sepal.Width)) 
p + geom_histogram()
p + stat_bin()
p <- ggplot(mtcars, aes(x = factor(cyl), fill = factor(am))) 
p + geom_bar() 
p + stat_count()

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + 
  geom_point() + 
  geom_smooth()
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + 
  geom_point() + 
  geom_smooth(se = FALSE, span = 0.4)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + 
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + 
  geom_point() + 
  geom_smooth(method = "lm", fullrange = TRUE)

# Amend the plot to add another smooth layer with dummy grouping
ggplot(mtcars, aes(x = wt, y = mpg, color = fcyl)) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE) + 
  stat_smooth(aes(group=1),method="lm",se=FALSE)
# # Amend the plot
ggplot(mtcars, aes(x = wt, y = mpg, color = fcyl)) +
  geom_point() +
  # Map color to dummy variable "All"
  stat_smooth(aes(color="All"),se = FALSE) +
  stat_smooth(method = "lm", se = FALS

1.2 Stats: sum and quantile

# Low precision (& integer) data
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) 
p + geom_point()
# Jittering may give a wrong impressions
p + geom_jitter(alpha = 0.5, width = 0.1, height = 0.1)
p + geom_count()
p + stat_sum()

library(AER) 
data(Journals) 
p <- ggplot(Journals, aes(log(price/citations), log(subs))) + 
        geom_point(alpha = 0.5)
# Using geom_quantiles
p + geom_quantile(quantiles = c(0.05, 0.50, 0.95))