# Generate data: means and standard errors of means for prices
# for each type of cut
dmod <- lm(price ~ cut, data = diamonds)
cuts <- data.frame(cut = unique(diamonds$cut), predict(dmod, data.frame(cut =
unique(diamonds$cut)), se = TRUE)[c("fit", "se.fit")])
se <- ggplot(cuts, aes(x = cut, y = fit, ymin = fit - se.fit,
ymax = fit + se.fit, colour = cut))
se + geom_pointrange()
# Boxplot with precomputed statistics
# generate sample data
library(plyr)
abc <- adply(matrix(rnorm(100), ncol = 5), 2, quantile, c(0, .25, .5, .75, 1))
b <- ggplot(abc, aes(x = X1, ymin = "0%", lower = "25%",
middle = "50%", upper = "75%", ymax = "100%"))
b + geom_boxplot(stat = "identity")
# Using annotate
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
p + annotate("rect", xmin = 2, xmax = 3.5, ymin = 2, ymax = 25,
fill = "dark grey", alpha = .5)
# Geom_segment examples
library(grid)
p + geom_segment(aes(x = 2, y = 15, xend = 2, yend = 25),
arrow = arrow(length = unit(0.5, "cm")))
p + geom_segment(aes(x = 2, y = 15, xend = 3, yend = 15),
arrow = arrow(length = unit(0.5, "cm")))
p + geom_segment(aes(x = 5, y = 30, xend = 3.5, yend = 25),
arrow = arrow(length = unit(0.5, "cm")))
# You can also use geom_segment to recreate plot(type = "h") :
counts <- as.data.frame(table(x = rpois(100, 5)))
counts$x <- as.numeric(as.character(counts$x))
with(counts, plot(x, Freq, type = "h", lwd = 10))
qplot(x, Freq, data = counts, geom = "segment", yend = 0, xend = x,
size = I(10))Run the code above in your browser using DataCamp Workspace