collapse = TRUE,
  comment = "#>",
  fig.path = "README-"


A pirate plot is a way of displaying data where a continuous dependent variable is a function of a categorical independent variable, in a more informative way than the traditional barplot. geom_pirate() plots the raw data as points (using ggplot2::geom_jitter()), along with layers showing descriptive and inferential statistics -- bars indicating means (using ggplot2::geom_col()), horizontal line indicating means (using ggplot2::geom_crossbar()), boxes indicating 95\% confidence intervals assuming a normal sampling distribution (using ggplot2::geom_tile()), and violins indicating the density (using ggplot2::geom_violin()).


You can install ggpirate from github with:

# install.packages("devtools")



Colour pirate plot:

ggplot(mpg, aes(x = class, y = cty)) +
  geom_pirate(aes(colour = class, fill = class))

Each of the layers can be turned off, e.g. for just means and confidence intervals:

ggplot(mpg, aes(x = class, y = cty)) +
  geom_pirate(aes(colour = class),
              points = FALSE, bars = FALSE, violins = FALSE)

Colour can be mapped to a different variable than the x axis, resulting in dodged subgroups:

mpg2 <- dplyr::mutate(mpg, drv = factor(drv))

ggplot(mpg2, aes(x = class, y = cty)) +
  geom_pirate(aes(colour = drv, fill = drv), show.legend = TRUE)

And it plays well with facetting:

mpg_gather <- tidyr::gather(mpg, type, mileage, cty, hwy)

ggplot(mpg_gather, aes(x = class, y = mileage)) +
  facet_wrap(~type) +
  geom_pirate(aes(colour = class, fill = class))

Finally, the parameters passed to any of the layers can be adjusted, e.g.:

ggplot(mpg, aes(x = class, y = cty)) +
  geom_pirate(aes(colour = class), bars = FALSE,
              points_params = list(shape = 19, alpha = 0.2),
              lines_params = list(size = 0.8))

mikabr/ggpirate documentation built on Sept. 23, 2018, 12:58 a.m.