ggplot2 to allow the user to make a quantile-quantile plot with a big dataset. Specifically,
geom_big_qq uses all the data provided to calculate quantiles, but drops points that would overplot before plotting. In this way, the resultant figure maintains all the accuracy of a Q-Q plot made with a large dataset, but renders as fast as one from a smaller dataset and, when stored as a vector graphic, has the file size of a Q-Q plot from a smaller dataset.
Here’s an example where
geom_qq takes 14 seconds to render the plot on my intel i5 and
geom_big_qq takes 1 second to produce the same plot.
set.seed(27599) d <- data.frame(s = runif(n = 5e5)) # # takes 14 seconds # d %>% # ggplot(mapping = aes(sample = s)) + # geom_qq(distribution = qunif) + # QQ_scale_x() + # QQ_scale_y() # takes 1 second d %>% ggplot(mapping = aes(sample = s)) + geom_QQ_unif() + scale_x_QQ() + scale_y_QQ() + theme_minimal()
geom works with other aesthetics, too.
set.seed(27599) n <- 5e5 d <- data.frame(fac1 = sample(x = LETTERS[1:3], size = n, replace = TRUE), fac2 = sample(x = LETTERS[1:3], size = n, replace = TRUE), s = runif(n = n)) # takes 1 second d %>% ggplot(mapping = aes(sample = s, color = fac1)) + geom_QQ_unif() + facet_wrap(~ fac2) + scale_x_QQ() + scale_y_QQ() + theme_minimal()