A quantile-quantile plot can be used to compare a sample of p-values to the uniform distribution. But when the dataset is big (i.e. > 1e4 p-values), plotting the quantile-quantile plot can be slow. geom_QQ uses all the data to calculate the quantiles, but thins it out in a way that focuses on points near zero before plotting to speed up plotting and decrease file size, when vector graphics are stored.
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. <!-- There's no use in having ten thousand points in a plot to define a line -- we can't even see most of them! --> 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))# d %>%# ggplot(mapping = aes(sample = s)) +# geom_qq(distribution = qunif) +# QQ_scale_x() +# QQ_scale_y()# takes 1 secondd %>%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 <- 5e5d <- 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 secondd %>%ggplot(mapping = aes(sample = s, color = fac1)) +geom_QQ_unif() +facet_wrap(~ fac2) +scale_x_QQ() +scale_y_QQ() +theme_minimal()