# Using a multivariate normal to draw a flower in ggplot2

Here’s a dumb thing I did over lunch; as I was playing around with the geom_density_2d and random draws from the standard multivariate normal distribution, I realized that the outcome looked a bit like the flower of a rose. So I figured I would try to see how difficult it would be to draw the full rose using ggplot2. So here goes.

knitr::opts_chunk$set(dev = "svg") library(tidyverse) We start with the stem. We’ll just draw this as a curved line, so geom_curve suits our purposes perfectly. p <- ggplot() + coord_equal(1, c(-4, 2), c(-7, 3)) + geom_curve(aes(x = -1, y = -7, xend = 0, yend = 0), ncp = 1000, curvature = -0.3, size = 1, color = "olivedrab3") p Next, we want to draw two leaves on the stem. Since we want to fill these in with a color, we’ll have to use geom_polygon. The solution I came up with is very awkward, and wouldn’t really generalize to drawing many different types of leaves, but it’s good enough for what we’re trying to achieve. (A better approach might use the bezier package.) geom_leaf <- function(x, xend, f, xoffset = 0, yoffset = 0, xflip = 1, yflip = 1, ...) { .x <- seq(x, xend, length.out = 100) .y <- f(.x) df <- tibble(x = c(.x, .y), y = c(.y, .x)) df$x <- xflip * df$x + xoffset df$y <- yflip * df\$y + yoffset

geom_polygon(aes(x = x, y = y), data = df, ...)
}

The primary inputs are start- and end-points on the x-axis, and a function f to be evaluated over that domain. Together with some parameters for moving and flipping around the leaves, it then evaluates the function and returns a geom_polygon representing the flower. Here it is in action:

f <- function(x) x^2 / 2

p <- p +
geom_leaf(0, 2, f, -1.6, -4.5, 1,
fill = "olivedrab3", color = "palegreen") +
geom_leaf(0, 2, f, -1.6, -5,  -1,
fill = "olivedrab3", color = "palegreen")
p

Finally, we create the flower by drawing n samples from a standard multivariate normal distribution and passing these to stat_density_2d.

geom_rose <- function(n, mean = c(0, 0), ...) {

.x <- mvtnorm::rmvnorm(n, mean)
df <- tibble(x = .x[, 1], y = .x[, 2])

list(
stat_density_2d(
aes(x = x, y = y, fill = calc(level)), data = df,
geom = "polygon", show.legend = FALSE, color = "grey80"),
}
Adding theme_void gives us the final result.
p +
theme_void()