# gghilbertstrings

A Hilbert curve (also known as a Hilbert space-filling curve) is a continuous fractal space-filling curve first described by the German mathematician David Hilbert in 1891, as a variant of the space-filling Peano curves discovered by Giuseppe Peano in 1890 (from Wikipedia).

This package provides an easy access to using Hilbert curves in `ggplot2`.

## Installation

You can install the released version of gghilbertstrings from CRAN with:

``install.packages("gghilbertstrings")``

You can install the development version from GitHub with:

``````# install.packages("remotes") # run only if not installed
remotes::install_github("Sumidu/gghilbertstrings")``````

## Usage

The `gghilbertstrings` package comes with functions for fast plotting of Hilbert curves in ggplot. At it’s core is a fast RCpp implementation that maps a 1D vector to a 2D position.

The `gghilbertplot` function creates a Hilbert curve and plots individual data points to the corners of this plot. It automatically rescales the used `ID`-variable to the full range of the Hilbert curve. The method also automatically picks a suitable level of detail able to represent all values of `ID`.

The following figure shows different hilbert curves for different maximum `ID`s. ### Plotting random data

The most simple way to plot data is to generate an `id` column that ranges from 1 to n, where n is the largest value to use in the Hilbert curve. Beware: The `id`s are rounded to integers.

``````library(gghilbertstrings)

# val is the ID column used here
df <- tibble(val = 1:256,
size = runif(256, 1, 5),        # create random sizes
color = rep(c(1,2,3,4),64))     # create random colours

gghilbertplot(df, val,
color = factor(color), # render color as a factor
size = size,
add_curve = T)         # also render the curves`````` ### Performance

We run the creation of a coordinate system 10 times. This means creating 1 entry for every possible corner in the Hilbert Curve.

``````library(microbenchmark)
library(HilbertCurve)
library(tidyverse)
library(gghilbertstrings)
mb <- list()
for (i in 1:10) {
df <- tibble(val = 1:4^i,
size = runif(4^i, 1, 5),
# create random sizes
color = rep(c(1, 2, 3, 4), 4^(i - 1)))
values <- df\$val
mb[[i]] <- microbenchmark(times = reps,
HilbertCurve = {
hc <- HilbertCurve(1, 4^i, level = i, newpage = FALSE)
},
gghilbertstrings = {
ggh <- hilbertd2xy(n = 4^i, values)
})
}`````` ### Useful example

We use the `eliasdabbas/search-engine-results-flights-tickets-keywords` data set on Kaggle as an example for a simple analysis. We map the full search URLs to the Hilbert curve and then add points when the URL was present for a specific search term. By comparing resulting facets we can see systematic difference in which provides show up for which search term. 