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`

.

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")
```

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.

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
```

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)
})
}
```

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.

Link: https://www.kaggle.com/eliasdabbas/search-engine-results-flights-tickets-keywords under License CC0

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