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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "75%",
fig.retina = 2
)
library(tidyverse)
library(patchwork)
library(tibble)
library(magrittr)
library(ggplot2)
library(gghilbertstrings)
library(glue)
reps <- 10 #takes about 50 seconds on iMac i9 3.5GHz
size_exponent <- 11
```
# gghilbertstrings
<!-- badges: start -->
[](https://travis-ci.com/Sumidu/gghilbertstrings)
[](https://ci.appveyor.com/project/Sumidu/gghilbertstrings)
[](https://codecov.io/gh/Sumidu/gghilbertstrings?branch=master)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://github.com/Sumidu/gghilbertstrings/actions)
[](https://CRAN.R-project.org/package=gghilbertstrings)
<!-- badges: end -->
A [Hilbert curve](https://en.wikipedia.org/wiki/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](https://CRAN.R-project.org) with:
``` r
install.packages("gghilbertstrings")
```
You can install the development version from [GitHub](https://github.com/) with:
``` r
# 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.
```{r hilbert, echo=FALSE}
p1 <- tibble(id = c(1,4)) %>% gghilbertplot(id, add_curve = T) + ggtitle("n = 4") + theme_void()
p2 <- tibble(id = c(1,16)) %>% gghilbertplot(id, add_curve = T) + ggtitle("n = 16") + theme_void()
p3 <- tibble(id = c(1,64)) %>% gghilbertplot(id, add_curve = T) + ggtitle("n = 64") + theme_void()
p4 <- tibble(id = c(1,256)) %>% gghilbertplot(id, add_curve = T) + ggtitle("n = 256") + theme_void()
p1 + p2 + p3 + p4 + plot_annotation(title = "Different depths of Hilbert curves")
```
### 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.
```{r example}
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 `r reps` times. This means creating 1 entry for every possible corner in the Hilbert Curve.
```r
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)
})
}
```
```{r setupmb, eval=TRUE, include=FALSE}
# Performance benchmark
library(microbenchmark)
remotes::install_github("jokergoo/HilbertCurve")
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$val
mb[[i]] <- microbenchmark(times = reps,
HilbertCurve = {
hc <- HilbertCurve(1, 4^i, level = i, newpage = FALSE)
},
gghilbertstrings = {
ggh <- hilbertd2xy(n = 4^i, values)
})
}
res <- data.frame()
for (i in 1:length(mb)) {
tmp <- mb[[i]] %>% as_tibble() %>% mutate(depth = i)
res <- res %>% bind_rows(tmp)
}
```
```{r output, echo=FALSE, message=FALSE, warning=FALSE}
library(scales)
res %>% mutate(time = time/1000) %>%
ggplot() +
aes(x = depth, y = time, color = expr) +
geom_jitter(width = 0.1, height = 0, alpha = 0.2) +
geom_smooth() +
scale_y_log10(labels = comma) +
scale_x_continuous(breaks = 3:14, minor_breaks = NULL) +
scale_color_viridis_d(begin = 0.5, end = 0.8, option = "D") +
labs(x = "Order of Hilbert Curve", y = "Time in ms (log-scale)", color = "Package",
title = "This package is two orders of magnitute faster",
subtitle = glue("Comparison of {reps} repetitions across all orders."),
caption = "Order 14 means 268,435,456 coordinates")
```
### Useful example
We use the `eliasdabbas/search-engine-results-flights-tickets-keywords` data set on [Kaggle](https://www.kaggle.com/eliasdabbas/search-engine-results-flights-tickets-keywords) as an example for a simple analysis. We map the full search URLs to the Hilbert curve and then add points when the URL was present for a specific search term. By comparing resulting facets we can see systematic difference in which provides show up for which search term.
```{r example_flt, echo=FALSE, message=FALSE, warning=FALSE}
#library(kaggler)
#kgl_auth() # requires a kaggle token.
data_set <- "eliasdabbas/search-engine-results-flights-tickets-keywords"
#refs <- kgl_datasets_list(owner_dataset = data_set)
#refs$datasetFiles
#kgl_datasets_download(owner_dataset = data_set,
# fileName = refs$datasetFiles$name[1], datasetVersionNumber = 14)
read_plus <- function(flnm) {
read_csv(flnm) %>%
mutate(filename = flnm)
}
# files must be added in the my_tests folder for this section to work.
df_with_sources <-
list.files(path = "my_tests",
pattern = "*.csv",
full.names = T) %>%
map_df(~read_plus(.))
comparison <- df_with_sources$searchTerms %>% unique() %>% head(4)
df_with_sources %>%
create_id_column(link) %>%
filter(searchTerms %in% comparison) %>%
gghilbertplot(gghid,
color = displayLink,
size = rank,
label = displayLink,
alpha = 0.1,
jitter = 0.1,
add_curve = F,
curve_alpha = 0.1) +
aes(group = displayLink) +
facet_wrap(~searchTerms) +
labs(title = "Comparsion of domains shown for different search queries") +
guides(color = FALSE) -> p
p
#plotly::ggplotly(p)
```
Link: https://www.kaggle.com/eliasdabbas/search-engine-results-flights-tickets-keywords under License CC0