GitHub - traversc/glow (original) (raw)
glow
A package for making glow-y plots
The glow package is a framework for creating plots with glowing points as an alternative way of plotting large point clouds.
Gallery
| Methylation 450K Volcano Plot | Diamonds |
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| Milky Way Galaxy (6.1 million stars) |
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| OpenStreetMap GPS traces (2.8 billion points) |
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| Clifford strange attractor (1 billion points) |
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| Airline Dataset (145 million points) | Glow-y Spiral |
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| U.S. Coronavirus Cases (2021) |
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Installation
remotes::install_github("traversc/glow")
Some advantages over traditional techniques
- Naturally displays point density
glowplots don’t depend on the order of points in the data (points are commutative and associative)- Multi-threaded, can be faster than geom_point depending on settings
- No loss of individual points compared to binning procedures
- Naturally works with larger-than-memory datasets (See “Airline” dataset in
inst/examples/examples.r)
Usage
Creating a glow plot is done through the GlowMapper or GlowMapper4classes, which utilize the R6 class framework.
The class function $map creates a raster that can be plotted withggplot’s geom_raster or output directly using the EBImage library.
See the help files and inst/examples/notes.txt for more information on each example.
ggplot example using the diamonds dataset
library(glow) library(ggplot2) library(viridisLite) # Magma color scale
Number of threads
nt <- 4
data(diamonds) gm <- GlowMapper$new(xdim=800, ydim = 640, blend_mode = "screen", nthreads=nt)
relx(0.002) makes point size relative to x-axis, e.g. each point radius is 0.2% of the y-axis
gm$map(x=diamonds$carat, y=diamonds$price, intensity=1, radius = rely(0.002)) pd <- gm$output_dataframe(saturation = 1)
Dark color theme
ggplot() + geom_raster(data = pd, aes(x = pd$x, y = pd$y, fill = pd$value), show.legend = FALSE) + scale_fill_gradientn(colors = additive_alpha(magma(12))) + coord_fixed(gm$aspect(), xlim = gm$xlim(), ylim = gm$ylim()) + labs(x = "carat", y = "price") + theme_night(bgcolor = magma(12)[1])
light "heat" color theme
light_colors <- light_heat_colors(144) ggplot() + geom_raster(data = pd, aes(x = pd$x, y = pd$y, fill = pd$value), show.legend = FALSE) + scale_fill_gradientn(colors = additive_alpha(light_colors)) + coord_fixed(gm$aspect(), xlim = gm$xlim(), ylim = gm$ylim()) + labs(x = "carat", y = "price") + theme_bw(base_size = 14)
light "cool" color theme
light_colors <- light_cool_colors(144) ggplot() + geom_raster(data = pd, aes(x = pd$x, y = pd$y, fill = pd$value), show.legend = FALSE) + scale_fill_gradientn(colors = additive_alpha(light_colors)) + coord_fixed(gm$aspect(), xlim = gm$xlim(), ylim = gm$ylim()) + labs(x = "carat", y = "price") + theme_bw(base_size = 14)
Writing a raster image directly
Instead of using ggplot, you can also output a raster image directly using the EBImage Bioconductor library.
library(EBImage)
Generate data
cliff_points <- clifford_attractor(1e6, 1.886,-2.357,-0.328, 0.918, 0.1, 0) color_pal <- circular_palette(n=144, pal_function=rainbow) cliff_points$color <- map_colors(color_pal, cliff_points$angle, min_limit=-pi, max_limit=pi)
Create raster
gm <- GlowMapper4$new(xdim=480, ydim = 270, blend_mode = "additive", nthreads=4) gm$map(x=cliff_points$x, y=cliff_points$y, radius=1e-3, color=cliff_points$color) pd <- gm$output_raw(saturation = 1)
Output raster with EBImage
image_array <- array(1, dim=c(480, 270, 3)) image_array[,,1] <- pd[[1]]*pd[[4]] image_array[,,2] <- pd[[2]]*pd[[4]] image_array[,,3] <- pd[[3]]*pd[[4]] img <- EBImage::Image(image_array, colormode='Color') plot(img) writeImage(img, "plots/clifford_vignette.png")
