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https://github.com/great-northern-diver/ggmulti
Package for adding some multivariate visualizations to ggplot2
https://github.com/great-northern-diver/ggmulti
Last synced: 3 months ago
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Package for adding some multivariate visualizations to ggplot2
- Host: GitHub
- URL: https://github.com/great-northern-diver/ggmulti
- Owner: great-northern-diver
- Created: 2020-12-05T15:08:33.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2023-12-13T16:48:48.000Z (11 months ago)
- Last Synced: 2024-07-16T19:04:22.824Z (4 months ago)
- Language: R
- Size: 81.3 MB
- Stars: 12
- Watchers: 4
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# ggmulti
[](https://travis-ci.com/great-northern-diver/ggmulti.svg?branch=main)
[](https://codecov.io/gh/great-northern-diver/ggmulti?branch=main)
[](https://cran.r-project.org/web/packages/ggmulti/index.html)
It provides materials (i.e. serialaxes objects) to visualize high dimensional data in `ggplot`.
Documentation: https://great-northern-diver.github.io/ggmulti/
## Introduction
Package `ggmulti` extends the `ggplot2` package to provide some high dimensional visualization functionality, such as
* Serialaxes coordinates (i.e., parallel or radial axis systems)
* General glyphs (e.g., polygons, images) to appear a scatterplot.
* "More general" `geom_histogram` and `geom_density` to allow them to appear on serial axes.
### Serialaxes Coordinates
Parallel coordinates
```
library(ggmulti)
p <- ggplot(iris,
mapping = aes(Sepal.Length = Sepal.Length,
Sepal.Width = Sepal.Width,
Petal.Length = Petal.Length,
Petal.Width = Petal.Width,
colour = Species)) +
geom_path(alpha = 0.2) +
coord_serialaxes()
p
```
We can also construct a radar plot by setting `axes.layout = "radial"` in `coord_serialaxes`. In addition, we can add histogram layer on top
```
p +
geom_histogram(mapping = aes(fill = Species), alpha = 0.5)
```
### Glyphs
The flag of Canada
```{r}
canada <- data.frame(
xmin = c(-2, -1, 1),
xmax = c(-1, 1, 2),
ymin = rep(-1.2, 3),
ymax = rep(1.2, 3),
fill = factor(c(1,2,1))
)
p <- ggplot() +
geom_rect(data = canada,
mapping = aes(xmin = xmin, xmax = xmax,
ymin = ymin, ymax = ymax,
fill = fill),
colour = "black") +
geom_polygon_glyph(data = data.frame(x = 0, y = 0),
mapping = aes(x = x, y = y),
polygon_x = x_maple,
polygon_y = y_maple,
fill = "red",
size = 12) +
scale_fill_manual(values = c("red", "white")) +
theme_void() +
theme(legend.position = "none")
p
```
We can save it as a `png` object, then call `geom_image_glyph` to display the image glyph
```
ggsave("canada.png", type = "cairo", bg = "white")
images <- png::readPNG("canada.png")
ggplot(data = data.frame(x = c(1,2,1.5,2,1), y = c(1,1,1.5,2,2)),
mapping = aes(x = x, y = y)) +
geom_image_glyph(images = rep(list(images), 5)) +
coord_cartesian(xlim = extendrange(c(1,2)),
ylim = extendrange(c(1,2)))
```
### "More general" `geom_histogram` and `geom_density`
Functions `geom_histogram_` and `geom_density_` are more general `geom_histogram` and `geom_density` since these two functions can accommodate both `x` and `y` simutaniously. If only one is provided, `geom_histogram` or `geom_density` will be executed.
The following figure displays the back to back plot (histogram and density)
```
iris %>%
tidyr::pivot_longer(cols = -Species,
names_to = "Outer sterile whorls",
values_to = "values") %>%
ggplot(mapping = aes(x = `Outer sterile whorls`,
y = values,
fill = Species)) +
geom_histogram_(scale.y = "group",
alpha = 0.5,
prop = 0.6) +
geom_density_(scale.y = "group",
prop = 0.6,
alpha = 0.5,
colour = NA,
positive = FALSE)
```
