https://github.com/great-northern-diver/loon.ggplot
ggplot to loon
https://github.com/great-northern-diver/loon.ggplot
data-analysis ggplot ggplot-features graphics interactive-plots loon visualizations
Last synced: 15 days ago
JSON representation
ggplot to loon
- Host: GitHub
- URL: https://github.com/great-northern-diver/loon.ggplot
- Owner: great-northern-diver
- Created: 2019-02-26T15:59:58.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2025-06-12T19:33:08.000Z (9 months ago)
- Last Synced: 2025-12-08T20:56:29.511Z (3 months ago)
- Topics: data-analysis, ggplot, ggplot-features, graphics, interactive-plots, loon, visualizations
- Language: HTML
- Size: 20.9 MB
- Stars: 27
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
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README
# loon.ggplot
[](https://travis-ci.com/great-northern-diver/loon.ggplot)
[](https://codecov.io/gh/great-northern-diver/loon.ggplot?branch=master)
[](https://cran.r-project.org/web/packages/loon.ggplot/index.html)
[](https://cran.r-project.org/package=loon.ggplot)
An R package to turn ggplot graphic data structures into interactive loon plots. Extends the grammar to add interactivity.
Documentation: [https://great-northern-diver.github.io/loon.ggplot/](https://great-northern-diver.github.io/loon.ggplot/)
## Introduction
The `ggplot2` graphics package (part of the `tidyverse` package collection) uses the base `grid` graphics package to produce publication quality graphics for data analysis. Based on a grammar for graphics, `ggplot2` also provides a lot of functionality (e.g. `facet`s) that can be extremely useful in data analysis.
The `loon` graphics package provides **interactive** graphics especially valuable in any **exploratory data analysis**. This includes programmatic and direct manipulation of the visualizations to effect interactive identification, zooming, panning, and linking between any number of displays. Of course, `loon` also provides publication quality static graphics in `grid` via loon's functions `grid.loon()` and `loonGrob()`.
The loon.ggplot package brings **both these packages together**. Data analysts who value the ease with which `ggplot2` can create meaningful graphics can now turn these `ggplot`s into interactive `loon` plots for more dynamic interaction with their data. Conversely, data analysts who explore data interactively can at any time turn a snapshot of their interactive `loon` plots into `ggplot`s. The former is accomplished by the simple translation function **`ggplot2loon()`** and the latter by the simple translation function **`loon2ggplot()`**.
## An interactive grammar
`loon.ggplot` extends the grammar of graphic to include interactive clauses:
- `+ linking()`
- `+ hover()`
- `+ selection()`
- `+ active()`
- `+ zoom()`
- `+ interactivity()`
A `ggplot()` created with any of these clauses (in addition to the usual grammar) will `print()` as an interactive `loon` plot and `plot()` as a static ggplot.
## Install from `R`
Just as the `tidyverse` includes `ggplot2`, the package suite called `diveR` includes `loon.ggplot` and many more interactive visualization packages.
Installing `diveR` from CRAN will include installing `loon.ggplot`:
```
install.packages("diveR")
```
Alternatively, `loon.ggplot` (and its dependencies) may be installed directly
- from `CRAN`
```
install.packages("loon.ggplot")
```
or
- from `github`
```
remotes::install_github("https://github.com/great-northern-diver/loon.ggplot")
```
## Examples
More detail is given in the vignettes, especially `"A Grammar Of Interactive Graphics"` and `"There And Back Again"`.
Below functions transforming from a ggplot to a loon plot, and from a loon plot to a ggplot, are demonstrated.
### `ggplot2loon()`: ggplot --> loon
* Construct `ggplot`
Consider the `mtcars` data set. Suppose we draw a scatterplot of the mileage `mpg` (miles per US gallon) versus the weight of the car `wt` in thousands of pounds and colour represents different cylinder numbers. In `ggplot2` this would be constructed as
```
library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg, colour = as.factor(cyl))) + geom_point()
p
```
We might also display a histogram of some other variate, say the engine's horsepower `hp`. In `ggplot2` this would be constructed as
```
h <- ggplot(mtcars, aes(x = hp, fill = as.factor(cyl))) + geom_histogram()
h
```
* To `loon`
the `"ggplot"` data structures `p` and `h` can be **turned into interactive loon plot**s using the `ggplot2loon()` function:
```
library(loon.ggplot)
pl <- ggplot2loon(p)
hl <- ggplot2loon(h)
```
An alternative way of doing so is to replace `ggplot()` function to `l_ggplot()` function. Then, follow the pipe rules of `ggplot()` but get a `loon` plot.
```{r}
# the scatter plot
l_ggplot(mtcars, aes(wt,
mpg,
colour = as.factor(cyl))) +
geom_point()
# the histogram
l_ggplot(mtcars, aes(x = hp, fill = as.factor(cyl))) +
geom_histogram()
```
Note that:
+ Loon "Hello World": Introduction to interactive `loon` plots can be found via [loon](https://cran.r-project.org/web/packages/loon/vignettes/introduction.html). It shows how to create, manipulate (selection, linking and etc) `loon` plots
+ `loon.ggplot` talk: A talk "Interactive ggplots in R" has been given in [SDSS 2019](https://ww2.amstat.org/meetings/sdss/2019/onlineprogram/AbstractDetails.cfm?AbstractID=306216). Slides can be found in [SDSS2019/loon.ggplot talk](https://www.math.uwaterloo.ca/~rwoldfor/talks/SDSS2019/loon.ggplot/assets/player/KeynoteDHTMLPlayer.html) which gives more details.
+ A `ggmatrix` object in package `GGally` can also be converted to a `loon` widget. See `help(ggplot2loon)` for more info.
### `loon2ggplot()`: loon --> ggplot
After creating `loon` plots and adding programmatic and direct manipulation of the visualizations to effect interactive identification, function `loon2ggplot` can be applied to return a static `ggplot`
```
pg <- loon2ggplot(pl)
hg <- loon2ggplot(hl)
```
Note that `pg` and `hg` are `ggplot` objects.
```
class(pg)
[1] "gg" "ggplot"
class(hg)
[1] "gg" "ggplot"
```
Layers, theme adjustment can be piped though like:
```
pg +
ggplot2::geom_smooth() +
ggplot2::ggtitle("Mtcars")
```
```
hg +
ggplot2::geom_density() +
ggplot2::coord_flip()
```
Note that:
+ Compound loon widget like `l_ts` and `l_pairs` are created by [`patchwork`](https://patchwork.data-imaginist.com/index.html). The `ggplot` components like `theme`, `labels` can be piped through but by the `patchwork` rule.
+ Some functionalities are provided
* Adding glyphs on scatterplot like `geom_serialAxesGlyph()`, `geom_polygonGlyph()`, `geom_imageGlyph()` and etc.
* Providing serial axes plots (parallel coordinate and radial coordinate) via `ggSerialAxes()`
### `loon.ggplot()`: loon <--> ggplot
`loon.ggplot()` gathers features of both `loon2ggplot()` and `ggplot2loon()`. It can take either a `loon` widget or `gg` object and transform back and forth.
```
p <- l_plot(iris)
# `loon` to `gg`
g <- loon.ggplot(p)
g <- g + geom_smooth(method = "lm") + theme_bw()
g
# `gg` to `loon`
l <- loon.ggplot(g)
```
## More than `loon` or `ggplot2`
More than connecting `ggplot2` and `loon` these two specific graphical systems, `loon.ggplot` is able to connect the suite behind them.
### `loon` --> `ggplot2` --> `gganimate`
Return an animation from a `loon` plot
```
# a loon plot
library(gapminder)
p <- with(gapminder,
l_plot(gdpPercap, lifeExp,
# scale the size into certain amounts
size = scales::rescale(pop, to = c(4, 50)),
color = continent,
itemLabel = as.character(country),
showItemLabels = TRUE
))
# highlight large population countires
library(dplyr)
top10in2007 <- gapminder %>%
filter(year == 2007) %>%
top_n(10, pop)
p['selected'][gapminder$country %in% top10in2007$country] <- TRUE
# to `ggplot` then to `animation`
library(gganimate)
loon.ggplot(p, selectedOnTop = FALSE) +
facet_wrap(gapminder$continent) +
theme(legend.position = "none") +
labs(title = 'Year: {frame_time}',
x = 'GDP per capita',
y = 'life expectancy') +
transition_time(gapminder$year) +
ease_aes('linear')
```
### `ggplot2` --> `loon` --> `shiny`
Return a shiny web app from a `ggplot` object
```
library(dplyr)
gp <- gapminder %>%
filter(year == 2007,
continent != "Oceania") %>%
ggplot(mapping = aes(gdpPercap, lifeExp,
colour = continent)) +
geom_point(mapping = aes(size = pop)) +
geom_smooth(mapping = aes(weight = pop),
method = "lm",
se = FALSE)
library(loon.shiny)
gp %>%
loon.ggplot() %>%
loon.shiny()
```
