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https://github.com/mjskay/ggdist
Visualizations of distributions and uncertainty
https://github.com/mjskay/ggdist
ggplot2 r r-package uncertainty uncertainty-visualization visualization
Last synced: about 2 months ago
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Visualizations of distributions and uncertainty
- Host: GitHub
- URL: https://github.com/mjskay/ggdist
- Owner: mjskay
- License: gpl-3.0
- Created: 2020-06-05T02:26:40.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-27T03:31:48.000Z (about 2 months ago)
- Last Synced: 2024-08-03T23:23:21.454Z (about 2 months ago)
- Topics: ggplot2, r, r-package, uncertainty, uncertainty-visualization, visualization
- Language: R
- Homepage: https://mjskay.github.io/ggdist/
- Size: 245 MB
- Stars: 817
- Watchers: 10
- Forks: 25
- Open Issues: 49
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - mjskay/ggdist - Visualizations of distributions and uncertainty (R)
README
---
output: github_document
---
```{r chunk_options, include=FALSE}
knitr::opts_chunk$set(
fig.path = "man/figures/README/"
)
knitr::opts_chunk$set(
fig.retina = 2
)
if (requireNamespace("ragg", quietly = TRUE)) {
knitr::opts_chunk$set(
dev = "ragg_png"
)
} else if (capabilities("cairo")) {
knitr::opts_chunk$set(
dev = "png",
dev.args = list(png = list(type = "cairo"))
)
}
```
# ggdist: Visualizations of distributions and uncertainty
[](https://github.com/mjskay/ggdist/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/github/mjskay/ggdist?branch=master)
[](https://cran.r-project.org/package=ggdist)
[](https://doi.org/10.1109/TVCG.2023.3327195)
[](https://doi.org/10.5281/zenodo.3879620)
```{r setup, include=FALSE}
library(dplyr)
library(tidyr)
library(distributional)
library(ggdist)
library(ggplot2)
library(patchwork)
theme_set(theme_ggdist())
```
```{r preview_setup, include=FALSE}
set.seed(12345)
d = dist_normal(4, 1)
d_quantiles = dist_sample(list(qnorm(ppoints(1000), 4, 1)))
q_100 = qnorm(ppoints(100), 4, 1)
x_samples_100 = rnorm(100, 4, 1)
d_samples_100 = dist_sample(list(x_samples_100))
dists_xlim = c(0,8)
```
```{r preview_slabinterval, include=FALSE}
slabinterval_plot = ggplot() +
stat_halfeye(aes(y = "01", xdist = d)) +
stat_eye(aes(y = "02", xdist = d)) +
stat_gradientinterval(aes(y = "03", xdist = d), scale = 0.75, fill_type = "gradient", show_interval = FALSE, show_point = FALSE, position = position_nudge(y = -0.2)) +
stat_ccdfinterval(aes(y = "04", xdist = d), scale = .5, justification = 0, position = position_nudge(y = -0.3)) +
stat_cdfinterval(aes(y = "05", xdist = d), scale = .5, justification = 0, position = position_nudge(y = -0.2)) +
stat_interval(
aes(y = "06", xdist = d), color = "gray65", alpha = 1/3, linewidth = 10,
position = position_nudge(y = -.1)
) +
stat_pointinterval(aes(y = "07", xdist = d)) +
stat_slab(aes(y = "08", xdist = d), position = position_nudge(y = - 0.2)) +
stat_histinterval(aes(y = "09", xdist = d_quantiles), position = position_nudge(y = - 0.25)) +
stat_slab(
aes(y = "10", xdist = d, fill_ramp = after_stat(level)),
show.legend = FALSE, .width = c(.5, .8, .95),
fill = scales::brewer_pal()(7)[[5]],
position = position_nudge(y = -0.5)
) +
stat_spike(
aes(y = "10", xdist = d, linetype = after_stat(at)),
at = c(mode = Mode, qi = function(...) qi(..., .width = .8)),
show.legend = FALSE,
position = position_nudge(y = -0.5)
) +
scale_x_continuous(limits = dists_xlim, expand = c(0,0), labels = NULL) +
scale_y_discrete(limits = rev, labels = NULL) +
labs(
title = "slabinterval",
x = NULL,
y = NULL
) +
theme(plot.margin = margin(5.5, 5.5, 5.5, 0), axis.ticks = element_blank())
```
```{r preview_dotsinterval, include=FALSE}
set.seed(123456)
x1 = rnorm(125, 3, 0.75)
x2 = rnorm(125, 5, 0.75)
dotsinterval_plot = ggplot() +
stat_dotsinterval(aes(y = "01", xdist = d)) +
stat_dots(aes(y = "02", xdist = d), layout = "weave", position = position_nudge(y = -0.2)) +
geom_weave(aes(y = "03", x = x1, fill = x1 > 4, group = NA), linewidth = NA, alpha = 0.75, binwidth = NA) +
geom_swarm(aes(y = "04", x = x2, fill = x2 > 4, group = NA), linewidth = NA, alpha = 0.75, binwidth = NA, position = position_nudge(y = 0.1)) +
scale_fill_brewer(palette = "Dark2", guide = "none") +
ggnewscale::new_scale_fill() +
stat_dotsinterval(aes(y = "05", xdist = d, fill = after_stat(level)), layout = "weave", slab_linewidth = NA, .width = c(.66, .95), quantiles = 200, position = position_nudge(y = -0.3)) +
scale_color_manual(values = scales::brewer_pal()(3)[-1], aesthetics = "fill", guide = "none") +
ggnewscale::new_scale_fill() +
geom_dots(aes(y = "06", x = x1, fill = "a"), side = "bottom", scale = 0.75, linewidth = NA, position = position_nudge(y = -0.1)) +
stat_slabinterval(aes(y = "06", x = x1, fill = "a"), scale = 0.5, position = position_nudge(y = -0.1)) +
geom_dots(aes(y = "07", x = x2, fill = "b"), side = "bottom", scale = 0.75, linewidth = NA, position = position_nudge(y = -0.2)) +
stat_slabinterval(aes(y = "07", x = x2, fill = "b"), scale = 0.5, position = position_nudge(y = -0.2)) +
scale_fill_brewer(palette = "Set2", guide = "none") +
scale_x_continuous(limits = dists_xlim, expand = c(0,0), labels = NULL) +
scale_y_discrete(limits = rev, labels = NULL) +
labs(
title = "dotsinterval",
x = NULL,
y = NULL
) +
theme(axis.ticks = element_blank())
```
```{r preview_lineribbon, include=FALSE}
m_mpg = lm(mpg ~ hp * cyl, data = mtcars)
mtcars_preds = mtcars %>%
group_by(cyl) %>%
expand(hp = seq(min(hp), max(hp), length.out = 50)) %>%
bind_cols(predict(m_mpg, newdata = ., se.fit = TRUE))
mtcars_plot = function(.width = c(.5, .8, .95), alpha = 1/4, ...) {
mtcars_preds %>%
ggplot(aes(x = hp, fill = ordered(cyl), color = ordered(cyl))) +
stat_lineribbon(
aes(ydist = dist_student_t(df, fit, se.fit)),
.width = .width, alpha = alpha, ...
) +
geom_point(aes(y = mpg), data = mtcars) +
scale_fill_brewer(palette = "Set2") +
scale_color_brewer(palette = "Dark2") +
labs(
color = "cyl",
fill = "cyl",
y = "mpg"
) +
theme_ggdist()
}
lineribbon_plot_1 = mtcars_plot() +
guides(color = "none", fill = "none", x = "none") +
scale_x_continuous(labels = NULL) +
scale_y_continuous(labels = NULL) +
labs(title = "lineribbon", x = NULL, y = NULL) +
theme(plot.margin = margin(5.5, 5.5, 0, 5.5), axis.ticks = element_blank())
lineribbon_plot_2 = mtcars_plot(.width = ppoints(30), alpha = 1/20) +
guides(color = "none", fill = "none") +
scale_x_continuous(labels = NULL) +
scale_y_continuous(labels = NULL) +
labs(x = NULL, y = NULL) +
theme(plot.margin = margin(0, 5.5, 5.5, 5.5), axis.ticks = element_blank())
```
```{r preview, echo=FALSE, fig.height=4.5, fig.width=9}
slabinterval_plot + dotsinterval_plot + (lineribbon_plot_1 / lineribbon_plot_2) +
plot_annotation(
caption = 'Some examples from the three main families of ggdist geometries'
)
```
[ggdist](https://mjskay.github.io/ggdist/) is an R package that provides a flexible set of `{ggplot2}` geoms and stats designed
especially for visualizing distributions and uncertainty. It is designed for both
frequentist and Bayesian uncertainty visualization, taking the view that uncertainty
visualization can be unified through the perspective of distribution visualization:
for frequentist models, one visualizes confidence distributions or bootstrap distributions (see `vignette("freq-uncertainty-vis")`);
for Bayesian models, one visualizes probability distributions (see the [tidybayes](https://mjskay.github.io/tidybayes/)
package, which builds on top of `{ggdist}`).
The `geom_slabinterval()` / `stat_slabinterval()` family (see `vignette("slabinterval")`) makes it
easy to visualize point summaries and intervals, eye plots, half-eye plots, ridge plots,
CCDF bar plots, gradient plots, histograms, and more:
The `geom_dotsinterval()` / `stat_dotsinterval()` family (see `vignette("dotsinterval")`) makes
it easy to visualize dot+interval plots, Wilkinson dotplots, beeswarm plots, and quantile dotplots
(and combined with half-eyes, composite plots like rain cloud plots):
```{r halfeye_dotplot, echo=FALSE, message=FALSE, warning=FALSE}
set.seed(12345) # for reproducibility
data.frame(
abc = c("a", "b", "c"),
value = rnorm(300, c(1, 8, 3), c(1, 1.7, 1))
) %>%
ggplot(aes(y = abc, x = value, fill = abc)) +
stat_slab() +
stat_dotsinterval(side = "bottom", slab_linewidth = NA) +
scale_fill_brewer(palette = "Set2") +
theme_ggdist()
```
The `geom_lineribbon()` / `stat_lineribbon()` family (see `vignette("lineribbon")`) makes it easy to visualize
fit lines with an arbitrary number of uncertainty bands:
```{r lineribbon, echo=FALSE, message=FALSE, warning=FALSE}
mtcars_plot()
```
All stats in `{ggdist}` also support visualizing analytical distributions and vectorized distribution
data types like [distributional](https://pkg.mitchelloharawild.com/distributional/) objects or `posterior::rvar()`
objects. This is particularly useful when visualizing uncertainty in frequentist
models (see `vignette("freq-uncertainty-vis")`) or when visualizing priors in a
Bayesian analysis.
The `{ggdist}` geoms and stats also form a core part of the [tidybayes](https://mjskay.github.io/tidybayes/) package (in fact,
they originally were part of `{tidybayes}`). For examples of the use of `{ggdist}` geoms and
stats for visualizing uncertainty in Bayesian models, see the vignettes in `{tidybayes}`, such as
`vignette("tidybayes", package = "tidybayes")` or `vignette("tidy-brms", package = "tidybayes")`.
## Cheat sheets
These cheat sheets focus on the `slabinterval` family of geometries:
## Installation
You can install the currently-released version from CRAN with this R
command:
```{r install, eval=FALSE}
install.packages("ggdist")
```
Alternatively, you can install the latest development version from GitHub with these R
commands:
```{r install_github, eval=FALSE}
install.packages("devtools")
devtools::install_github("mjskay/ggdist")
```
## Dependencies
`{ggdist}` aims to have minimal additional dependencies beyond those already
required by `{ggplot2}`. The `{ggdist}` dependencies fall into the following
categories:
1. `{ggplot2}`.
2. Packages that `{ggplot2}` also depends on. These packages add no additional
dependency cost because `{ggplot2}` already requires them: `{rlang}`, `{cli}`,
`{scales}`, `{tibble}`, `{vctrs}`, `{withr}`, `{gtable}`, and `{glue}`.
3. Packages that `{ggplot2}` does not depend on. These are all well-maintained
packages with few dependencies and a clear need within `{ggdist}`:
- `{distributional}`: this implementation of distribution vectors powers
much of `{ggdist}`. This package adds minimal additional cost, as its only
dependency that is not also a dependency of `{ggplot2}` is `{numDeriv}`, which
is needed by `{ggdist}` anyway (see below).
- `{numDeriv}`: used for calculating Jacobians of scale transformations.
Needed because testing has revealed common situations where
`stats::numericDeriv()` fails but `{numDeriv}` does not. Widely used by other
CRAN packages and has no additional dependencies.
- `{quadprog}`: Used to solve constrained optimization problems during different
parts of dotplot layout, particularly to avoid dot overlaps in the `"bin"`
and `"weave"` layouts. Widely used by other CRAN packages and has no
additional dependencies.
- `{Rcpp}`: Used to implement faster dotplot layout. Widely used by other CRAN
packages and has no additional dependencies.
## Feedback, issues, and contributions
I welcome feedback, suggestions, issues, and contributions! If you have found a bug, please file it [here](https://github.com/mjskay/ggdist/issues/new) with minimal code to reproduce the issue. Pull requests should be filed against the [`dev`](https://github.com/mjskay/ggdist/tree/dev) branch. I am not particularly reliable over email, though you can try to contact me at . A [Twitter](https://twitter.com/mjskay) DM is more likely to elicit a response.
## Citing `ggdist`
Matthew Kay (2024). ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics. _IEEE Transactions on Visualization and Computer Graphics_, 30(1), 414--424. DOI: [10.1109/TVCG.2023.3327195](https://doi.org/10.1109/TVCG.2023.3327195).
Matthew Kay (`r format(Sys.Date(), "%Y")`). ggdist: Visualizations of Distributions and Uncertainty. R package version `r getNamespaceVersion("ggdist")`, . DOI: [10.5281/zenodo.3879620](https://doi.org/10.5281/zenodo.3879620).