R ggdist package for distribution visualizations. Use for visualizing distributions and uncertainty.
Scanned 6/5/2026
Install via CLI
openskills install LeoLin990405/r-analytics-skill---
name: ggdist
description: R ggdist package for distribution visualizations. Use for visualizing distributions and uncertainty.
---
# ggdist
Visualizations of distributions and uncertainty.
## Stat Halfeye
```r
library(ggplot2)
library(ggdist)
ggplot(data, aes(x = group, y = value)) +
stat_halfeye()
# With options
ggplot(data, aes(x = group, y = value)) +
stat_halfeye(
.width = c(0.66, 0.95),
point_interval = median_qi
)
```
## Stat Eye
```r
# Full eye (violin + interval)
ggplot(data, aes(x = group, y = value)) +
stat_eye()
```
## Stat Slab
```r
# Just the density
ggplot(data, aes(x = group, y = value)) +
stat_slab()
# Horizontal
ggplot(data, aes(y = group, x = value)) +
stat_slab()
```
## Stat Interval
```r
# Just intervals
ggplot(data, aes(x = group, y = value)) +
stat_interval()
# Multiple intervals
ggplot(data, aes(x = group, y = value)) +
stat_interval(.width = c(0.5, 0.8, 0.95))
```
## Stat Pointinterval
```r
# Point + interval
ggplot(data, aes(x = group, y = value)) +
stat_pointinterval()
# Different point types
ggplot(data, aes(x = group, y = value)) +
stat_pointinterval(point_interval = mean_qi)
```
## Stat Dots
```r
# Dot plot
ggplot(data, aes(x = group, y = value)) +
stat_dots()
# Quantile dots
ggplot(data, aes(x = group, y = value)) +
stat_dotsinterval()
```
## Gradient Intervals
```r
ggplot(data, aes(x = group, y = value)) +
stat_gradientinterval()
```
## CCDF Bars
```r
# Complementary CDF
ggplot(data, aes(x = group, y = value)) +
stat_ccdfinterval()
```
## Combining
```r
ggplot(data, aes(x = group, y = value)) +
stat_halfeye(
adjust = 0.5,
width = 0.6,
.width = 0,
justification = -0.2,
point_colour = NA
) +
geom_boxplot(
width = 0.15,
outlier.shape = NA
) +
stat_dots(
side = "left",
justification = 1.1,
binwidth = 0.25
)
```
## With Posterior Samples
```r
# From Bayesian models
library(brms)
posterior <- as_draws_df(fit)
ggplot(posterior, aes(x = b_Intercept)) +
stat_halfeye()
```
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