ggplot_rs/stat/
mod.rs

1pub mod align;
2pub mod arcdiagram;
3pub mod bin;
4pub mod bin2d;
5pub mod bindot;
6pub mod binhex;
7pub mod boxplot;
8pub mod calendar;
9pub mod chord;
10#[cfg(feature = "ggpubr")]
11pub mod compare_means;
12pub mod connect;
13pub mod contour;
14pub mod contour_filled;
15#[cfg(feature = "ggpubr")]
16pub mod cor;
17pub mod count;
18pub mod density;
19pub mod density2d;
20pub mod dist;
21pub mod distribution;
22pub mod ecdf;
23pub mod ellipse;
24pub mod function;
25pub mod funnel;
26pub(crate) mod gam;
27pub mod heikin_ashi;
28pub mod identity;
29pub mod indicator;
30pub mod loess;
31pub mod manual;
32pub mod marching_squares;
33pub mod marimekko;
34pub mod network;
35pub mod pack;
36pub mod pareto;
37pub mod partition;
38pub mod qq;
39#[cfg(feature = "regression")]
40pub mod quantile;
41pub mod renko;
42pub mod sankey;
43#[cfg(feature = "sf")]
44pub mod sf_coordinates;
45pub mod smooth;
46pub mod sum;
47pub mod summary;
48pub mod summary2d;
49pub mod summary_bin;
50pub mod tree;
51pub mod treemap;
52pub mod unique;
53pub mod venn;
54pub mod waffle;
55pub mod waterfall;
56pub mod xdensity_ridges;
57pub mod ydensity;
58
59use crate::aes::{Aes, Aesthetic};
60use crate::data::DataFrame;
61use crate::scale::ScaleSet;
62
63/// Trait for statistical transformations.
64pub trait Stat: Send + Sync {
65    /// Transform data for a single group.
66    fn compute_group(&self, data: &DataFrame, scales: &ScaleSet) -> DataFrame;
67
68    /// Required aesthetics this stat needs.
69    fn required_aes(&self) -> Vec<Aesthetic>;
70
71    /// Default aesthetic mappings this stat produces.
72    fn default_aes(&self) -> Aes {
73        Aes::default()
74    }
75
76    /// If true, the stat receives every row of a panel at once (grouped only by
77    /// facet variables) instead of once per aesthetic group. Needed for
78    /// cross-group comparisons such as `stat_compare_means`, which must see all
79    /// groups together. Defaults to per-group behaviour.
80    fn panelwise(&self) -> bool {
81        false
82    }
83
84    /// True when the stat computes `y` itself in data units (a count, a
85    /// density, an ECDF) — ggplot2's `y = after_stat(count)` default. The
86    /// build then passes it through the y scale's transform (#490).
87    fn computed_y(&self) -> bool {
88        false
89    }
90
91    /// Name for debug/display.
92    fn name(&self) -> &str;
93
94    /// Validate the stat's own fixed configuration (not the data) and push
95    /// a human-readable warning for each problem found — e.g.
96    /// `StatBinHex::binwidth` being NaN, zero or negative. Called once per
97    /// layer, before [`compute_group`](Self::compute_group) runs (which has
98    /// no warnings channel of its own: the per-group call only sees
99    /// `&DataFrame` and `&ScaleSet`, not the plot's warnings sink). Default:
100    /// no-op.
101    fn validate(&self, _warnings: &mut Vec<String>) {}
102}
103
104/// R's `bw.nrd0`: Silverman's rule of thumb, `0.9 · min(sd, IQR/1.34) · n^-0.2`,
105/// with R's fallbacks when the spread is zero — `sd`, then `|x[0]|`, then `1` —
106/// so a constant sample still gets a positive bandwidth (never 0 → NaN).
107pub(crate) fn bw_nrd0(values: &[f64]) -> f64 {
108    let n = values.len();
109    if n < 2 {
110        return 1.0;
111    }
112    let nf = n as f64;
113    let mean = values.iter().sum::<f64>() / nf;
114    let sd = (values.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / (nf - 1.0)).sqrt();
115    let mut sorted = values.to_vec();
116    sorted.sort_by(|a, b| a.total_cmp(b));
117    let q = |p: f64| {
118        let h = (n - 1) as f64 * p;
119        let lo = h.floor() as usize;
120        let hi = (lo + 1).min(n - 1);
121        sorted[lo] + (h - lo as f64) * (sorted[hi] - sorted[lo])
122    };
123    let iqr = q(0.75) - q(0.25);
124    let mut lo = sd.min(iqr / 1.34);
125    for fallback in [sd, values[0].abs(), 1.0] {
126        if lo > 0.0 && lo.is_finite() {
127            break;
128        }
129        lo = fallback;
130    }
131    0.9 * lo * nf.powf(-0.2)
132}
ggplot_rs/stat/mod.rs

ggplot_rs/stat/
mod.rs
