Struct StatSummary 

pub struct StatSummary {
    pub fun_y: SummaryFun,
    pub fun_ymin: SummaryFun,
    pub fun_ymax: SummaryFun,
    pub fun_data: Option<SummaryData>,
}

Summarize y values for each unique x. Either a fun.data measure (mean ± CI, median hilow, …) or three independent scalar SummaryFuns (y / ymin / ymax).

Fields

fun_y: SummaryFun

fun_ymin: SummaryFun

fun_ymax: SummaryFun

fun_data: Option<SummaryData>

When set, overrides the three scalar functions and computes y/ymin/ymax together (needed for centre-dependent intervals like mean ± CI).

Implementations

impl StatSummary

pub fn mean_se() -> Self

Mean ± standard error.

pub fn mean_cl_normal() -> Self

Mean with a 95% normal-theory (t) confidence interval.

pub fn mean_cl_boot() -> Self

Mean with a 95% bootstrap-percentile confidence interval.

pub fn mean_sdl() -> Self

Mean ± 2 sd.

pub fn median_hilow() -> Self

Median with the 2.5% / 97.5% sample quantiles.

Trait Implementations

impl Default for StatSummary

fn default() -> Self

Returns the “default value” for a type.

impl Stat for StatSummary

fn compute_group(&self, data: &DataFrame, _scales: &ScaleSet) -> DataFrame

Transform data for a single group.

fn required_aes(&self) -> Vec<Aesthetic>

Required aesthetics this stat needs.

fn name(&self) -> &str

Name for debug/display.

fn default_aes(&self) -> Aes

Default aesthetic mappings this stat produces.

fn panelwise(&self) -> bool

If true, the stat receives every row of a panel at once (grouped only by facet variables) instead of once per aesthetic group. Needed for cross-group comparisons such as stat_compare_means, which must see all groups together. Defaults to per-group behaviour.