Automatically selects and generates the most appropriate statistical visualization based on variable data types. Supports both independent and repeated measures designs with various plot types including violin plots, scatter plots, bar charts, and alluvial diagrams.
Usage
statsplot2(
data,
dep,
group,
grvar,
direction = "independent",
distribution = "p",
alluvsty = "t1",
excl = FALSE,
originaltheme = FALSE
)Arguments
- data
The data as a data frame.
- dep
The dependent variable (y-axis, 1st measurement). Can be continuous or categorical.
- group
The grouping variable (x-axis, 2nd measurement). Can be continuous or categorical.
- grvar
Optional grouping variable for creating grouped plots across multiple panels.
- direction
Measurement design type. "independent" for between-subjects comparisons, "repeated" for within-subjects/repeated measures comparisons.
- distribution
Statistical approach: "p" = parametric, "np" = nonparametric, "r" = robust, "bf" = Bayes factor.
- alluvsty
Style for alluvial diagrams: "t1" = ggalluvial with stratum labels, "t2" = easyalluvial with automatic variable selection.
- excl
If TRUE, excludes rows with missing values before analysis.
- originaltheme
If TRUE, uses original ggplot2 themes instead of ggstatsplot themes.
Examples
# Automatic plot selection for factor vs continuous variables
statsplot2(
data = mtcars,
dep = "mpg",
group = "cyl",
direction = "independent",
distribution = "p"
)
#> Error in statsplot2(data = mtcars, dep = "mpg", group = "cyl", direction = "independent", distribution = "p"): argument "grvar" is missing, with no default
# Repeated measures with alluvial diagram
statsplot2(
data = survey_data,
dep = "condition_baseline",
group = "condition_followup",
direction = "repeated",
alluvsty = "t1"
)
#> Error in statsplot2(data = survey_data, dep = "condition_baseline", group = "condition_followup", direction = "repeated", alluvsty = "t1"): argument "grvar" is missing, with no default