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Box-Violin Plots to Compare Within Groups

Source: R/jjwithinstats.h.R
jjwithinstats.Rd

Wrapper Function for ggstatsplot::ggwithinstats and ggstatsplot::grouped_ggwithinstats to generate violin plots for repeated measurements and within-subjects analysis with statistical annotations and significance testing.

Usage

jjwithinstats(
  data,
  dep1,
  dep2,
  dep3 = NULL,
  dep4 = NULL,
  pointpath = FALSE,
  centralitypath = FALSE,
  centralityplotting = FALSE,
  centralitytype = "parametric",
  clinicalpreset = "custom",
  typestatistics = "parametric",
  pairwisecomparisons = FALSE,
  pairwisedisplay = "significant",
  padjustmethod = "holm",
  effsizetype = "biased",
  violin = TRUE,
  boxplot = TRUE,
  point = FALSE,
  mytitle = "Within Group Comparison",
  xtitle = "",
  ytitle = "",
  originaltheme = FALSE,
  resultssubtitle = FALSE,
  bfmessage = FALSE,
  conflevel = 0.95,
  k = 2,
  plotwidth = 650,
  plotheight = 450
)

Arguments

data

The data as a data frame.

dep1

.

dep2

.

dep3

.

dep4

.

pointpath

.

centralitypath

.

centralityplotting

Display mean/median lines across time points to visualize the overall trend. Helps identify if the group as a whole is improving, declining, or stable over time.

centralitytype

.

clinicalpreset

Choose a preset optimized for common clinical scenarios, or select Custom for manual configuration.

typestatistics

Parametric: Assumes normal distribution, most powerful when appropriate. Nonparametric: No distribution assumptions, robust for skewed biomarker data. Robust: Uses trimmed means, reduces outlier influence. Bayesian: Provides evidence strength rather than p-values.

pairwisecomparisons

Enable to see which specific time points differ significantly (e.g., baseline vs month 3, month 3 vs month 6). Useful for identifying when changes occur during treatment or disease progression.

pairwisedisplay

.

padjustmethod

.

effsizetype

.

violin

.

boxplot

.

point

.

mytitle

.

xtitle

.

ytitle

.

originaltheme

.

resultssubtitle

.

bfmessage

Whether to display Bayes Factor in the subtitle when using Bayesian analysis.

conflevel

Confidence level for confidence intervals.

k

Number of decimal places for displaying statistics in the subtitle.

plotwidth

Width of the plot in pixels. Default is 650.

plotheight

Height of the plot in pixels. Default is 450.

Value

A results object containing:

results$todoa html
results$interpretationa html
results$plotan image
results$summarya html

Examples

# Basic within-subjects analysis with iris dataset (simulated repeated measures)
data(iris)
iris_wide <- data.frame(
    Subject = 1:50,
    Time1 = iris$Sepal.Length[1:50],
    Time2 = iris$Sepal.Width[1:50] * 2.5,
    Time3 = iris$Petal.Length[1:50] * 1.8
)
jjwithinstats(
    data = iris_wide,
    dep1 = "Time1",
    dep2 = "Time2",
    dep3 = "Time3",
    typestatistics = "parametric"
)
#> 
#>  VIOLIN PLOTS TO COMPARE WITHIN GROUPS
#> 
#>  ✅ Ready for analysis: Violin Plot comparing 3 repeated measurements.
#> 
#>  <div style='background-color:#f8f9fa;padding:15px;margin:10px
#>  0;border-left:4px solid #007bff;'><h4
#>  style='margin-top:0;color:#007bff;'>Clinical Context
#> 
#>  This analysis compares 3 measurements from the same subjects. Useful
#>  for: biomarker tracking, treatment response monitoring, disease
#>  progression assessment.
#> 
#>  Test Used: Repeated measures ANOVA tests whether means differ
#>  significantly across time points, assuming normal distribution.
#> 
#>  🔍 What to look for:
#>  • Statistical significance (p < 0.05) indicates real changes over time
#>  • Effect sizes show practical importance
#>  • Individual trajectories reveal response patterns
#>  • Outliers may indicate treatment non-responders or measurement errors
#> 
#>  <div style='background-color:#f0f8f0;padding:10px;border:1px solid
#>  #28a745;'>Analysis: Repeated measures ANOVA with 3 measurements per
#>  subject


# Advanced within-subjects analysis with custom settings
jjwithinstats(
    data = iris_wide,
    dep1 = "Time1",
    dep2 = "Time2",
    dep3 = "Time3",
    typestatistics = "nonparametric",
    pairwisecomparisons = TRUE,
    pairwisedisplay = "significant",
    centralityplotting = TRUE,
    centralitytype = "nonparametric",
    pointpath = TRUE,
    mytitle = "Repeated Measurements Over Time",
    xtitle = "Time Point",
    ytitle = "Measurement Value"
)
#> 
#>  VIOLIN PLOTS TO COMPARE WITHIN GROUPS
#> 
#>  ✅ Ready for analysis: Violin Plot comparing 3 repeated measurements.
#> 
#>  <div style='background-color:#f8f9fa;padding:15px;margin:10px
#>  0;border-left:4px solid #007bff;'><h4
#>  style='margin-top:0;color:#007bff;'>Clinical Context
#> 
#>  This analysis compares 3 measurements from the same subjects. Useful
#>  for: biomarker tracking, treatment response monitoring, disease
#>  progression assessment.
#> 
#>  Test Used: Friedman test compares medians across time points without
#>  assuming normal distribution (robust for skewed biomarker data).
#> 
#>  🔍 What to look for:
#>  • Statistical significance (p < 0.05) indicates real changes over time
#>  • Effect sizes show practical importance
#>  • Individual trajectories reveal response patterns
#>  • Outliers may indicate treatment non-responders or measurement errors
#> 
#>  <div style='background-color:#f0f8f0;padding:10px;border:1px solid
#>  #28a745;'>Analysis: Friedman test with 3 measurements per subject
#>  Configuration:
#>  ✓ Pairwise comparisons enabled
#>  ✓ Central tendency displayed
#>  ✓ Individual trajectories shown


# Robust analysis with mtcars dataset (horsepower measurements)
data(mtcars)
mtcars_wide <- data.frame(
    Car = rownames(mtcars)[1:20],
    HP_Stock = mtcars$hp[1:20],
    HP_Tuned = mtcars$hp[1:20] * 1.15,
    HP_Racing = mtcars$hp[1:20] * 1.35
)
jjwithinstats(
    data = mtcars_wide,
    dep1 = "HP_Stock",
    dep2 = "HP_Tuned",
    dep3 = "HP_Racing",
    typestatistics = "robust",
    centralityplotting = TRUE,
    centralitytype = "robust",
    effsizetype = "unbiased",
    conflevel = 0.99,
    k = 3
)
#> 
#>  VIOLIN PLOTS TO COMPARE WITHIN GROUPS
#> 
#>  ✅ Ready for analysis: Violin Plot comparing 3 repeated measurements.
#> 
#>  <div style='background-color:#f8f9fa;padding:15px;margin:10px
#>  0;border-left:4px solid #007bff;'><h4
#>  style='margin-top:0;color:#007bff;'>Clinical Context
#> 
#>  This analysis compares 3 measurements from the same subjects. Useful
#>  for: biomarker tracking, treatment response monitoring, disease
#>  progression assessment.
#> 
#>  Test Used: Robust test uses trimmed means, reducing influence of
#>  outliers common in clinical measurements.
#> 
#>  🔍 What to look for:
#>  • Statistical significance (p < 0.05) indicates real changes over time
#>  • Effect sizes show practical importance
#>  • Individual trajectories reveal response patterns
#>  • Outliers may indicate treatment non-responders or measurement errors
#> 
#>  <div style='background-color:#f0f8f0;padding:10px;border:1px solid
#>  #28a745;'>Analysis: Robust repeated measures test with 3 measurements
#>  per subject
#>  Configuration:
#>  ✓ Central tendency displayed


# Bayesian analysis with ToothGrowth dataset (growth measurements)
data(ToothGrowth)
tooth_wide <- data.frame(
    Subject = 1:20,
    Week1 = ToothGrowth$len[1:20],
    Week2 = ToothGrowth$len[21:40],
    Week3 = ToothGrowth$len[41:60]
)
jjwithinstats(
    data = tooth_wide,
    dep1 = "Week1",
    dep2 = "Week2",
    dep3 = "Week3",
    typestatistics = "bayes",
    bfmessage = TRUE,
    centralityplotting = TRUE,
    mytitle = "Tooth Growth Over Time"
)
#> 
#>  VIOLIN PLOTS TO COMPARE WITHIN GROUPS
#> 
#>  ✅ Ready for analysis: Violin Plot comparing 3 repeated measurements.
#> 
#>  <div style='background-color:#f8f9fa;padding:15px;margin:10px
#>  0;border-left:4px solid #007bff;'><h4
#>  style='margin-top:0;color:#007bff;'>Clinical Context
#> 
#>  This analysis compares 3 measurements from the same subjects. Useful
#>  for: biomarker tracking, treatment response monitoring, disease
#>  progression assessment.
#> 
#>  Test Used: Bayesian analysis provides evidence for/against
#>  differences, useful when traditional p-values are inconclusive.
#> 
#>  🔍 What to look for:
#>  • Statistical significance (p < 0.05) indicates real changes over time
#>  • Effect sizes show practical importance
#>  • Individual trajectories reveal response patterns
#>  • Outliers may indicate treatment non-responders or measurement errors
#> 
#>  <div style='background-color:#f0f8f0;padding:10px;border:1px solid
#>  #28a745;'>Analysis: Bayesian repeated measures analysis with 3
#>  measurements per subject
#>  Configuration:
#>  ✓ Central tendency displayed