Publication-Ready Plots (ggpubr) — jjpubr • ClinicoPath

Create publication-ready plots using the ggpubr package. Provides easy-to-use functions for creating customized plots with statistical annotations for scientific publications.

Usage

jjpubr(
  data,
  plotType = "boxplot",
  xvar,
  yvar,
  groupvar = NULL,
  facetvar = NULL,
  addStats = FALSE,
  statMethod = "auto",
  pairwiseComparisons = FALSE,
  addCorr = FALSE,
  corrMethod = "pearson",
  addSmoothLine = TRUE,
  addMarginal = FALSE,
  addDensity = FALSE,
  bins = 30,
  addMean = FALSE,
  addMedian = FALSE,
  addPoints = FALSE,
  pointAlpha = 0.6,
  addMeanSD = FALSE,
  palette = "jco",
  fillColor = "#0073C2FF",
  title = "",
  xlab = "",
  ylab = "",
  legendPosition = "right",
  theme = "pubr",
  plotWidth = 600,
  plotHeight = 500
)

Arguments

data: the data as a data frame
plotType: Type of plot: boxplot, violin, scatter, histogram, density, barplot, dotplot, line, errorplot
xvar: X-axis variable
yvar: Y-axis variable (continuous)
groupvar: Grouping variable for coloring points/boxes
facetvar: Variable for faceting plots into panels
addStats: Whether to add statistical comparison p-values
statMethod: Method for statistical comparison
pairwiseComparisons: Whether to show all pairwise comparisons
addCorr: Whether to add correlation statistics
corrMethod: Correlation method
addSmoothLine: Whether to add smooth trend line
addMarginal: Whether to add marginal histograms
addDensity: Whether to add density curve overlay
bins: Number of histogram bins
addMean: Whether to add mean line
addMedian: Whether to add median line
addPoints: Whether to add jittered points
pointAlpha: Alpha transparency for points
addMeanSD: Whether to show mean ± SD
palette: Color palette name
fillColor: Fill color for single-variable plots
title: Plot title
xlab: X-axis label
ylab: Y-axis label
legendPosition: Legend position
theme: Theme name
plotWidth: Plot width in pixels
plotHeight: Plot height in pixels

Value

A results object containing:

`results$todo`					a html
`results$plot`					an image
`results$statistics`					a table
`results$correlation`					a table
`results$descriptives`					a table
`results$plotInfo`					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$statistics$asDF

as.data.frame(results$statistics)

Details

Available plot types:

Box plots with statistical comparisons
Violin plots with distribution visualization
Scatter plots with correlation analysis
Histograms with density overlays
Density plots for distribution analysis
Bar plots for categorical data

Each plot type supports:

Automatic statistical testing and p-value annotations
Publication-ready themes
Customizable color palettes
Grouping and faceting options

Examples

# Box plot with statistical comparison
jjpubr(
    data = mtcars,
    plotType = "boxplot",
    xvar = "cyl",
    yvar = "mpg",
    addStats = TRUE,
    palette = "jco"
)

# Scatter plot with correlation
jjpubr(
    data = mtcars,
    plotType = "scatter",
    xvar = "wt",
    yvar = "mpg",
    addCorr = TRUE,
    addMarginal = TRUE
)

# Histogram with density overlay
jjpubr(
    data = mtcars,
    plotType = "histogram",
    xvar = "mpg",
    addDensity = TRUE,
    fillColor = "#0073C2FF"
)