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rCode: Enhanced R Functions for Statistical Analysis and Reporting

Created by Mark Colley

rCode is a collection of custom R functions that streamline statistical analysis and result reporting. Built upon popular R packages such as ggstatsplot and ARTool, this collection offers a wide array of tools for simplifying reproducible analyses, generating high-quality visualizations, and producing APA-compliant outputs.

The primary goal of this package is to significantly reduce repetitive coding efforts, allowing you to focus on interpreting results. Whether you're dealing with ANOVA assumptions, reporting effect sizes, or creating publication-ready visualizations, rCode makes these tasks easier.

Key Features

  • Automated Assumption Checking: For ANOVA models, automatically verify normality and homogeneity of variance.
  • Enhanced ggstatsplot Functions: Easily switch between parametric and non-parametric versions of tests based on the data's characteristics.
  • APA-Compliant Reporting: Copy-paste-ready results in LaTeX format, suitable for academic publications.
  • Custom Visualizations: Generate effect plots and multi-objective optimization plots with minimal effort.
  • Pareto Analysis and Post-Hoc Tests: Automate these analyses and produce formatted outputs.

Installation

TypeCommand
Developmentdevtools::source_url("https://raw.githubusercontent.com/M-Colley/rCode/main/r_functionality.R")

Summary of Benefits

  • Code Reduction: Automates common tasks in data analysis, such as assumption checks and reporting.
  • Copy-Paste Ready Outputs: Streamlines report generation with LaTeX-ready text outputs.
  • Flexible Visualizations: Customize plots and output professional-quality graphics with ease.
  • Easy-to-Update: Modifying analyses or text outputs is simple and consistent.

Primary Functions

checkAssumptionsForAnova

This function suite checks normality and homogeneity of variance assumptions for ANOVA models. Takes a vector of factors. For details on assumptions checking, refer to Datanovia.

Example:

checkAssumptionsForAnova(data = main_df, y = "dependent_var", factors = c("factor1", "factor2"))

ggwithinstatsWithPriorNormalityCheckAsterisk and ggbetweenstatsWithPriorNormalityCheckAsterisk

These functions include APA-compliant asterisks (e.g., *** for p < 0.001) on your ggwithinstats or ggbetweenstats plots. They automatically adjust for the appropriate test based on the data's normality.

Note: Avoid using these functions if your data has more than two groups, as geom_signif does not support more than two groups.

generateEffectPlot

Generates a plot that emphasizes either main effects or interaction effects, with clear formatting and options for publication-ready visuals. This function supports customizing group colors, axis labels, and plot size.

Example:

generateEffectPlot(df = main_df, x = "factor1", y = "dependent_var", fillColourGroup = "group", ytext = "Y Label", xtext = "X Label", legendPos = c(0.1, 0.2), shownEffect = "interaction")

reportNPAV

Generates APA-compliant LaTeX output for within-subject designs analyzed using np.anova. The function handles both main and interaction effects. The necessary LaTeX commands are:

\newcommand{\F}[3]{$F({#1},{#2})={#3}$}
\newcommand{\p}{\textit{p=}}
\newcommand{\pminor}{\textit{p$<$}}

Example:

model <- np.anova(tlx_mental ~ factor1 * factor2 + Error(Subject / factor1), data = main_df)
reportNPAV(model, "Dependent Variable")

reportNPAVChi

Similar to reportNPAV, but for between-subject designs. This function generates formatted LaTeX output for non-parametric ANOVA results. LaTeX commands:

\newcommand{\F}[3]{$F({#1},{#2})={#3}$}
\newcommand{\p}{\textit{p=}}
\newcommand{\pminor}{\textit{p$<$}}

reportNparLD

Reports the model produced by nparLD in APA-compliant format. While still supported, this function has largely been superseded by np.anova for non-parametric variance analyses.

reportMeanAndSD

For each level of an independent variable, this function calculates the mean and standard deviation of a dependent variable and returns them in APA-compliant LaTeX format:

\newcommand{\m}{\textit{M=}}
\newcommand{\sd}{\textit{SD=}}

Example:

reportMeanAndSD(main_df, iv = "factor1", dv = "dependent_var")

reportDunnTest and reportDunnTestTable

This function summarizes the results of FSA::dunnTest objects in text or table form. Both versions output LaTeX-ready results:

\newcommand{\padjminor}{\textit{p$_{adj}<$}}
\newcommand{\padj}{\textit{p$_{adj}$=}}

Example:

d <- dunnTest(dependent_var ~ factor1, data = main_df, method = "holm")
reportDunnTest(main_df, d, iv = "factor1", dv = "dependent_var")

reportART

Generates LaTeX-formatted results from art models for factorial designs. The necessary LaTeX commands are:

\newcommand{\F}[3]{$F({#1},{#2})={#3}$}
\newcommand{\p}{\textit{p=}}
\newcommand{\pminor}{\textit{p$<$}}

Example:

model <- art(formula = dependent_var ~ factor1 * factor2 + Error(Subject / (factor1 * factor2)), data = main_df) |> anova()
reportART(anova(model), "Dependent Variable")

add_pareto_emoa_column

This function adds a Pareto front classification column to a dataset, useful in multi-objective optimization scenarios.

Attention: must be done per User - Conditon etc group.

Example:

# This would do it over **all** participants and **all** conditions
objectives <- c("objective1", "objective2", "objective3")
main_df <- add_pareto_emoa_column(main_df, objectives)

# This would do it **per** participant and **per** condition combination
# (so far, does not natively support piping ``|>'')
main_df <- main_df |> 
  group_by(User_ID, ConditionID) |> 
  mutate(PARETO_EMOA = add_pareto_emoa_column(pick(everything()), objectives = objectives)$PARETO_EMOA) |> 
  ungroup()

generateMoboPlot and generateMoboPlot2

Creates a multi-objective optimization plot, visualizing sampling and optimization phases. This is particularly useful for visualizing iterations in optimization problems. generateMoboPlot2 is appropriate when using https://github.com/Pascal-Jansen/Bayesian-Optimization-for-Unity/releases starting version 1.1.0.

Example:

generateMoboPlot2(data = main_df, x = "Iteration", y = "objective1", fillColourGroup = "group", ytext = "Y Axis Label")

Example Plot:

remove_outliers_REI

Calculates the Response Entropy Index (REI) and flags suspicious entries based on their REI percentile. This function is useful for identifying outliers in Likert scale data.

Example:

result <- remove_outliers_REI(main_df, header = TRUE, variables = "var1,var2,var3", range = c(1, 5))

replace_values

Replaces specified values in a data frame with custom replacements. This can be used to clean or preprocess your data.

Example:

new_df <- replace_values(main_df, to_replace = c("bad_val1", "bad_val2"), replace_with = c("good_val1", "good_val2"))

Using NPAV (Lüpsen) with this package

reportNPAV() formats results from Lüpsen’s nonparametric ANOVA (np.anova) output. NPAV is not shipped with this package, and it is loaded manually by the user from Lüpsen’s site: https://www.uni-koeln.de/~luepsen/R/.

This step requires internet access, so it is documented here (not in @examples, which should run offline during package checks).

# Download Lüpsen's NPAV bundle (anova.lib) and load it into a dedicated environment
npav_file <- tempfile(fileext = ".lib")
utils::download.file(
  url      = "https://www.uni-koeln.de/~luepsen/R/anova.lib",
  destfile = npav_file,
  mode     = "wb",
  quiet    = TRUE
)

npav_env <- new.env(parent = base::emptyenv())
base::load(npav_file, envir = npav_env)

# Example
set.seed(1)
main_df <- data.frame(
  UserID     = factor(rep(1:12, each = 8)),
  Video      = factor(rep(c("V1", "V2"), times = 48)),
  gesture    = factor(rep(c("g1", "g2"), each = 4, times = 12)),
  eHMI       = factor(rep(c("off", "on"), each = 2, times = 24)),
  tlx_mental = rnorm(96)
)

model <- npav_env$np.anova(
  tlx_mental ~ Video * gesture * eHMI + Error(UserID / (gesture * eHMI)),
  data = main_df
)

reportNPAV(model, dv = "mental workload")

If download.file() is blocked in your environment, download anova.lib manually from the NPAV page and point npav_file to the local path





## Contact
For questions or remarks, please contact [Mark Colley](https://m-colley.github.io/).



## Citations

```bibtex
@misc{colley2024rcode,
  author       = {Mark Colley},
  title        = {rCode: Enhanced R Functions for Statistical Analysis and Reporting},
  year         = {2024},
  howpublished = {\url{https://github.com/M-Colley/rCode}},
  note         = {A collection of custom R functions for streamlining statistical analysis, visualizations, and APA-compliant reporting.},
  doi          = {10.5281/zenodo.16875755},
  url          = {https://doi.org/10.5281/zenodo.16875755},
}

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Version

Install

install.packages('colleyRstats')

Version

0.0.1

License

MIT + file LICENSE

Maintainer

Mark Colley

Last Published

December 19th, 2025

Functions in colleyRstats (0.0.1)

latexify_report

Transform text from report::report() into LaTeX-friendly output.
normalize

This function normalizes the values in a vector to the range [new_min, new_max] based on their original range [old_min, old_max].
reportDunnTestTable

report Dunn test as a table. Customizable with sensible defaults. Required commands in LaTeX: \newcommand{\padjminor}{\textit{p$_{adj}<$}} \newcommand{\padj}{\textit{p$_{adj}$=}} \newcommand{\rankbiserial}[1]{$r_{rb} = #1$}
rFromWilcoxAdjusted

rFromWilcoxAdjusted
rFromNPAV

Calculation based on Rosenthal's formula (1994). N stands for the number of measurements. Necessary command:
rFromWilcox

Calculation based on Rosenthal's formula (1994). N stands for the number of measurements.
ggwithinstatsWithPriorNormalityCheckAsterisk

Check the data's distribution. If non-normal, take the non-parametric variant of ggwithinstats. x and y have to be in parentheses, e.g., "ConditionID". Add Astersiks instead of p-values.
reportNparLD

Report the model produced by nparLD. The model provided must be the model generated by the command 'nparLD' nparLD (see https://CRAN.R-project.org/package=nparLD).
ggbetweenstatsWithPriorNormalityCheckAsterisk

Check the data's distribution. If non-normal, take the non-parametric variant of ggbetweenstats. x and y have to be in parentheses, e.g., "ConditionID".
reportDunnTest

Report dunnTest as text. Required commands in LaTeX: \newcommand{\padjminor}{\textit{p$_{adj}<$}} \newcommand{\padj}{\textit{p$_{adj}$=}} \newcommand{\rankbiserial}[1]{$r_{rb} = #1$}
stat_sum_df

Generating the sum and adding a crossbar.
remove_outliers_REI

Remove outliers and calculate REI
reshape_data

Reshape Excel Data Based on Custom Markers and Include Custom ID Column
reportNPAV

Generate the Latex-text based on the NPAV by Lüpsen (see https://www.uni-koeln.de/~luepsen/R/). Only significant main and interaction effects are reported. P-values are rounded for the third digit and partial eta squared values are provided when possible. Attention: the independent variables of the formula and the term specifying the participant must be factors (i.e., use as.factor()).
reportggstatsplot

Report statistical details for ggstatsplot.
reportggstatsplotPostHoc

Report significant post-hoc pairwise comparisons
reportMeanAndSD

Report the mean and standard deviation of a dependent variable for all levels of an independent variable rounded to the 2nd digit.
reportART

Generate the Latex-text based on the ARTool (see https://github.com/mjskay/ARTool). The ART result must be piped into an anova(). Only significant main and interaction effects are reported. P-values are rounded for the third digit. Attention: Effect sizes are not calculated! Attention: the independent variables of the formula and the term specifying the participant must be factors (i.e., use as.factor()).
debug_contr_error

Debug contrast errors in ANOVA-like models
data the data frame

Replace values across a data frame
generateMoboPlot

Generate a Multi-objective Optimization Plot
generateMoboPlot2

Generate a Multi-objective Optimization Plot
add_pareto_emoa_column

Add Pareto EMOA Column to a Data Frame
checkAssumptionsForAnova

Check the assumptions for an ANOVA with a variable number of factors: Normality and Homogeneity of variance assumption.
colleyRstats_setup

Configure Global R Environment for colleyRstats
generateEffectPlot

Function to define a plot, either showing the main or interaction effect in bold.
check_homogeneity_by_group

Check homogeneity of variances across groups
check_normality_by_group

Check normality for groups
ggwithinstatsWithPriorNormalityCheck

Check the data's distribution. If non-normal, take the non-parametric variant of ggwithinstats. x and y have to be in parentheses, e.g., "ConditionID".
ggbetweenstatsWithPriorNormalityCheck

Check the data's distribution. If non-normal, take the non-parametric variant of ggbetweenstats. x and y have to be in parentheses, e.g., "ConditionID".
not_empty

Ensure input is not empty