Title:	Functions to Streamline Statistical Analysis and Reporting
Version:	0.0.1
Description:	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, this package makes these tasks easier.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.5.0)
Imports:	ARTool, car, clipr, conflicted, dplyr (≥ 1.1.4), effectsize (≥ 1.0.1), FSA, ggplot2 (≥ 4.0.1), ggpmisc (≥ 0.6.3), ggsignif, ggstatsplot (≥ 0.13.4), ggtext, purrr, readxl, report (≥ 0.6.1), rlang, rstatix, see, stats, stringr, tidyr, utils, writexl, xtable
Suggests:	afex, BayesFactor, bayestestR, Cairo, dunn.test, DT, emmeans, emoa, flexdashboard, Hmisc, moocore (≥ 0.1.10), nparLD, psych, reporttools, rstantools, scales, sjPlot, stargazer, styler, tibble, testthat (≥ 3.3.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-12-15 11:14:44 UTC; Mark
Author:	Mark Colley [aut, cre, cph]
Maintainer:	Mark Colley <mark.colley@yahoo.de>
Repository:	CRAN
Date/Publication:	2025-12-19 15:00:07 UTC

Add Pareto EMOA Column to a Data Frame

Description

This function calculates the Pareto front for a given set of objectives in a data frame and adds a new column, PARETO_EMOA, which indicates whether each row in the data frame belongs to the Pareto front.

Usage

add_pareto_emoa_column(data, objectives)

Arguments

Value

A data frame with the same columns as data, along with an additional column, PARETO_EMOA, which is TRUE for rows that are on the Pareto front and FALSE otherwise.

Examples

# Define objective columns
objectives <- c("trust", "predictability", "perceivedSafety", "Comfort")

# Example data frame
main_df <- data.frame(
  trust = runif(10),
  predictability = runif(10),
  perceivedSafety = runif(10),
  Comfort = runif(10)
)

# Add the Pareto front column
main_df <- add_pareto_emoa_column(data = main_df, objectives)
head(main_df)

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

Description

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

Usage

checkAssumptionsForAnova(data, y, factors)

Arguments

Value

A message indicating whether to use parametric or non-parametric ANOVA

Examples

set.seed(123)

main_df <- data.frame(
  tlx_mental      = rnorm(40),
  Video           = factor(rep(c("A", "B"), each = 20)),
  DriverPosition  = factor(rep(c("Left", "Right"), times = 20))
)

checkAssumptionsForAnova(
  data    = main_df,
  y       = "tlx_mental",
  factors = c("Video", "DriverPosition")
)

Check homogeneity of variances across groups

Description

Check homogeneity of variances across groups

Usage

check_homogeneity_by_group(data, x, y)

Arguments

Value

TRUE if Levene's test is non-significant (p >= .05), FALSE otherwise

Check normality for groups

Description

Check normality for groups

Usage

check_normality_by_group(data, x, y)

Arguments

Value

TRUE if all groups are normal, FALSE otherwise

Configure Global R Environment for colleyRstats

Description

Sets ggplot2 themes and conflict preferences to match the standards used in the colleyRstats workflow.

Usage

colleyRstats_setup(
  set_options = TRUE,
  set_theme = TRUE,
  set_conflicts = TRUE,
  print_citation = TRUE,
  verbose = TRUE
)

Arguments

Value

Invisibly returns NULL.

Examples

# Runs everywhere, no extra packages, no session side effects
colleyRstats::colleyRstats_setup(
  set_options = FALSE,
  set_theme = FALSE,
  set_conflicts = FALSE,
  print_citation = FALSE,
  verbose = FALSE
)


# Full setup (requires suggested packages; changes session defaults)
if (requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("see", quietly = TRUE)) {
  local({
    old_theme <- ggplot2::theme_get()
    on.exit(ggplot2::theme_set(old_theme), add = TRUE)

    colleyRstats::colleyRstats_setup(
      set_options = FALSE,
      set_conflicts = FALSE,   # avoid persisting conflict prefs in checks
      print_citation = FALSE,
      verbose = TRUE
    )

    ggplot2::ggplot(mtcars, ggplot2::aes(mpg, wt)) +
      ggplot2::geom_point()
  })
}

Replace values across a data frame

Description

Replace all occurrences of given values in all columns of a data frame.

The data data frame contains a collection of records, with attributes organized in columns. It may include various types of values, such as numerical, categorical, or textual data.

Usage

replace_values(data, to_replace, replace_with)

Arguments

Value

Modified data frame with specified values replaced.

Examples

data <- data.frame(
  q1 = c("neg2", "neg1", "0"),
  q2 = c("1", "neg2", "neg1")
)

replace_values(
  data,
  to_replace = c("neg2", "neg1"),
  replace_with = c("-2", "-1")
)

Debug contrast errors in ANOVA-like models

Description

Debug contrast errors in ANOVA-like models

Usage

debug_contr_error(dat, subset_vec = NULL)

Arguments

Value

A list with two elements:

nlevels

Integer vector giving the number of levels for each factor variable in dat.

levels

List of factor level labels for each factor variable in dat.

Examples

dat <- data.frame(
  group = factor(rep(letters[1:3], each = 3)),
  score = rnorm(9)
)

debug_contr_error(dat = dat)

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

Description

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

Usage

generateEffectPlot(
  data,
  x,
  y,
  fillColourGroup,
  ytext = "testylab",
  xtext = "testxlab",
  legendPos = c(0.1, 0.23),
  legendHeading = NULL,
  shownEffect = "main",
  effectLegend = FALSE,
  effectDescription = NULL,
  xLabelsOverwrite = NULL,
  useLatexMarkup = FALSE,
  numberColors = 6
)

Arguments

Value

a plot

Examples

set.seed(123)
main_df <- data.frame(
  strategy    = factor(rep(c("A", "B"), each = 20)),
  Emotion     = factor(rep(c("Happy", "Sad"), times = 20)),
  trust_mean  = rnorm(40, mean = 5, sd = 1)
)

generateEffectPlot(
  data = main_df,
  x = "strategy",
  y = "trust_mean",
  fillColourGroup = "Emotion",
  ytext = "Trust",
  xtext = "Strategy",
  legendPos = c(0.1, 0.23)
)

Generate a Multi-objective Optimization Plot

Description

This function generates a multi-objective optimization plot using ggplot2. The plot visualizes the relationship between the x and y variables, grouping and coloring by a fill variable, with the option to customize legend position, labels, and annotation of sampling and optimization phases.

Usage

generateMoboPlot(
  data,
  x,
  y,
  fillColourGroup = "ConditionID",
  ytext,
  legendPos = c(0.65, 0.85),
  numberSamplingSteps = 5,
  labelPosFormulaY = "top",
  verticalLinePosY = 0.75
)

Arguments

Value

A ggplot object representing the multi-objective optimization plot, ready to be rendered.

Examples

library(ggplot2)
library(ggpmisc)

# Example with numeric x-axis
df <- data.frame(
  x = 1:20,
  y = rnorm(20),
  ConditionID = rep(c("A", "B"), 10)
)
generateMoboPlot(df, x = "x", y = "y")

# Example with factor x-axis
df <- data.frame(
  x = factor(rep(1:5, each = 4)),
  y = rnorm(20),
  ConditionID = rep(c("A", "B"), 10)
)
generateMoboPlot(df, x = "x", y = "y", numberSamplingSteps = 3)

Generate a Multi-objective Optimization Plot

Description

This function generates a multi-objective optimization plot using ggplot2. The plot visualizes the relationship between the x and y variables, grouping and coloring by a fill variable, with the option to customize legend position, labels, and annotation of sampling and optimization phases. Appropriate if you use https://github.com/Pascal-Jansen/Bayesian-Optimization-for-Unity in version 1.1.0 or higher.

Usage

generateMoboPlot2(
  data,
  x = "Iteration",
  y,
  phaseCol = "Phase",
  fillColourGroup = "",
  ytext,
  legendPos = c(0.65, 0.85),
  labelPosFormulaY = "top",
  verticalLinePosY = 0.75
)

Arguments

Value

A ggplot object representing the multi-objective optimization plot, ready to be rendered.

Examples

library(ggplot2)
library(ggpmisc)

# Example with numeric x-axis
df <- data.frame(
  x = 1:20,
  y = rnorm(20),
  ConditionID = rep(c("A", "B"), 10),
  Phase = rep(c("Sampling", "Optimization"), 10)
)
generateMoboPlot2(data = df, x = "x", y = "y")

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".

Description

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".

Usage

ggbetweenstatsWithPriorNormalityCheck(
  data,
  x,
  y,
  ylab,
  xlabels,
  showPairwiseComp = TRUE,
  plotType = "boxviolin"
)

Arguments

Value

A ggplot object produced by ggstatsplot::ggbetweenstats, which can be printed or further modified with +.

Examples

set.seed(123)

# Toy within-subject style data
main_df <- data.frame(
  Participant = factor(rep(1:20, each = 3)),
  CondID      = factor(rep(c("A", "B", "C"), times = 20)),
  tlx_mental  = rnorm(60, mean = 50, sd = 10)
)

# Custom x-axis labels
labels_xlab <- c("Condition A", "Condition B", "Condition C")


ggbetweenstatsWithPriorNormalityCheck(
  data = main_df,
  x = "CondID",
  y = "tlx_mental", ylab = "Mental Demand",
  xlabels = labels_xlab,
  showPairwiseComp = TRUE
)

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".

Description

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".

Usage

ggbetweenstatsWithPriorNormalityCheckAsterisk(
  data,
  x,
  y,
  ylab,
  xlabels,
  plotType = "boxviolin"
)

Arguments

Value

A ggplot object produced by ggstatsplot::ggbetweenstats with additional significance annotations, which can be printed or modified.

Examples

set.seed(123)

# Toy within-subject style data
main_df <- data.frame(
  Participant = factor(rep(1:20, each = 3)),
  CondID      = factor(rep(c("A", "B", "C"), times = 20)),
  tlx_mental  = rnorm(60, mean = 50, sd = 10)
)

# Custom x-axis labels
labels_xlab <- c("Condition A", "Condition B", "Condition C")


ggbetweenstatsWithPriorNormalityCheckAsterisk(
  data = main_df,
  x = "CondID", y = "tlx_mental", ylab = "Mental Demand", xlabels = labels_xlab
)

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".

Description

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".

Usage

ggwithinstatsWithPriorNormalityCheck(
  data,
  x,
  y,
  ylab,
  xlabels = NULL,
  showPairwiseComp = TRUE,
  plotType = "boxviolin"
)

Arguments

Value

A ggplot object produced by ggstatsplot::ggwithinstats with additional significance annotations, which can be printed or modified.

Examples

#'   set.seed(123)

# Toy within-subject style data
main_df <- data.frame(
  Participant = factor(rep(1:20, each = 3)),
  CondID      = factor(rep(c("A", "B", "C"), times = 20)),
  tlx_mental  = rnorm(60, mean = 50, sd = 10)
)

# Custom x-axis labels
labels_xlab <- c("Condition A", "Condition B", "Condition C")


ggwithinstatsWithPriorNormalityCheck(
  data = main_df,
  x = "CondID", y = "tlx_mental",
  ylab = "Mental Demand",
  xlabels = labels_xlab,
  showPairwiseComp = TRUE
)

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.

Description

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.

Usage

ggwithinstatsWithPriorNormalityCheckAsterisk(
  data,
  x,
  y,
  ylab,
  xlabels,
  plotType = "boxviolin"
)

Arguments

Value

A ggplot object produced by ggstatsplot::ggwithinstats with additional significance annotations, which can be printed or modified.

Examples

set.seed(123)

# Toy within-subject style data
main_df <- data.frame(
  Participant = factor(rep(1:20, each = 3)),
  CondID      = factor(rep(c("A", "B", "C"), times = 20)),
  tlx_mental  = rnorm(60, mean = 50, sd = 10)
)

# Custom x-axis labels
labels_xlab <- c("Condition A", "Condition B", "Condition C")


ggwithinstatsWithPriorNormalityCheckAsterisk(
  data = main_df,
  x = "CondID", y = "tlx_mental",
  ylab = "Mental Demand", xlabels = labels_xlab
)

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

Description

This function transforms the text output from report::report() by performing several substitutions to prepare the text for LaTeX typesetting. In particular, it replaces instances of R2, ⁠%⁠, and ~ with the corresponding LaTeX code. Additionally, it provides options to:

• Omit bullet items marked as "non-significant" (when only_sig = TRUE).

• Remove a concluding note about standardized parameters (when remove_std = TRUE).

• Wrap bullet items in a LaTeX itemize environment or leave them as plain text (controlled by itemize).

Usage

latexify_report(
  x,
  print_result = TRUE,
  only_sig = FALSE,
  remove_std = FALSE,
  itemize = TRUE
)

Arguments

Value

A single string with the LaTeX-friendly formatted report text.

Examples

if (requireNamespace("report", quietly = TRUE)) {
  # Simple linear model on the iris dataset
  model <- stats::lm(
    Sepal.Length ~ Sepal.Width + Petal.Length,
    data = datasets::iris
  )

  # Format the report output, showing only significant items, removing the
  # standard note, and wrapping bullet items in an itemize environment.
  latexify_report(
    report::report(model),
    only_sig = TRUE,
    remove_std = TRUE,
    itemize = TRUE
  )
}

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

Description

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

Usage

normalize(x_vector, old_min, old_max, new_min, new_max)

Arguments

Value

A numeric vector with the normalized values.

Examples

normalize(c(1, 2, 3, 4, 5), 1, 5, 0, 1)

Ensure input is not empty

Description

Stops execution if x is NULL, empty, or contains only NAs.

Usage

not_empty(x, msg = "Input must not be empty.")

Arguments

Value

Invisible TRUE if valid.

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

Description

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

Usage

rFromNPAV(pvalue, N)

Arguments

Value

Invisibly returns a list with components:

• r: effect size as a numeric scalar.

• z: corresponding z-statistic.

• text: LaTeX-formatted character string that is also sent to the console.

Examples

rFromNPAV(0.02, N = 180)

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

Description

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

Usage

rFromWilcox(wilcoxModel, N)

Arguments

Value

Invisibly returns a list with components:

• r: effect size as a numeric scalar.

• z: corresponding z-statistic.

• text: character string that is also sent to the console.

Examples

set.seed(1)
d <- data.frame(
  group = rep(c("A", "B"), each = 10),
  value = rnorm(20)
)
w <- stats::wilcox.test(value ~ group, data = d, exact = FALSE)
rFromWilcox(w, N = nrow(d))

rFromWilcoxAdjusted

Description

rFromWilcoxAdjusted

Usage

rFromWilcoxAdjusted(wilcoxModel, N, adjustFactor)

Arguments

Value

Invisibly returns a list with components:

• r: adjusted effect size as a numeric scalar.

• z: adjusted z-statistic.

• text: character string that is also sent to the console.

Examples

set.seed(1)
d <- data.frame(
  group = rep(c("A", "B"), each = 10),
  value = rnorm(20)
)
w <- stats::wilcox.test(value ~ group, data = d, exact = FALSE)
rFromWilcoxAdjusted(w, N = nrow(d), adjustFactor = 2)

Remove outliers and calculate REI

Description

This function takes a data frame, optional header information, variables to consider, and a range for a Likert scale. It then calculates the Response Entropy Index (REI) and flags suspicious entries based on percentiles.

Usage

remove_outliers_REI(df, header = FALSE, variables = "", range = c(1, 5))

Arguments

Details

For more information on the REI method, refer to: Response Entropy Index Method

Value

A data frame with calculated REI, percentile, and a 'Suspicious' flag.

Examples

df <- data.frame(var1 = c(1, 2, 3), var2 = c(2, 3, 4))
result <- remove_outliers_REI(df, TRUE, "var1,var2", c(1, 5))

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()).

Description

To easily copy and paste the results to your manuscript, the following commands must be defined in Latex: \newcommand{\F}[3]{$F({#1},{#2})={#3}$} \newcommand{\p}{\textit{p=}} \newcommand{\pminor}{\textit{p$<$}}

Usage

reportART(model, dv = "Testdependentvariable", write_to_clipboard = FALSE)

Arguments

Value

A message describing the statistical results.

Examples

if (requireNamespace("ARTool", quietly = TRUE)) {
  set.seed(123)

  main_df <- data.frame(
    tlx_mental = stats::rnorm(80),
    Video      = factor(rep(c("A", "B"), each = 40)),
    gesture    = factor(rep(c("G1", "G2"), times = 40)),
    eHMI       = factor(rep(c("On", "Off"), times = 40)),
    UserID     = factor(rep(1:20, each = 4))
  )

  art_model <- ARTool::art(
    tlx_mental ~ Video * gesture * eHMI +
      Error(UserID / (gesture * eHMI)),
    data = main_df
  )

  model_anova <- stats::anova(art_model)
  reportART(model_anova, dv = "mental demand")
}

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

Description

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

Usage

reportDunnTest(d, data, iv = "testiv", dv = "testdv")

Arguments

Value

A message describing the statistical results.

Examples

if (requireNamespace("FSA", quietly = TRUE)) {
  # Use built-in iris data
  data(iris)

  # Dunn test on Sepal.Length by Species
  d <- FSA::dunnTest(Sepal.Length ~ Species,
    data   = iris,
    method = "holm"
  )

  # Report the Dunn test
  reportDunnTest(d,
    data = iris,
    iv   = "Species",
    dv   = "Sepal.Length"
  )
}

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]{$r_{rb} = #1$}

Description

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]{$r_{rb} = #1$}

Usage

reportDunnTestTable(
  d = NULL,
  data,
  iv = "testiv",
  dv = "testdv",
  orderByP = FALSE,
  numberDigitsForPValue = 4,
  latexSize = "small",
  orderText = TRUE
)

Arguments

Value

A message describing the statistical results in a table.

Examples

if (requireNamespace("FSA", quietly = TRUE)) {
  # Use built-in iris data
  data(iris)

  # Dunn test on Sepal.Length by Species
  d <- FSA::dunnTest(Sepal.Length ~ Species,
    data   = iris,
    method = "holm"
  )

  # Report the Dunn test
  reportDunnTestTable(d,
    data = iris,
    iv   = "Species",
    dv   = "Sepal.Length"
  )
}

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

Description

#' To easily copy and paste the results to your manuscript, the following commands must be defined in Latex: \newcommand{\m}{\textit{M=}} \newcommand{\sd}{\textit{SD=}}

Usage

reportMeanAndSD(data, iv = "testiv", dv = "testdv")

Arguments

Value

Mean and SD values

Examples

example_data <- data.frame(Condition = rep(c("A", "B", "C"),
each = 10), TLX1 = stats::rnorm(30))

reportMeanAndSD(example_data, iv = "Condition", dv = "TLX1")

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()).

Description

To easily copy and paste the results to your manuscript, the following commands must be defined in Latex: \newcommand{\F}[3]{$F({#1},{#2})={#3}$} \newcommand{\p}{\textit{p=}} \newcommand{\pminor}{\textit{p$<$}}

Usage

reportNPAV(model, dv = "Testdependentvariable", write_to_clipboard = FALSE)

Arguments

Value

A message describing the statistical results.

Examples

model <- data.frame(
  Df = c(1, 1, 10),
  `F value` = c(6.12, 5.01, NA),
  `Pr(>F)` = c(0.033, 0.045, NA),
  check.names = FALSE
)
rownames(model) <- c("Video", "gesture:eHMI", "Residuals")
reportNPAV(model, dv = "mental workload")

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).

Description

#' Only significant main and interaction effects are reported. P-values are rounded for the third digit and relative treatment effects (RTE) are included when available. Attention: the independent variables of the formula and the term specifying the participant must be factors (i.e., use as.factor()).

Usage

reportNparLD(model, dv = "Testdependentvariable", write_to_clipboard = FALSE)

Arguments

Details

#' To easily copy and paste the results to your manuscript, the following commands must be defined in Latex: \newcommand{\F}{\textit{F=}} \newcommand{\p}{\textit{p=}} \newcommand{\pminor}{\textit{p$<$}}

Value

A message describing the statistical results.

Examples

if (requireNamespace("nparLD", quietly = TRUE)) {
  # Small toy data set for nparLD
  set.seed(123)
  example_data <- data.frame(
    Subject = factor(rep(1:10, each = 3)),
    Time    = factor(rep(c("T1", "T2", "T3"), times = 10)),
    TLX1    = stats::rnorm(30, mean = 50, sd = 10)
  )

  # Fit nparLD model
  model <- nparLD::nparLD(
    TLX1 ~ Time,
    data        = example_data,
    subject     = "Subject",
    description = FALSE
  )

  # Report the nparLD result
  reportNparLD(model, dv = "TLX1")
  }

Report statistical details for ggstatsplot.

Description

Report statistical details for ggstatsplot.

Usage

reportggstatsplot(
  p,
  iv = "independent",
  dv = "Testdependentvariable",
  write_to_clipboard = FALSE
)

Arguments

Value

A message describing the statistical results.

Examples

library(ggstatsplot)
library(dplyr)

# Generate a plot
plt <- ggbetweenstats(mtcars, am, mpg)

reportggstatsplot(plt, iv = "am", dv = "mpg")

Report significant post-hoc pairwise comparisons

Description

This function extracts significant pairwise comparisons from a ggstatsplot object, calculates the mean and standard deviation for the groups involved using the raw data, and prints LaTeX-formatted sentences reporting the results.

Usage

reportggstatsplotPostHoc(
  data,
  p,
  iv = "testiv",
  dv = "testdv",
  label_mappings = NULL
)

Arguments

Value

No return value. The function prints LaTeX-formatted text to the console.

LaTeX Requirements

To easily copy and paste the results to your manuscript, the following commands (or similar) must be defined in your LaTeX preamble, as the function outputs commands taking arguments (e.g., ⁠\m{value}⁠):

  \newcommand{\m}[1]{\textit{M}=#1}
  \newcommand{\sd}[1]{\textit{SD}=#1}
  \newcommand{\padj}[1]{$p_{adj}=#1$}
  \newcommand{\padjminor}[1]{$p_{adj}<#1$}

Examples

library(ggstatsplot)
library(dplyr)

# Generate a plot
plt <- ggbetweenstats(mtcars, am, mpg)

# Report stats
reportggstatsplotPostHoc(
  data = mtcars,
  p = plt,
  iv = "am",
  dv = "mpg",
  label_mappings = list("0" = "Automatic", "1" = "Manual")
)

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

Description

This function takes an Excel file with data in a wide format and transforms it to a long format. It includes a customizable "ID" column in the first position and repeats it for each slice. The function identifies sections of columns between markers that start with a user-defined string (default is "videoinfo") and appends those sections under the first section, aligning by column index.

Usage

reshape_data(
  input_filepath,
  sheetName = "Results",
  marker = "videoinfo",
  id_col = "ID",
  output_filepath
)

Arguments

Details

Relevant if you receive data in wide-format but cannot use built-in functionality due to naming (e.g., in LimeSurvey)

Attention, known bug: the ID column will first have only the IDs, this has to be fixed manually.

Value

None, writes the reshaped data to an Excel file specified by output_filepath.

Examples

if (requireNamespace(c("write_xlsx", "readxl"), quietly = TRUE)) {
  tmp_in  <- tempfile(fileext = ".xlsx")
  tmp_out <- tempfile(fileext = ".xlsx")

  # Minimal toy input that includes your required pieces:
  # an ID column and something that contains the marker value.
  toy <- data.frame(
    ID = c(1, 1, 2, 2),
    section = c("videoinfo", "videoinfo", "videoinfo", "videoinfo"),
    key = c("fps", "duration_s", "fps", "duration_s"),
    value = c(30, 12.3, 25, 9.8),
    stringsAsFactors = FALSE
  )

  writexl::write_xlsx(toy, tmp_in)

  reshape_data(
    input_filepath = tmp_in,
    marker = "videoinfo",
    id_col = "ID",
    output_filepath = tmp_out
  )

  out <- readxl::read_excel(tmp_out)
  print(out)
}

Generating the sum and adding a crossbar.

Description

Generating the sum and adding a crossbar.

Usage

stat_sum_df(fun, geom = "crossbar", ...)

Arguments

Value

A ggplot2 layer that can be added to a ggplot object.

Examples

  # Simple summary function: use the mean as y, ymin, and ymax
  mean_fun <- function(x) {
    m <- mean(x, na.rm = TRUE)
    data.frame(y = m, ymin = m, ymax = m)
  }

  ggplot2::ggplot(mtcars, ggplot2::aes(x = factor(cyl), y = mpg)) +
    stat_sum_df(mean_fun)