Package {gtstats}

Type:	Package
Title:	Beginner-Friendly Statistics and Publication-Ready Tables
Version:	1.0.0
Description:	Provides beginner-friendly functions for common descriptive and inferential statistical analyses, together with tools for creating publication-ready tables. The package includes data description, summary statistics, distribution assessment, group comparisons, effect sizes, correlations, proportions and rates with confidence intervals, crosstabs for epidemiological measures, plots, and modular table-building workflows. Statistical methods include unequal-variance comparisons described by Welch (1947) <doi:10.1093/biomet/34.1-2.28>, score confidence intervals described by Wilson (1927) <doi:10.1080/01621459.1927.10502953>, and robust variance assessment described by Brown and Forsythe (1974) <doi:10.1080/01621459.1974.10482955>.
URL:	https://gtstats.thinkdenominator.com/, https://github.com/ThinkDenominator/gtstats
BugReports:	https://github.com/thinkdenominator/gtstats/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.1.0)
Imports:	dplyr, tibble, gt, flextable, officer, stats, ggplot2, tidyselect
Suggests:	shiny, rstudioapi, rio, testthat (≥ 3.0.0), knitr, rmarkdown, pkgdown, webshot2
Config/testthat/edition:	3
VignetteBuilder:	knitr
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-09-11 14:56:33 UTC; drrubesh
Author:	Rubeshkumar Polani [aut, cre], Mogan Kaviprawin [aut], Manikandanesan Sakthivel [aut], Salin K Eliyas [aut], Yuvaraj Krishnamoorthy [aut]
Maintainer:	Rubeshkumar Polani <rubesh@thinkdenominator.com>
Repository:	CRAN
Date/Publication:	2026-09-21 21:50:02 UTC

Add confidence intervals to a summary table

Description

Add confidence intervals to all eligible summaries, or only to selected variables, without rebuilding the descriptive table. Categorical variables receive binomial confidence intervals for their displayed proportions. Continuous variables displayed with a mean receive a t-based confidence interval for the mean. Median-only summaries are left unchanged because a distribution-free median interval is not implied by the displayed IQR. Compact tables show the interval after the estimate without repeating the confidence level in every cell. Separate tables use concise CI child columns. The confidence level and interval method are stated once in the publication footnote.

Usage

add_ci(
  x,
  vars = NULL,
  conf.level = 0.95,
  method = c("wilson", "exact"),
  digits = NULL,
  type = NULL,
  estimate = NULL,
  numerator = NULL,
  denominator = NULL,
  sd = NULL,
  n = NULL,
  se = NULL,
  multiplier = 1,
  ci_name = NULL
)

Arguments

Value

The updated gtstats_summary object.

Examples

summary_table(mtcars, by = am, include = c(mpg, cyl), layout = "separate") |>
  add_ci()

summary_table(mtcars, by = am, include = c(mpg, cyl, vs)) |>
  add_ci(vars = c(mpg, vs), conf.level = 0.90)

aggregate_rates <- data.frame(
  Group = c("A", "B"), Events = c(8, 14), PersonYears = c(420, 510)
)
as_stats_table(aggregate_rates) |>
  add_ci(type = "rate", numerator = Events, denominator = PersonYears,
         multiplier = 1000)

Add p-values to a descriptive table

Description

Add a p-value column to a descriptive table by comparing each displayed variable across the grouping variable. P-values are calculated using compare_groups() and inserted once per variable, with optional superscript markers indicating which statistical test was used.

Usage

add_p(
  x,
  test = "auto",
  include = tidyselect::everything(),
  paired = FALSE,
  id = NULL,
  distribution_check = TRUE,
  var_equal = FALSE,
  correction = TRUE,
  fisher_seed = 1049L,
  p_adjust = c("none", setdiff(stats::p.adjust.methods, "none")),
  digits = 3
)

Arguments

Details

This function works only for descriptive tables created in mode = "summary" and requires a grouping variable supplied via summary_table().

Supported methods include:

• "auto"

• "welch_t"

• "t_test"

• "wilcox"

• "anova"

• "welch_anova"

• "kruskal"

• "chisq"

• "fisher"

• "mcnemar"

You may also provide a named character vector or named list to specify different methods for individual variables.

Automatic selection and audit trail

With test = "auto", add_p() delegates each comparison to compare_groups() using the same fixed selection policy: Welch t-test or Welch ANOVA by default when distribution guidance does not flag skewness; Student's t-test or classical ANOVA when var_equal = TRUE; rank-based tests when marked skewness is flagged; and chi-square or Fisher's exact test according to expected cell counts. For independent ordered factors, automatic mode uses the same chi-square/Fisher distribution comparison as other categorical variables; select "wilcox" or "kruskal" explicitly when a rank-based ordinal comparison is wanted. Shapiro-Wilk is supporting information only and does not itself select a test.

The publication table contains only the p-value and compact test markers. The full per-variable audit trail is retained in ⁠$assumptions⁠, ⁠$diagnostics⁠, ⁠$p_values⁠, and ⁠$denominators⁠. In particular, diagnostics_stats(x) records automatic selection, distribution guidance, expected cell counts where relevant, and observed group spread for independent continuous comparisons. Observed spread is descriptive context, not a variance-test gatekeeper; Welch methods do not require equal variances.

Value

An updated gtstats_summary object with a p-value column added. When paired = TRUE, ⁠$paired_p_notes⁠ records the complete-pair denominator for each displayed p-value; to_gt() displays this as a concise p-value footnote.

Examples

summary_table(mtcars, by = am, include = c(mpg, wt, cyl)) |>
  add_p()

summary_table(mtcars, by = am, include = c(mpg, wt, cyl)) |>
  add_p(test = c(mpg = "welch_t", wt = "wilcox", cyl = "chisq"))

summary_table(mtcars, by = am, include = c(mpg, wt, cyl)) |>
  add_p(include = c(mpg, wt))

Add a proportion row to a descriptive table

Description

Add a row showing the proportion of a selected level of a binary, categorical, or ordinal variable within a gtstats descriptive table.

Usage

add_proportion(
  x,
  var,
  level = NULL,
  ci = TRUE,
  conf.level = NULL,
  ci_method = NULL,
  display = NULL,
  layout = NULL,
  digits = NULL,
  label = NULL
)

Arguments

Details

This is useful when you want to highlight a specific category such as "Yes", "1", or "TRUE" within a Table 1 workflow. The row can be added overall, by groups, or both, depending on how the descriptive table was created.

If level = NULL, the function chooses a default level using the following order:

• "1"

• "Yes" / "yes"

• "TRUE" / "True" / "true"

• the second available level for binary variables

• otherwise the first available non-missing level

Wilson confidence intervals are used by default. Exact binomial intervals are available with ci_method = "exact".

Value

An updated gtstats_summary object with a proportion row appended.

Examples

summary_table(mtcars, by = am, overall = TRUE) |>
  add_proportion(var = vs)

summary_table(mtcars, by = am, overall = TRUE) |>
  add_proportion(var = vs, level = "1", ci = TRUE)

summary_table(mtcars) |>
  add_proportion(var = vs, ci = FALSE)

Add an event-rate row

Description

Add an event rate to a table created with summary_table(). Event counts and accumulated time are calculated from complete event-time pairs only. Event counts and time values must be finite. Exact Poisson confidence intervals are shown by default. A row with zero accumulated time is retained as ⁠\u2014⁠ and recorded as not estimable in the audit.

Usage

add_rate(
  x,
  event,
  time,
  label = NULL,
  multiplier = 1000,
  time_label = NULL,
  ci = TRUE,
  conf.level = 0.95,
  digits = 1,
  layout = NULL
)

Arguments

Details

Rate denominators are tracked separately from ordinary descriptive rows and are stated in the table audit and footnote.

Value

The updated gtstats_summary.

Examples

summary_table(mtcars, by = am, overall = TRUE) |>
  add_rate(
    event = carb,
    time = cyl,
    label = "Carburettor rate",
    multiplier = 1000
  )

# Rates may be added alongside ordinary summaries
summary_table(mtcars, by = am, include = mpg) |>
  add_rate(event = carb, time = cyl, multiplier = 1000)

Add a custom row to a descriptive table

Description

Add a user-defined row to a gtstats descriptive table. This is useful for inserting contextual information such as study period, data source, setting, or other custom annotations that should appear alongside the main table content.

Usage

add_row(x, label, overall = NULL, values = NULL, level = "")

Arguments

Details

The row is matched to the current table structure automatically:

• if overall = TRUE, an Overall column is supported

• if by is used, values must be named using displayed group column names

• if neither overall nor by is used, a single Value column is used

Value

An updated gtstats_summary object with the custom row appended.

Examples

res <- summary_table(mtcars, by = am, include = c(mpg, wt), overall = TRUE) |>
  add_row(
    label = "Study period",
    overall = "2020-2024",
    values = c("am = 1" = "2020-2024", "am = 0" = "2020-2024")
  )

Add summary rows to a descriptive table

Description

Add summary statistics to a gtstats descriptive table builder.

Usage

add_summary(
  x,
  vars,
  statistic = "recommended",
  percent = c("column", "row", "overall", "none"),
  categorical = c("n_percent", "n_over_N_percent", "n", "percent"),
  categorical_layout = c("combined", "separate"),
  overall_categorical = c("auto", "n_percent", "n_over_N_percent", "n", "percent"),
  show_dichotomous = c("all_levels", "single_row"),
  value = NULL,
  layout = NULL,
  missing = c("ifany", "always", "no", "as_category"),
  digits = 1
)

Arguments

Details

This function is the main way to populate a descriptive table with variable summaries. It supports both grouped and ungrouped tables and can optionally add an Overall column when the descriptive table was created with overall = TRUE.

Continuous variables can be displayed in one of four formats:

• "recommended": mean (SD) or median (IQR) as appropriate

• "mean_sd": mean (SD)

• "mean_se": mean (standard error)

• "mean_ci": mean with a t confidence interval

• "median_iqr": median (IQR)

• "both": mean (SD) and median (IQR)

Variable names may be supplied either as bare names, for example c(age, sex, bmi), or as a character vector, for example c("age", "sex", "bmi").

Value

An updated gtstats_summary object with summary rows added.

Examples

summary_table(mtcars, by = am) |>
  add_summary(vars = c(mpg, wt, cyl))

summary_table(mtcars, by = am, overall = TRUE) |>
  add_summary(vars = c("mpg", "wt", "cyl"))

summary_table(mtcars) |>
  add_summary(vars = c(mpg, wt), statistic = "mean_sd")

missing_example <- mtcars
missing_example$vs[1:3] <- NA
summary_table(missing_example) |>
  add_summary(vars = vs, missing = "as_category")

Add total counts to a descriptive table

Description

Add a total row to a gtstats descriptive table showing the number of observations overall or within each displayed group.

Usage

add_total(x, label = "Total (N)", position = c("last", "first"))

Arguments

Details

This helper is useful in Table 1 workflows where a final row is needed to show the number of observations contributing to each column. When the table includes an Overall column, the total number of rows in the source data is shown there. When the table is grouped, totals are calculated within each displayed group. Publication-table headers already display these cohort denominators automatically, so this row is optional.

This helper can be used only with descriptive tables created in mode = "summary".

Value

An updated gtstats_summary object with a total row appended.

Examples

summary_table(mtcars, by = am, include = c(mpg, wt, cyl)) |>
  add_total()

summary_table(mtcars, by = am, include = c(mpg, wt), overall = TRUE) |>
  add_total()

summary_table(mtcars) |>
  add_total()

Convert an already summarised data frame into a publication table

Description

Wrap a data frame or tibble containing values that have already been calculated so it can be rendered, styled, and exported with gtstats. No descriptive statistics, confidence intervals, or p-values are calculated or checked by this function.

Usage

as_stats_table(data, notes = NULL)

Arguments

Details

Use summary_table() instead when each row represents a participant or observation and descriptive statistics still need to be calculated. Use epi_table() when events and denominators need to be calculated from a line list or aggregate outbreak/surveillance data.

Value

A gt_data_table object. Use customise_table(), to_flextable(), to_gt(), or save_output() to present or export it.

Examples

summarised <- mtcars |>
  dplyr::summarise(
    Cars = dplyr::n(),
    `Mean mpg` = mean(mpg),
    `Mean weight` = mean(wt)
  )

table <- as_stats_table(
  summarised,
  notes = "Values were calculated before table formatting."
)
customise_table(table, title = "Vehicle summary")
to_flextable(table)
to_gt(table)

Assess the empirical distribution of continuous variables

Description

Assess the empirical distribution of continuous numeric variables to support descriptive reporting. The function describes missingness, finite sample size, skewness, and (optionally) Shapiro-Wilk results. It provides guidance about presenting a variable; it does not select an inferential test.

Usage

assess_distribution(
  data,
  vars = NULL,
  by = NULL,
  normality_test = TRUE,
  skew_cutoff = 1,
  min_n = 3,
  plots = FALSE,
  digits = 2,
  format = c("table", "tibble")
)

Arguments

Details

When by is supplied, diagnostics are calculated within every group and one consistent, variable-level recommendation is also returned in ⁠$recommendations⁠. Shapiro-Wilk is supporting information only: it is sensitive to sample size and never determines the recommendation by itself.

Value

With format = "table", a gt_distribution object that prints as a publication-ready table. ⁠$summary⁠ contains group-level diagnostics and ⁠$recommendations⁠ contains one descriptive recommendation per variable. With format = "tibble", the group-level summary tibble is returned.

Examples

assess_distribution(mtcars, vars = c(mpg, wt))
assess_distribution(mtcars, vars = c(mpg, wt), by = am)
assess_distribution(mtcars, vars = "mpg", normality_test = FALSE)
assess_distribution(mtcars, vars = c("mpg", "wt"), plots = TRUE)$plots

Assess variation of continuous variables across groups

Description

Describe the spread of continuous numeric variables within groups. This is a diagnostic companion to assess_distribution() and is intended to make variation visible before a group comparison is interpreted.

Usage

assess_variance(
  data,
  vars = NULL,
  by,
  digits = 2,
  test = c("levene", "none", "bartlett"),
  format = c("table", "tibble")
)

Arguments

Details

assess_variance() reports group sample sizes, standard deviations, variances, and the ratio of the largest to the smallest group SD and variance. These ratios are descriptive diagnostics, not pass/fail tests. The function deliberately does not run a variance hypothesis test by default, and it does not choose an inferential test. Welch t-tests and Welch ANOVA do not require equal variances for independent groups; this function does not assess pairing, repeated-measures sphericity, or select a repeated-measures method. The default is the median-centred Levene test (often called the Brown-Forsythe modification) as supporting information. It is less sensitive to non-normality than Bartlett's test. Set test = "none" for descriptive spread only, or test = "bartlett" when the normal-distribution assumption is justified. Neither test is used to select a test in compare_groups().

Value

With format = "table", a gt_variance object that prints as one readable row per variable: each group's n and SD, the observed SD ratio, the requested test p-value, and a plain-language interpretation. ⁠$summary⁠ contains the full group-level values and ⁠$diagnostics⁠ retains technical test metadata. With format = "tibble", the detailed summary tibble is returned.

Examples

assess_variance(mtcars, vars = c(mpg, wt), by = am)
assess_variance(mtcars, vars = "mpg", by = am, digits = 1)
assess_variance(mtcars, vars = "mpg", by = am, test = "bartlett")
assess_variance(mtcars, vars = "mpg", by = am, test = "levene")

Inspect statistical assumptions

Description

Return a plain-language checklist of items to confirm before reporting a result. Use view = "audit" to retrieve the underlying technical status and result codes retained by the analysis object.

Usage

assumptions_stats(
  x,
  format = c("table", "tibble"),
  title = "Checks before reporting",
  subtitle = NULL,
  view = c("checklist", "audit")
)

Arguments

Value

A tibble or gt_tbl.

Examples

result <- compare_groups(mtcars, variable = mpg, group = am)
assumptions_stats(result)
assumptions_stats(result, format = "tibble")

Low birth weight data

Description

A labelled and analysis-ready version of the low birth weight study data. It contains 189 observations and is derived from the dataset distributed in MASS, originally reported by Hosmer and Lemeshow. Numeric clinical codes have been converted to readable factors; antenatal_visits is an ordered factor and the original numeric variables are retained.

Usage

birthwt

Format

A data frame with 189 rows and 12 variables:

low

Birth-weight outcome: Normal birth weight or Low birth weight.

age

Maternal age in years.

lwt

Maternal weight in pounds.

race

Maternal race.

smoke

Smoking during pregnancy.

ptl

Number of previous premature labours.

ht

History of hypertension.

ui

Uterine irritability.

ftv

Number of first-trimester physician visits.

bwt

Birth weight in grams.

previous_preterm

Any previous premature labour.

antenatal_visits

Ordered visit category.

Source

Hosmer, D. W. and Lemeshow, S. (1989). Applied Logistic Regression. Derived from MASS::birthwt.

Examples

describe_data(birthwt)
summary_table(birthwt, by = low, include = c(age, lwt, smoke), overall = TRUE)

Compare groups using common inferential tests

Description

Compare a variable across groups using a practical set of common inferential tests.

Usage

compare_groups(
  data,
  variable,
  group,
  paired = FALSE,
  id = NULL,
  test = c("auto", "t_test", "welch_t", "wilcox", "anova", "welch_anova", "kruskal",
    "chisq", "fisher", "mcnemar", "rm_anova", "friedman", "cochran_q"),
  effect_size = FALSE,
  conf.level = 0.95,
  digits = 2,
  var_equal = FALSE,
  fisher_seed = 1049L,
  format = c("table", "tibble")
)

Arguments

Details

This function is designed for beginner-friendly and teaching-focused workflows. It combines:

• descriptive summaries by group

• automatic or user-specified test selection

• effect size calculation where supported

• a simple display-ready results table

Supported outcome types are:

• continuous

• binary

• categorical

• ordinal

Supported tests include:

• "auto"

• "t_test"

• "welch_t"

• "wilcox"

• "anova"

• "welch_anova"

• "kruskal"

• "chisq"

• "fisher"

• "mcnemar"

• "rm_anova"

• "friedman"

• "cochran_q"

Automatic selection policy

test = "auto" uses the following fixed, data-driven rules. These rules are intended as transparent defaults, not a substitute for a prespecified analysis plan.

• Continuous outcome, two independent groups: Welch t-test by default, or Student's t-test when var_equal = TRUE, unless marked skewness is flagged in either group, then Wilcoxon rank-sum test.

• Continuous outcome, three or more independent groups: Welch ANOVA by default, or classical one-way ANOVA when var_equal = TRUE, unless marked skewness is flagged in any group, then Kruskal-Wallis test.

• Paired continuous outcome: for two occasions, paired t-test unless marked skewness in within-pair differences is flagged, then Wilcoxon signed-rank; for three or more occasions, repeated-measures ANOVA unless marked skewness is flagged, then Friedman test. Repeated-measures ANOVA reports a conservative Greenhouse-Geisser-corrected p-value.

• Independent ordinal, binary, or nominal categorical outcome: Pearson chi-square test when no expected count is below 1 and no more than 20% are below 5; Fisher's exact test otherwise (Monte Carlo p-value for larger tables). This compares the distribution of all levels, which is the usual Table 1 question. Use an explicit rank test ("wilcox" or "kruskal") when the ordered scale itself is the intended estimand.

• Paired ordinal outcome: Wilcoxon signed-rank for two occasions; Friedman for three or more paired occasions.

• Paired binary outcome: McNemar test for two occasions; Cochran's Q test for three or more occasions.

Distribution guidance uses the package's skewness assessment within each group (or within-pair differences). Shapiro-Wilk is supporting information; it does not by itself change the selected test. Automatic decisions, the values used, and the selected method are retained in ⁠$method⁠, ⁠$diagnostics⁠, and ⁠$notes⁠.

For independent continuous comparisons, ⁠$diagnostics⁠ also reports the observed standard deviation and variance ratios across groups. These are descriptive context only: they have no pass/fail threshold and do not alter automatic test selection. var_equal is a user-specified analytical assumption, not a variance hypothesis test: gtstats never infers it using Levene, Bartlett, or F tests. Welch t-tests and Welch ANOVA are the conservative defaults because they do not require equal variances.

When effect_size = TRUE, the function selects an effect size from the comparison structure:

• Hedges' g for two-group parametric comparisons

• rank-biserial correlation for two-group rank comparisons

• omega-squared for ANOVA or Welch ANOVA

• epsilon-squared for Kruskal-Wallis comparisons

• Cramer's V for categorical contingency tables

Hedges' g is accompanied by a large-sample confidence interval. Other effect-size intervals are omitted unless a supported interval method is available. Conventional magnitude labels are retained in inferential for teaching but are not displayed as clinical importance thresholds.

Value

A gt_compare object containing:

• inputs — function inputs and settings

• descriptives — descriptive summaries by group

• inferential — inferential test results

• table — display-ready results table

• method — metadata on detected variable types

• notes — explanatory notes

• call — matched function call

For paired or repeated analyses, only complete, uniquely matched identifiers are analysed. The number retained and excluded is available in ⁠$denominators⁠ and ⁠$notes⁠; rendered tables also identify the complete-pair denominator. Friedman and Cochran's Q require within-participant variation and fail clearly when it is absent.

Examples

compare_groups(mtcars, variable = mpg, group = am)

compare_groups(
  mtcars,
  variable = mpg,
  group = am,
  effect_size = TRUE
)

compare_groups(
  mtcars,
  variable = vs,
  group = am,
  test = "chisq",
  effect_size = TRUE
)

compare_groups(
  mtcars,
  variable = mpg,
  group = am,
  paired = FALSE,
  test = "welch_t"
)

compare_groups(mtcars, variable = mpg, group = am, var_equal = TRUE)

to_gt(compare_groups(mtcars, variable = mpg, group = am))

Correlation analysis for one pair or several continuous variables

Description

correlation() analyses either one prespecified pair (x and y) or a correlation matrix (vars). Matrix mode uses one method throughout, retains pairwise sample sizes and inferential results in ⁠$summary⁠, and prints a compact publication-ready matrix. Use plot_correlation() for a shaded heatmap of a matrix result.

Usage

correlation(
  data,
  x = NULL,
  y = NULL,
  method = c("auto", "pearson", "spearman"),
  conf.level = 0.95,
  digits = 2,
  vars = NULL,
  triangle = c("lower", "upper", "full"),
  order = c("input", "alphabetical", "cluster"),
  show_diagonal = TRUE,
  display = c("estimate", "estimate_p", "estimate_n", "estimate_p_n", "estimate_ci"),
  shade = TRUE,
  missing = c("pairwise"),
  adjust = c("none", "holm", "bonferroni", "BH"),
  format = c("table", "tibble")
)

Arguments

Details

In automatic matrix mode, Pearson correlation is used only when every selected variable has absolute sample skewness below 1; otherwise Spearman correlation is used throughout. This is transparent descriptive guidance, not proof of linearity or monotonicity. Inspect the matrix heatmap and relevant pairwise plots before interpretation.

Value

A gt_correlation object. Matrix results additionally inherit from gt_correlation_matrix and contain a tidy pair-level ⁠$summary⁠.

Examples

correlation(mtcars, x = mpg, y = wt)
correlation(mtcars, vars = c(mpg, disp, hp, wt))
plot_correlation(correlation(mtcars, vars = c(mpg, disp, hp, wt)))

Cross-tabulations with optional 2x2 epidemiological measures

Description

Create a publication-ready cross-tabulation for any two categorical variables. Counts, selected row/column/total percentages, and margins are displayed. For a binary 2x2 table, risks, risk ratios, odds ratios, and risk differences are additionally available because their direction is defined. For a 2x2 table, the selected exposed and event levels are always retained in the result, including when they were chosen automatically. Association diagnostics, zero-cell handling, and complete-pair denominators remain in the audit components rather than cluttering the displayed table.

Usage

crosstabs(
  data,
  row,
  col,
  percent = "column",
  totals = TRUE,
  row_level = NULL,
  col_level = NULL,
  measures = c("rr", "or", "rd"),
  conf.level = 0.95,
  risk_ci = c("wilson", "exact"),
  test = c("auto", "none", "chisq", "fisher"),
  zero_correction = c("haldane_anscombe", "none"),
  simulate_B = 10000,
  digits = 2,
  format = c("table", "tibble")
)

Arguments

Value

A gt_twobytwo object.

Examples

crosstabs(mtcars, row = am, col = vs)

Customize a gtstats table

Description

Apply titles, labels, alignment, emphasis, colours, and a predefined visual theme to a table produced by gtstats.

Usage

customise_table(
  x,
  engine = c("flextable", "gt"),
  theme = c("default", "journal", "classic", "minimal", "compact"),
  title = NULL,
  subtitle = NULL,
  source_note = NULL,
  col_labels = NULL,
  row_labels = NULL,
  level_labels = NULL,
  align = NULL,
  hide_cols = NULL,
  bold_cols = NULL,
  italic_cols = NULL,
  font_size = NULL,
  font = NULL,
  width = NULL,
  row_striping = NULL,
  accent_color = NULL,
  stripe_color = NULL,
  bold_labels = TRUE,
  show_footnotes = TRUE,
  spanning_header = NULL,
  footnotes = NULL,
  borders = c("horizontal", "all", "minimal"),
  density = c("standard", "compact", "spacious"),
  column_widths = NULL,
  pvalue_style = c("threshold", "fixed", "scientific"),
  pvalue_digits = 3,
  pvalue_threshold = 0.001,
  pvalue_prefix = FALSE
)

Arguments

Value

A styled flextable by default, or a gt_tbl when engine = "gt" or x is already a gt table.

Examples

result <- summary_table(mtcars, include = c(mpg, wt))
customise_table(result, title = "Vehicle characteristics")

Inspect statistical denominators

Description

Return a transparent audit of the observations, missing values, numerators, denominators, and denominator rules used in a gtstats result.

Usage

denominators_stats(
  x,
  format = c("table", "tibble"),
  title = "Denominator audit",
  subtitle = NULL,
  view = c("readable", "audit")
)

Arguments

Value

A tibble or gt_tbl.

Examples

result <- proportion_stats(mtcars, var = vs, by = am)
denominators_stats(result)

Understand a dataset before analysis

Description

Create a concise, clinically oriented first look at a dataset. One row is returned per variable, combining its label, detected type, completeness, cardinality, a type-specific overview, and range or levels. Potential data-quality findings are kept separately in ⁠$issues⁠.

Usage

describe_data(data, vars = NULL, digits = 2, format = c("table", "tibble"))

Arguments

Details

describe_data() deliberately does not assess distributional assumptions or recommend inferential tests. Use assess_distribution() for the shape of selected continuous variables, summary_table() for detailed descriptive statistics, and compare_groups() for inferential comparisons.

Value

With format = "table", a gt_describe object that prints as a publication-ready table. ⁠$summary⁠ is the concise variable overview and ⁠$issues⁠ contains only findings requiring review. With format = "tibble", the concise summary tibble is returned directly.

Examples

describe_data(mtcars)
describe_data(mtcars, vars = c("mpg", "cyl", "am"))
to_gt(describe_data(mtcars))

Inspect statistical diagnostics

Description

Return diagnostic checks, observed values, thresholds, and interpretations retained by a gtstats result.

Usage

diagnostics_stats(
  x,
  format = c("table", "tibble"),
  title = "Diagnostics",
  subtitle = NULL,
  view = c("readable", "audit")
)

Arguments

Value

A tibble or gt_tbl.

Examples

result <- compare_groups(mtcars, variable = vs, group = am)
diagnostics_stats(result)

Estimate an effect size

Description

Quantify the magnitude of a group difference or association without adding the full hypothesis-test output produced by compare_groups(). For directional two-group measures, the Contrast column names the grouping variable and reports first group minus second group. Factor order is therefore meaningful. Cramer's V and omnibus measures have no direction.

Usage

effect_size(
  data,
  variable,
  group,
  method = c("auto", "hedges_g", "rank_biserial", "omega_squared", "epsilon_squared",
    "cramers_v"),
  paired = FALSE,
  id = NULL,
  conf.level = 0.95,
  interpretation = FALSE,
  digits = 2,
  format = c("table", "tibble")
)

Arguments

Details

The default method = "auto" selects one measure from the outcome and comparison structure:

• Hedges' g for two-group parametric comparisons

• rank-biserial correlation for two-group rank comparisons

• omega-squared for comparisons involving more than two continuous groups

• epsilon-squared when a multi-group rank method is requested

• Cramer's V for categorical associations

Risk ratios, odds ratios, and risk differences are intentionally not duplicated here; use crosstabs() for those epidemiological measures.

Value

A publication-ready gt_effect object containing summary, table, inputs, method, assumptions, diagnostics, denominators, and notes.

Examples

effect_size(mtcars, variable = mpg, group = am)

effect_size(
  mtcars,
  variable = mpg,
  group = am,
  method = "hedges_g",
  interpretation = TRUE
)

Outbreak and surveillance summary table

Description

Build a publication-ready epidemiology table from either individual line-list records or aggregate numerator/denominator data. Unlike summary_table(), this function always makes the event and denominator explicit and always reports a confidence interval.

Usage

epi_table(
  data,
  outcomes = NULL,
  by = NULL,
  event = NULL,
  numerator = NULL,
  denominator = NULL,
  label = NULL,
  person_time = NULL,
  measure = c("proportion", "prevalence", "attack_rate", "incidence_rate"),
  multiplier = NULL,
  ci_method = c("wilson", "exact"),
  conf.level = 0.95,
  p_value = FALSE,
  p_adjust = c("none", "holm", "bonferroni", "BH", "fdr"),
  effects = "none",
  layout = c("auto", "wide", "long"),
  digits = 1,
  format = c("table", "tibble")
)

Arguments

Value

A gt_epi_table object containing ⁠$summary⁠, ⁠$table⁠, ⁠$denominators⁠, ⁠$p_values⁠, ⁠$effects⁠, ⁠$inputs⁠, and ⁠$notes⁠.

Examples

epi_table(
  birthwt, outcomes = low, by = smoke,
  event = "Low birth weight", measure = "prevalence"
)

aggregate_data <- data.frame(
  ward = c("A", "B"), cases = c(12, 7), population = c(80, 65)
)
epi_table(
  aggregate_data, numerator = cases, denominator = population,
  by = ward, label = "Influenza", measure = "attack_rate"
)

Launch the gtstats graphical interface

Description

Open a guided Shiny interface for the most common gtstats workflows: inspecting and preparing a dataset, assessing continuous-variable distributions and spread, building a summary table, creating outbreak or surveillance tables, comparing groups, producing correlation tables, and producing a cross-tabulation. Data can be selected from the current R environment, loaded from a teaching dataset, or uploaded. Results, plots, and generated R code can be downloaded. The table customiser can also wrap an already calculated results data frame with as_stats_table() without recalculating its values. Excel uploads are available when the suggested rio package is installed.

Usage

gtstats_app(..., launch.browser = NULL)

Arguments

Details

The app is a companion to the package's code-first workflow. It creates reproducible R code for every analysis, so users can begin in the interface and continue in an R script when they are ready.

Shiny is a suggested dependency and is loaded only when gtstats_app() is called. It is therefore not required for ordinary use of gtstats. While the app is open, R runs the local Shiny session and the console shows a ⁠Listening on ...⁠ message. This is expected. Click Close app in the bottom-right corner to stop the session cleanly and return to the R prompt.

Value

Invisibly returns the result of shiny::runApp().

Examples

if (interactive()) {
  gtstats_app()
}

Oswego foodborne-outbreak line list

Description

A beginner-friendly copy of the 1940 Oswego County church-supper outbreak line list supplied by the US Centers for Disease Control and Prevention (CDC) with Epi Info. The 75 interview records are unchanged. Variable names use snake case, Y/N values are labelled Yes/No, and variable labels have been added.

Usage

outbreak_data

Format

A data frame with 75 rows and 21 variables. It contains participant age and sex, illness status and onset information, meal time, and indicators for foods and drinks consumed. ill is the outcome; food variables such as vanilla_ice_cream can be used as exposures.

Source

CDC Epi Info teaching data, documented in the Epi Info 6 User's Guide. The source material is a US Government work available without charge. CDC does not endorse gtstats. See the CDC agency materials policy.

Examples

epi_table(
  outbreak_data,
  outcomes = ill,
  by = vanilla_ice_cream,
  event = "Yes",
  measure = "attack_rate",
  p_value = TRUE,
  effects = "all"
)

Paired follow-up teaching data

Description

Simulated long-format data for paired analyses. Each participant has a Baseline and Follow-up record. pain_score supports a paired t-test example, days_off_work is skewed for a signed-rank example, and symptom_present supports McNemar's test.

Usage

paired_data

Format

A data frame with 180 rows (90 complete pairs) and 5 variables: id, visit, pain_score, days_off_work, and symptom_present.

Examples

compare_groups(
  paired_data, variable = pain_score, group = visit,
  paired = TRUE, id = id
)
compare_groups(
  paired_data, variable = symptom_present, group = visit,
  paired = TRUE, id = id
)

Plot a group comparison

Description

Create a publication-ready comparison plot using a sensible visual selected from the outcome type and study design.

Usage

plot_compare(
  data,
  variable,
  group,
  paired = FALSE,
  id = NULL,
  type = c("auto", "box", "bar"),
  display = c("proportion", "count"),
  show_points = TRUE,
  show_p = FALSE,
  test = c("auto", "t_test", "welch_t", "wilcox", "anova", "welch_anova", "kruskal",
    "chisq", "fisher", "mcnemar", "rm_anova", "friedman", "cochran_q"),
  var_equal = FALSE,
  palette = NULL,
  base_size = 14,
  title = NULL,
  caption = NULL,
  xlab = NULL,
  ylab = NULL,
  legend_title = NULL
)

Arguments

Details

The minimal call is plot_compare(data, variable, group). Continuous outcomes are shown as boxplots with individual observations, categorical outcomes as stacked within-group proportions, and paired continuous outcomes as participant-level connected observations. The returned object is a standard ggplot and can be customized with ordinary ggplot2 layers.

When show_p = TRUE, the annotation is obtained from compare_groups() with the same test, var_equal, paired, and id settings. The test name is always shown with the p-value. The annotation and plotted denominators use the same complete observations; non-finite continuous values are excluded.

Value

A ggplot object.

Examples

plot_compare(mtcars, variable = mpg, group = am)

plot_compare(
  mtcars,
  variable = vs,
  group = am,
  display = "proportion",
  show_p = TRUE
)

Plot the relationship between two continuous variables

Description

Create a publication-ready scatterplot that is aligned with correlation(). The minimal call is plot_correlation(data, x, y). Incomplete pairs are excluded and the analysed number of complete pairs is shown in the caption.

Usage

plot_correlation(
  data,
  x = NULL,
  y = NULL,
  method = c("auto", "pearson", "spearman"),
  trend = c("auto", "linear", "smooth", "none"),
  show_ci = TRUE,
  show_correlation = FALSE,
  conf.level = 0.95,
  digits = 2,
  point_color = "#4472C4",
  line_color = "#ED7D31",
  base_size = 14,
  title = NULL,
  caption = NULL,
  xlab = NULL,
  ylab = NULL,
  triangle = NULL,
  show_diagonal = NULL,
  show_values = TRUE,
  low_color = "#355C7D",
  mid_color = "#FFFFFF",
  high_color = "#C06C5B"
)

Arguments

Details

With trend = "auto", a linear trend is used for Pearson correlation and a smooth trend for Spearman correlation. Set trend = "none" to display the observations alone. When show_correlation = TRUE, the caption reports the same method, coefficient, confidence interval when available, and p-value as correlation(). The returned object is a standard ggplot, so ordinary ggplot2 layers can be added.

Value

A ggplot object.

Examples

plot_correlation(mtcars, x = mpg, y = wt)

plot_correlation(
  mtcars,
  x = mpg,
  y = wt,
  show_correlation = TRUE
)

matrix_result <- correlation(mtcars, vars = c(mpg, disp, hp, wt))
plot_correlation(matrix_result)

Print a gtstats compare object

Description

Print a publication-ready comparison table.

Usage

## S3 method for class 'gt_compare'
print(x, ...)

Arguments

Details

The print method renders the concise publication table. Detailed numerical results and audit information remain available in the object components.

Value

The input object, invisibly.

Examples

x <- compare_groups(mtcars, variable = mpg, group = am)
print(x)

Print a gtstats correlation object

Description

Print a publication-ready correlation table.

Usage

## S3 method for class 'gt_correlation'
print(x, ...)

Arguments

Details

Detailed numerical results and audit information remain available in the object components.

Value

The input object, invisibly.

Examples

x <- correlation(mtcars, x = mpg, y = wt)
print(x)

Print an already summarised gtstats table

Description

Print an already summarised gtstats table

Usage

## S3 method for class 'gt_data_table'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print a gtstats describe object

Description

Print the publication-ready table stored by a gt_describe object.

Usage

## S3 method for class 'gt_describe'
print(x, ...)

Arguments

Details

The underlying concise tibble remains available in ⁠$summary⁠, and focused data-quality findings are available in ⁠$issues⁠.

Value

The input object, invisibly.

Examples

x <- describe_data(mtcars)
print(x)

Print a gtstats distribution object

Description

Print publication-ready distribution diagnostics and recommendations.

Usage

## S3 method for class 'gt_distribution'
print(x, ...)

Arguments

Details

The detailed diagnostic table includes the suggested presentation beside the numerical diagnostics. With groups, the common variable-level suggestion is shown once beside the first group row. Machine-readable results remain available in ⁠$summary⁠ and ⁠$recommendations⁠.

Value

The input object, invisibly.

Examples

x <- assess_distribution(mtcars, vars = c("mpg", "wt"))
print(x)

Print a gtstats effect-size object

Description

Print the publication-ready table stored by a gt_effect object.

Usage

## S3 method for class 'gt_effect'
print(x, ...)

Arguments

Value

The input object, invisibly.

Examples

x <- effect_size(mtcars, variable = mpg, group = am)
print(x)

Print an outbreak and surveillance table

Description

Print an outbreak and surveillance table

Usage

## S3 method for class 'gt_epi_table'
print(x, ...)

Arguments

Value

The input object, invisibly.

Print a gtstats proportion object

Description

Print a compact console preview of a gt_prop object.

Usage

## S3 method for class 'gt_prop'
print(x, ...)

Arguments

Details

The print method shows the dataset name, variable, selected level, optional grouping variable, and the display-ready proportion table.

Value

The input object, invisibly.

Examples

x <- proportion_stats(mtcars, var = vs, by = am)
print(x)

Print a gtstats rate object

Description

Print a compact console preview of a gt_rate object.

Usage

## S3 method for class 'gt_rate'
print(x, ...)

Arguments

Details

The print method shows the dataset name, event variable, person-time variable, optional grouping variable, and the display-ready rate table.

Value

The input object, invisibly.

Examples

df <- data.frame(
  event = c(1, 0, 1, 0, 1, 1),
  ptime = c(10, 12, 8, 9, 11, 7),
  arm = c("A", "A", "A", "B", "B", "B")
)
x <- rate_stats(df, event = event, time = ptime, by = arm)
print(x)

Print a gtstats 2x2 table object

Description

Print a compact console preview of a gt_twobytwo object.

Usage

## S3 method for class 'gt_twobytwo'
print(x, ...)

Arguments

Details

The print method shows the dataset name, row/reference definition, column/event definition, and the display-ready 2x2 epidemiology table.

Value

The input object, invisibly.

Examples

x <- crosstabs(mtcars, row = am, col = vs)
print(x)

Print a gtstats variance object

Description

Print publication-ready variance diagnostics. Group-level sample sizes, standard deviations, variances, and observed spread ratios are displayed; the underlying values and explanatory metadata remain available in ⁠$summary⁠ and ⁠$diagnostics⁠.

Usage

## S3 method for class 'gt_variance'
print(x, ...)

Arguments

Value

The input object, invisibly.

Examples

x <- assess_variance(mtcars, vars = c(mpg, wt), by = am)
print(x)

Print a descriptive table

Description

Print a completed gtstats_summary as a publication-ready flextable. An empty builder instead prints a short instruction explaining how to add rows.

Usage

## S3 method for class 'gtstats_summary'
print(x, ...)

Arguments

Details

Use format = "tibble" in summary_table() for a plain console table, or call to_gt() when an HTML-oriented gt table is required.

Value

The input object, invisibly.

Examples

x <- summary_table(mtcars, by = am, overall = TRUE)
print(x)

Proportion statistics

Description

Calculate a proportion and confidence interval, optionally within groups. This is the descriptive-statistics counterpart to rate_stats().

Usage

proportion_stats(
  data,
  var,
  by = NULL,
  level = NULL,
  conf.level = 0.95,
  ci_method = c("wilson", "exact"),
  display = c("n_percent", "percent", "n_over_N_percent"),
  digits = 1,
  format = c("table", "tibble")
)

Arguments

Value

A gt_prop object containing numeric results and a display table.

Examples

proportion_stats(mtcars, var = vs)
proportion_stats(mtcars, var = vs, by = am)

Incidence rate with exact Poisson confidence interval

Description

Compute an incidence or event rate with exact Poisson confidence intervals, either overall or within groups defined by a categorical variable.

Usage

rate_stats(
  data,
  event,
  time,
  by = NULL,
  multiplier = 1000,
  time_label = NULL,
  conf.level = 0.95,
  digits = 1,
  format = c("table", "tibble")
)

Arguments

Details

This function is designed for simple epidemiological summaries where events are counted over a denominator of person-time. The event variable may be a binary indicator, a logical variable, or a count variable. The time variable must be numeric, finite, and non-negative. A zero accumulated person-time denominator is retained and clearly marked as not estimable rather than silently converted to a rate.

When a grouping variable is supplied, rates are calculated separately within each group. Confidence intervals are calculated using stats::poisson.test(). The publication table uses each group as a spanning header, with separate columns for events, accumulated time, rate, and confidence interval. The tidy long-form numerical results remain available in ⁠$summary⁠.

Value

A gt_rate object containing:

• inputs — function inputs and settings

• summary — detailed summary table

• table — display-ready table

• notes — explanatory note

• call — matched function call

Examples

df <- data.frame(
  event = c(1, 0, 1, 0, 1, 1),
  ptime = c(10, 12, 8, 9, 11, 7),
  arm = c("A", "A", "A", "B", "B", "B")
)

rate_stats(df, event = event, time = ptime)

rate_stats(df, event = event, time = ptime, by = arm)

to_gt(rate_stats(df, event = event, time = ptime, by = arm))

Save a gtstats table or plot

Description

Save a gtstats result, rendered flextable, rendered gt_tbl, or ggplot2 plot. A named list of gtstats results, flextables, and plots can be combined into one Word document. The object determines the export route automatically. The file type is inferred from filename.

Usage

save_output(
  x,
  filename,
  path = NULL,
  title = NULL,
  subtitle = NULL,
  bold_labels = TRUE,
  show_footnotes = TRUE,
  zoom = 2,
  expand = 5,
  vwidth = 992,
  vheight = 744,
  width = 8,
  height = 6,
  units = "in",
  dpi = 300,
  bg = "white",
  page_break = TRUE,
  quiet = FALSE,
  ...
)

Arguments

Value

The normalized saved path, invisibly.

Examples

table <- summary_table(mtcars) |> add_summary(vars = c(mpg, wt))
save_output(table, "summary.html", path = tempdir())

plot <- plot_compare(mtcars, variable = mpg, group = am)
save_output(plot, "comparison.png", path = tempdir())

save_output(
  list("Table 1" = table, "Comparison plot" = plot),
  "statistical-report.docx",
  path = tempdir()
)

Create a summary table builder

Description

Create the descriptive foundation of a publication-ready table. Add further layers only when needed: add_ci() for confidence intervals, add_p() for statistical comparisons, and specialist helpers such as add_proportion(), add_rate(), add_total(), and add_row().

Usage

summary_table(
  data,
  by = NULL,
  include = NULL,
  overall = FALSE,
  statistic = "recommended",
  categorical = c("n_percent", "n_over_N_percent", "n", "percent"),
  categorical_layout = c("combined", "separate"),
  show_dichotomous = c("all_levels", "single_row"),
  value = NULL,
  percent = c("column", "row", "overall"),
  overall_categorical = c("auto", "n_percent", "n_over_N_percent", "n", "percent"),
  digits = 1,
  missing = c("ifany", "always", "no", "as_category"),
  layout = c("compact", "separate"),
  label = NULL,
  conf.level = 0.95,
  format = c("table", "tibble")
)

Arguments

Details

For the usual Table 1 workflow, select all variables together with include. Continuous, binary, categorical, and ordinal variables are detected automatically and added using beginner-friendly defaults. There is no need to add continuous and categorical variables separately.

When include = NULL, an empty builder is returned for specialist row-only workflows. Printing a completed object automatically displays a publication-ready flextable; call to_gt() when an HTML-oriented gt table is required.

A grouping variable may be supplied to create one column per group. An optional overall column can also be requested for later use.

Value

A gtstats_summary object containing the source data, structural settings, and placeholders for table components.

Examples

summary_table(
  mtcars,
  by = am,
  include = c(mpg, wt, cyl, vs),
  overall = TRUE
)

summary_table(
  mtcars,
  by = am,
  include = c(mpg, wt, cyl, vs),
  overall = TRUE
) |>
  add_p()

# Percentages without decimals and Overall displayed last
summary_table(
  mtcars,
  by = am,
  include = c(mpg, wt, cyl, vs),
  overall = "last",
  digits = c(continuous = 1, percent = 0)
)

# Add confidence intervals as a visible layer
summary_table(
  mtcars,
  include = c(cyl, vs),
  categorical = "percent",
  layout = "separate"
) |>
  add_ci()

# Compact binary rows, with an explicit event where required
summary_table(
  mtcars,
  include = c(mpg, vs, am),
  show_dichotomous = "single_row",
  value = c(vs = "1", am = "1")
)

# Include Missing in a categorical percentage denominator
missing_example <- mtcars
missing_example$vs[1:3] <- NA
summary_table(
  missing_example,
  include = vs,
  missing = "as_category"
)

Archived weekly US hospital-admission surveillance data

Description

A teaching extract from CDC's archived weekly United States COVID-19 hospital-admission surveillance dataset. It contains one record per health service area for the report dated 12 January 2024. The CDC county file repeats the same area-level metrics for each constituent county; this copy retains one exact metric record per health service area to prevent double counting. Names and labels were simplified, dates were parsed, and the source spelling ⁠Insuficient Data⁠ was corrected in the factor label.

Usage

surveillance_data

Format

A data frame with 808 rows and 9 variables:

health_service_area_id

CDC health service area identifier.

population

Health service area population used as denominator.

report_date

CDC report date.

week_end_date

End date of the surveillance week.

mmwr_week

MMWR epidemiological week.

mmwr_year

MMWR year.

admissions

Weekly confirmed COVID-19 hospital admissions.

admissions_per_100k

CDC-published weekly rate per 100,000.

admission_level

CDC admission-level category.

Two areas have insufficient admission data and therefore missing counts and rates. The archived values are provisional teaching data and should not be used to describe current disease activity.

Source

CDC, Weekly United States COVID-19 Hospitalization Metrics by County - ARCHIVED, dataset akn2-qxic. The metadata identifies this dataset as USGOV_WORKS; it is available without charge. CDC does not endorse gtstats. See the CDC agency materials policy.

Examples

complete_surveillance <- subset(surveillance_data, !is.na(admissions))
epi_table(
  complete_surveillance,
  numerator = admissions,
  denominator = population,
  label = admission_level,
  measure = "incidence_rate",
  multiplier = 100000
)

Convert a gtstats object to flextable

Description

Convert a supported gtstats object into a flextable for use in Word, PowerPoint, and other Office-style outputs.

Usage

to_flextable(
  x,
  font_size = 10,
  font = NULL,
  autofit = TRUE,
  show_footnotes = TRUE,
  title = NULL,
  subtitle = NULL
)

Arguments

Details

This function is intended for export workflows where a gtstats object needs to be inserted into a report or presentation. The input should be the original analytical object, not the rendered gt table returned by to_gt().

Text columns are left-aligned and value columns are right-aligned. Notes, footnotes, and p-value method footnotes are appended to the footer when available.

Value

A flextable object.

Examples

res <- summary_table(mtcars, by = am, overall = TRUE) |>
  add_summary(vars = c(mpg, wt, cyl)) |>
  add_total() |>
  add_p()

to_flextable(res)

Convert a gtstats result to a gt table

Description

Render supported gtstats objects as formatted gt tables.

Usage

to_gt(
  x,
  title = NULL,
  subtitle = NULL,
  bold_labels = TRUE,
  show_footnotes = TRUE
)

Arguments

Details

This function is the explicit rendering bridge between analytical gtstats objects and presentation-ready table output. It supports descriptive, inferential, epidemiological, and table-builder objects created by the package and applies a consistent visual style using gt.

Supported inputs include:

• gt_describe

• gt_distribution

• gt_variance

• gt_compare

• gt_correlation

• gt_effect

• gt_prop

• gt_rate

• gt_twobytwo

• gtstats_summary

• gt_data_table

Value

A gt_tbl object.

Examples

to_gt(describe_data(mtcars))

to_gt(summary_table(mtcars, by = am, include = c(mpg, wt)))

to_gt(compare_groups(mtcars, variable = mpg, group = am))

to_gt(
  summary_table(
    mtcars,
    by = am,
    include = c(mpg, wt, cyl),
    overall = TRUE
  ) |>
    add_total() |>
    add_p()
)

Three-arm clinical trial teaching data

Description

Simulated data for demonstrating independent-group analyses. The dataset is intentionally constructed to include approximately symmetric continuous outcomes, a right-skewed outcome, an ordinal outcome, common and rare binary outcomes, correlated biomarkers, and person-time with event counts.

Usage

trial_data

Format

A data frame with 180 rows and 13 variables. arm has three groups; change_score is approximately symmetric; hospital_days is right-skewed; response is ordered; rare_event is sparse; and followup_years with infection_events supports rate examples.

Examples

compare_groups(trial_data, variable = change_score, group = arm)
compare_groups(trial_data, variable = hospital_days, group = arm)
compare_groups(trial_data, variable = rare_event, group = arm)