Continuous summary table

Computes descriptive statistics (mean, SD, min, max, confidence interval of the mean, n) for one or many continuous variables selected with tidyselect syntax.

With by, produces grouped summaries and reports a group-comparison p-value by default (Welch test; change via test). Additional inferential output is opt-in: test statistics (statistic) and effect sizes (effect_size / effect_size_ci). Set p_value = FALSE to suppress the p-value column. Without by, produces one-way descriptive summaries.

Multiple output formats are available via output: a printed ASCII table ("default"), a plain data.frame ("data.frame" or "long" – synonyms for the underlying long-format data, see Details), or publication-ready tables ("tinytable", "gt", "flextable", "excel", "clipboard", "word").

This is the descriptive companion to table_continuous_lm(). The two functions share their layout, alignment, and reporting precision so descriptive and model-based analyses of the same data look uniform side by side. Use table_continuous_lm() when you need robust SE, weighted contrasts, fitted means, or covariate adjustment.

Usage

table_continuous(
  data,
  select = tidyselect::everything(),
  by = NULL,
  exclude = NULL,
  regex = FALSE,
  drop_na = TRUE,
  test = c("welch", "student", "nonparametric"),
  p_value = NULL,
  statistic = FALSE,
  show_n = TRUE,
  effect_size = c("none", "auto", "hedges_g", "eta_sq", "r_rb", "epsilon_sq"),
  effect_size_ci = FALSE,
  ci = TRUE,
  labels = NULL,
  ci_level = 0.95,
  digits = 2,
  effect_size_digits = 2,
  p_digits = 3,
  decimal_mark = ".",
  align = c("decimal", "center", "right"),
  output = c("default", "data.frame", "long", "tinytable", "gt", "flextable", "excel",
    "clipboard", "word"),
  excel_path = NULL,
  excel_sheet = "Descriptives",
  clipboard_delim = "\t",
  word_path = NULL,
  verbose = FALSE,
  user_na = TRUE
)

Arguments

data

A data.frame.

select

Columns to include. If regex = FALSE, use tidyselect syntax or a character vector of column names (default: tidyselect::everything()). If regex = TRUE, provide a regular expression pattern (character string).

by

Optional grouping column. Accepts an unquoted column name or a single character column name. Coerced to factor for grouping; non-numeric grouping columns (factor, character, logical) are supported as-is. Factor levels keep their declared order; any other by (character, numeric, haven labelled) forms groups in order of first appearance in the data – the same convention as table_categorical(). For a haven labelled by, the group headers are the raw codes (value labels are not used for grouping headers – the family convention shared with table_categorical() and table_continuous_lm()); declared missing values follow user_na as usual.

exclude

Columns to exclude. Supports tidyselect syntax and character vectors of column names.

regex

Logical. If FALSE (the default), uses tidyselect helpers. If TRUE, the select argument is treated as a regular expression.

drop_na

Logical. Controls how missing values in the by column are handled – the same argument as table_categorical(), with one structural difference: a continuous summary has no "(Missing)" row for the summarized variable itself (a mean cannot include NA), so NAs in each summarized variable are always excluded from that variable's statistics and the exclusion is disclosed in a table note ("Missing values removed: ...") rather than silent. If TRUE (the default, preserving this function's historical behavior; table_categorical() defaults to FALSE), rows with NA in by are removed from the grouped summaries, with a warning and a dedicated note line ("Rows with missing ... removed"). If FALSE, rows with NA in by form a dedicated "(Missing)" group – the field convention for descriptive tables (gtsummary's "Unknown" row; see the Epidemiologist R Handbook, Descriptive tables) – while the group-comparison test and effect size are still computed on the observed groups only (show the missing, test the observed, matching table_categorical()). Ignored (with a warning) when by is not used.

test

Character. Statistical test to use when comparing groups. One of "welch" (default), "student", or "nonparametric".

"welch": Welch t-test (2 groups) or Welch one-way ANOVA (3+ groups). Does not assume equal variances.
"student": Student t-test (2 groups) or classic one-way ANOVA (3+ groups). Assumes equal variances.
"nonparametric": Wilcoxon rank-sum / Mann–Whitney U (2 groups) or Kruskal–Wallis H (3+ groups).

Used whenever by is supplied (since p_value defaults to TRUE in that case) or when statistic = TRUE / effect_size = TRUE. Ignored when by is not used, or when all three display toggles are turned off.

p_value

Logical or NULL. If TRUE and by is used, adds a p-value column from the test specified by test. When NULL (the default), the p-value is shown automatically whenever by is supplied, and hidden otherwise. Pass p_value = FALSE to suppress the column explicitly. Ignored when by is not used.

statistic

Logical. If TRUE and by is used, the test statistic is shown in an additional column (e.g., t(df) = ..., F(df1, df2) = ..., W = ..., or H(df) = ...). Both p_value and statistic are independent; either or both can be enabled. Defaults to FALSE. Ignored when by is not used.

show_n

Logical. If TRUE, includes an unweighted n column in the printed ASCII table and in every rendered output (tinytable, gt, flextable, word, excel, clipboard). Set to FALSE to drop the n column structurally from those outputs (no empty placeholder, no spanner). The n column is always present in the raw output = "data.frame" / "long" for downstream programmatic access. Defaults to TRUE.

effect_size

Effect-size measure to include in the rendered outputs. One of:

"none" (default): no effect-size column.
"auto": auto-select the canonical measure for the chosen test and group count – Hedges' g (parametric, 2 groups), eta-squared (parametric, 3+ groups), rank-biserial r (nonparametric, 2 groups), epsilon-squared (nonparametric, 3+ groups).
"hedges_g": Hedges' g (bias-corrected standardised mean difference, 2 groups, parametric). CI via the Hedges & Olkin normal approximation.
"eta_sq": Eta-squared (\(\eta^2\), parametric ANOVA-style SS_between / SS_total). CI via inversion of the noncentral F distribution.
"r_rb": Rank-biserial r from the Wilcoxon / Mann-Whitney statistic (2 groups, nonparametric). CI via Fisher z-transform.
"epsilon_sq": Epsilon-squared (\(\varepsilon^2\)) from the Kruskal-Wallis statistic (3+ groups, nonparametric). CI via percentile bootstrap (2 000 replicates).

For backward compatibility, effect_size = TRUE is silently coerced to "auto" and effect_size = FALSE to "none". Explicit choices are validated against the active test and the number of groups; an incompatible request (e.g. "eta_sq" with two groups, or "hedges_g" with test = "nonparametric") triggers an actionable error. Ignored when by is not used.

effect_size_ci

Logical. If TRUE, appends the confidence interval of the effect size in brackets (e.g., g = 0.45 [0.22, 0.68]). Implies a non-"none" effect size: if left at the default effect_size = "none", the function warns and promotes effect_size to "auto" so the requested CI can be shown. Defaults to FALSE.

ci

Logical. If TRUE, includes the mean confidence interval columns (<level>% CI LL / <level>% CI UL) and their spanner in the printed ASCII table and in every rendered output (tinytable, gt, flextable, word, excel, clipboard). Set to FALSE to drop both columns and the CI spanner structurally from those outputs (no empty placeholders, no border lines under an empty header). The CI bounds are always present as ci_lower / ci_upper in the raw output = "data.frame" / "long" for downstream programmatic access. Defaults to TRUE. The CI level is taken from ci_level.

labels

An optional named character vector of variable labels. Names must match column names in data. When NULL (the default), labels are auto-detected from variable attributes (e.g., haven labels); if none are found, the column name is used.

ci_level

Confidence level for the mean confidence interval (default: 0.95). Must be between 0 and 1 exclusive.

digits

Number of decimal places for descriptive values and test statistics (default: 2).

effect_size_digits

Number of decimal places for effect-size values in formatted displays (default: 2).

p_digits

Integer >= 1. Number of decimal places used to render p-values in the p column (default: 3, the APA Publication Manual standard). Both the displayed precision and the small-p threshold derive from this argument: p_digits = 3 prints .045 and <.001; p_digits = 4 prints .0451 and <.0001; p_digits = 2 prints .05 and <.01. Useful for genomics / GWAS contexts with very small p-values, or for journals using a coarser convention. Leading zeros are always stripped, following APA convention.

decimal_mark

Character used as decimal separator. Either "." (default) or ",".

align

Horizontal alignment of numeric columns in the printed ASCII table and in the tinytable, gt, flextable, word, and clipboard outputs. The first column (Variable) and Group (when present) are always left-aligned. One of:

"decimal" (default): align numeric columns on the decimal mark, the standard scientific-publication convention used by SPSS, SAS, and LaTeX siunitx. Numeric cells are pre-padded with figure-spaces (U+2007, digit-width) so every string in a column has the same width with the decimal mark at the same internal position; centring those uniform-width strings then stacks the decimal points vertically. The same pad-then-centre strategy is applied on every engine (gt, tinytable, flextable, word, clipboard, ASCII print) for a homogeneous rendering, matching table_regression() and table_continuous_lm().
"center": center-align all numeric columns.
"right": right-align all numeric columns.

The excel output uses the engine's default alignment in any case: cell-string padding does not align decimals under proportional fonts. Same default and semantics as table_continuous_lm().

output

Output format. One of:

"default": a printed ASCII table, returned invisibly.
"data.frame" / "long": a plain data.frame with one row per (variable x group) (or one row per variable when by is not used). The two names are synonyms; pick whichever reads better in your pipeline ("long" matches table_continuous_lm()'s naming).
"tinytable" (requires tinytable)
"gt" (requires gt)
"flextable" (requires flextable)
"excel" (requires openxlsx2)
"clipboard" (requires clipr)
"word" (requires flextable and officer)

excel_path

File path for output = "excel".

excel_sheet

Sheet name for output = "excel" (default: "Descriptives").

clipboard_delim

Delimiter for output = "clipboard" (default: "\t").

word_path

File path for output = "word".

verbose

Logical. If TRUE, prints messages about excluded non-numeric columns (default: FALSE).

user_na

Logical. If TRUE (the default), declared missing values never reach the numeric summaries: they are excluded like NA and disclosed in the table note (Declared missing values removed: ...); declared-missing by values form no group. If FALSE, the declared codes are summarized as ordinary numbers. See the "Declared missing values" section of freq().

Value

Depends on output:

"default": prints a styled ASCII table and returns the underlying data.frame invisibly (S3 class "spicy_continuous_table" / "spicy_table"). The object can be re-coerced via as.data.frame.spicy_continuous_table() or piped into broom::tidy() / broom::glance().
"data.frame" / "long": a plain data.frame with columns variable, label, group (when by is used), mean, sd, min, max, ci_lower, ci_upper, n. When by is used together with p_value = TRUE, statistic = TRUE, or effect_size != "none", additional columns are appended (populated on the first row of each variable block only):
- test_type – test identifier (e.g., "welch_t", "welch_anova", "student_t", "anova", "wilcoxon", "kruskal").
- statistic, df1, df2, p.value – test results.
- es_type – effect-size identifier ("hedges_g", "eta_sq", "r_rb", or "epsilon_sq"), when effect_size != "none".
- es_value, es_ci_lower, es_ci_upper – effect-size estimate and confidence interval bounds.
The two names "data.frame" and "long" are synonyms (the descriptive output is naturally already long). Pick whichever reads better in your code.
"tinytable": a tinytable object.
"gt": a gt_tbl object.
"flextable": a flextable object.
"excel" / "word": writes to disk and returns the file path invisibly.
"clipboard": copies the table and returns the display data.frame invisibly.

Tests

The omnibus test is computed only when by is supplied and at least two groups remain after dropping NAs, with every group contributing at least two observations. Choice of test family is driven by test (see the @param entry for the full dispatch and the underlying stats:: functions called).

For model-based contrasts (heteroskedasticity-consistent SE, cluster-robust SE, weighted contrasts, fitted means, covariate adjustment), use table_continuous_lm().

Effect sizes

See @param effect_size for the dispatch table (canonical measure for each (test, n_groups) combination) and the validation rules applied to explicit requests.

Confidence intervals (enabled with effect_size_ci = TRUE) use noncentral F inversion for \(\eta^2\), the Hedges-Olkin normal approximation for g, the Fisher z-transform for r, and percentile bootstrap (2,000 replicates) for \(\varepsilon^2\). The bootstrap bounds depend on the random number generator state: call set.seed() before the table for reproducible \(\varepsilon^2\) intervals (the other three CIs are closed-form and deterministic).

For Cohen's d, Hays' \(\omega^2\), and Cohen's f\(^2\) (derived from a fitted, possibly weighted lm()), use the model-based companion table_continuous_lm().

Display conventions

Decimal alignment, p-value formatting, and required suggested packages per output engine are documented under @param align, @param p_digits, and @param output respectively.

Non-numeric columns are silently dropped (set verbose = TRUE to see which columns were excluded). When a constant column is passed, its statistics are reported exactly: SD is 0.00 and the CI degenerates to [m, m]. "--" cells appear only when a statistic is undefined (fewer than two valid observations).

Declared missing values

Survey files imported with haven often carry declared missing values: codes such as 8 = Don't know or 9 = Refused that the source file marks as missing while keeping them distinct from a plain NA. Two kinds of declaration exist: na_values / na_range metadata on haven::labelled_spss() vectors, and tagged missing values created by haven::tagged_na() (the Stata .a, .b, ... convention).

spicy honors the declaration by default (user_na = TRUE): declared missing values are excluded from every statistic exactly like NA – valid percentages, means, chi-squared tests, association measures, row-wise summaries, and group definitions – but they are not erased from display. freq() lists each observed declared value as its own row of the Missing block, with its value label; cross_tab(), table_categorical(), and table_continuous() disclose the exclusion in the table note (Declared missing values removed: x (2).); varlist() and code_book() count them as missing in N_valid / NAs / N_distinct while still listing the declared codes in Values.

Every function involved offers the same escape hatch: set user_na = FALSE to ignore the declaration and treat the declared codes as valid values (the behavior of spicy before 0.13.0). Tagged missing values are genuine NAs either way; for them, user_na = FALSE only collapses the per-tag breakdown back into the regular NA count.

Examples

# --- Basic usage ---------------------------------------------------------

# Default: ASCII console table.
table_continuous(
  sochealth,
  select = c(bmi, wellbeing_score)
)
#> Descriptive statistics
#> 
#>  Variable                      │   M     SD     Min    Max    95% CI LL 
#> ───────────────────────────────┼────────────────────────────────────────
#>  Body mass index               │ 25.93   3.72  16.00   38.90    25.72   
#>  WHO-5 wellbeing index (0-100) │ 69.04  15.62  18.70  100.00    68.16   
#> 
#>  Variable                      │ 95% CI UL   n   
#> ───────────────────────────────┼─────────────────
#>  Body mass index               │   26.14    1188 
#>  WHO-5 wellbeing index (0-100) │   69.93    1200 
#> 
#> Missing values removed: bmi (12).

# Grouped by education (Welch p-value added by default).
table_continuous(
  sochealth,
  select = c(bmi, wellbeing_score),
  by = education
)
#> Descriptive statistics
#> 
#>  Variable                      │ Group              M     SD     Min    Max   
#> ───────────────────────────────┼──────────────────────────────────────────────
#>  Body mass index               │ Lower secondary  28.09   3.47  18.20   38.90 
#>                                │ Upper secondary  26.02   3.43  16.00   37.10 
#>                                │ Tertiary         24.39   3.52  16.00   33.00 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  WHO-5 wellbeing index (0-100) │ Lower secondary  57.22  15.44  18.70   97.90 
#>                                │ Upper secondary  68.97  13.62  26.70  100.00 
#>                                │ Tertiary         76.85  13.23  40.40  100.00 
#> 
#>  Variable                      │ Group            95% CI LL  95% CI UL   n  
#> ───────────────────────────────┼────────────────────────────────────────────
#>  Body mass index               │ Lower secondary    27.66      28.51    260 
#>                                │ Upper secondary    25.73      26.31    534 
#>                                │ Tertiary           24.04      24.74    394 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  WHO-5 wellbeing index (0-100) │ Lower secondary    55.33      59.10    261 
#>                                │ Upper secondary    67.82      70.12    539 
#>                                │ Tertiary           75.55      78.15    400 
#> 
#>  Variable                      │ Group              p   
#> ───────────────────────────────┼────────────────────────
#>  Body mass index               │ Lower secondary  <.001 
#>                                │ Upper secondary        
#>                                │ Tertiary               
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  WHO-5 wellbeing index (0-100) │ Lower secondary  <.001 
#>                                │ Upper secondary        
#>                                │ Tertiary               
#> 
#> Missing values removed: bmi (12).

# Test statistic alongside the p-value.
table_continuous(
  sochealth,
  select = c(bmi, wellbeing_score),
  by = education,
  statistic = TRUE
)
#> Descriptive statistics
#> 
#>  Variable                      │ Group              M     SD     Min    Max   
#> ───────────────────────────────┼──────────────────────────────────────────────
#>  Body mass index               │ Lower secondary  28.09   3.47  18.20   38.90 
#>                                │ Upper secondary  26.02   3.43  16.00   37.10 
#>                                │ Tertiary         24.39   3.52  16.00   33.00 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  WHO-5 wellbeing index (0-100) │ Lower secondary  57.22  15.44  18.70   97.90 
#>                                │ Upper secondary  68.97  13.62  26.70  100.00 
#>                                │ Tertiary         76.85  13.23  40.40  100.00 
#> 
#>  Variable                      │ Group            95% CI LL  95% CI UL   n  
#> ───────────────────────────────┼────────────────────────────────────────────
#>  Body mass index               │ Lower secondary    27.66      28.51    260 
#>                                │ Upper secondary    25.73      26.31    534 
#>                                │ Tertiary           24.04      24.74    394 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  WHO-5 wellbeing index (0-100) │ Lower secondary    55.33      59.10    261 
#>                                │ Upper secondary    67.82      70.12    539 
#>                                │ Tertiary           75.55      78.15    400 
#> 
#>  Variable                      │ Group                    Test             p   
#> ───────────────────────────────┼───────────────────────────────────────────────
#>  Body mass index               │ Lower secondary  F(2, 654.48) = 87.96   <.001 
#>                                │ Upper secondary                               
#>                                │ Tertiary                                      
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  WHO-5 wellbeing index (0-100) │ Lower secondary  F(2, 638.59) = 144.35  <.001 
#>                                │ Upper secondary                               
#>                                │ Tertiary                                      
#> 
#> Missing values removed: bmi (12).

# --- Effect sizes -------------------------------------------------------

# Auto-selected effect size with confidence interval (Hedges' g for
# binary `by`, eta-squared for k > 2).
table_continuous(
  sochealth,
  select = wellbeing_score,
  by = sex,
  effect_size = "auto",
  effect_size_ci = TRUE
)
#> Descriptive statistics
#> 
#>  Variable                      │ Group     M     SD     Min    Max    95% CI LL 
#> ───────────────────────────────┼────────────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │ Female  67.16  14.80  19.60  100.00    65.99   
#>                                │ Male    71.05  16.23  18.70  100.00    69.73   
#> 
#>  Variable                      │ Group   95% CI UL   n     p   
#> ───────────────────────────────┼───────────────────────────────
#>  WHO-5 wellbeing index (0-100) │ Female    68.33    620  <.001 
#>                                │ Male      72.37    580        
#> 
#>  Variable                      │ Group              ES            
#> ───────────────────────────────┼──────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │ Female  g = -0.25 [-0.36, -0.14] 
#>                                │ Male                             

# Explicit effect-size measure.
table_continuous(
  sochealth,
  select = wellbeing_score,
  by = education,
  effect_size = "eta_sq",
  effect_size_ci = TRUE,
  effect_size_digits = 3
)
#> Descriptive statistics
#> 
#>  Variable                      │ Group              M     SD     Min    Max   
#> ───────────────────────────────┼──────────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │ Lower secondary  57.22  15.44  18.70   97.90 
#>                                │ Upper secondary  68.97  13.62  26.70  100.00 
#>                                │ Tertiary         76.85  13.23  40.40  100.00 
#> 
#>  Variable                      │ Group            95% CI LL  95% CI UL   n  
#> ───────────────────────────────┼────────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │ Lower secondary    55.33      59.10    261 
#>                                │ Upper secondary    67.82      70.12    539 
#>                                │ Tertiary           75.55      78.15    400 
#> 
#>  Variable                      │ Group              p   
#> ───────────────────────────────┼────────────────────────
#>  WHO-5 wellbeing index (0-100) │ Lower secondary  <.001 
#>                                │ Upper secondary        
#>                                │ Tertiary               
#> 
#>  Variable                      │ Group                       ES             
#> ───────────────────────────────┼────────────────────────────────────────────
#>  WHO-5 wellbeing index (0-100) │ Lower secondary  η² = 0.208 [0.169, 0.246] 
#>                                │ Upper secondary                            
#>                                │ Tertiary                                   

# --- Selection helpers --------------------------------------------------

# Regex selection.
table_continuous(
  sochealth,
  select = "^life_sat",
  regex = TRUE
)
#> Descriptive statistics
#> 
#>  Variable                                   │  M     SD   Min   Max   95% CI LL 
#> ────────────────────────────────────────────┼───────────────────────────────────
#>  Satisfaction with health (1-5)             │ 3.55  1.25  1.00  5.00    3.48    
#>  Satisfaction with work (1-5)               │ 3.38  1.18  1.00  5.00    3.31    
#>  Satisfaction with relationships (1-5)      │ 3.72  1.10  1.00  5.00    3.66    
#>  Satisfaction with standard of living (1-5) │ 3.40  1.16  1.00  5.00    3.33    
#> 
#>  Variable                                   │ 95% CI UL   n   
#> ────────────────────────────────────────────┼─────────────────
#>  Satisfaction with health (1-5)             │   3.62     1192 
#>  Satisfaction with work (1-5)               │   3.45     1192 
#>  Satisfaction with relationships (1-5)      │   3.79     1192 
#>  Satisfaction with standard of living (1-5) │   3.46     1192 
#> 
#> Missing values removed: life_sat_health (8), life_sat_work (8), life_sat_relationships (8), life_sat_standard (8).

# Pretty labels keyed by column name.
table_continuous(
  sochealth,
  select = c(bmi, life_sat_health),
  labels = c(
    bmi = "Body mass index",
    life_sat_health = "Satisfaction with health"
  )
)
#> Descriptive statistics
#> 
#>  Variable                 │   M     SD    Min    Max   95% CI LL  95% CI UL 
#> ──────────────────────────┼─────────────────────────────────────────────────
#>  Body mass index          │ 25.93  3.72  16.00  38.90    25.72      26.14   
#>  Satisfaction with health │  3.55  1.25   1.00   5.00     3.48       3.62   
#> 
#>  Variable                 │  n   
#> ──────────────────────────┼──────
#>  Body mass index          │ 1188 
#>  Satisfaction with health │ 1192 
#> 
#> Missing values removed: bmi (12), life_sat_health (8).

# --- Output formats -----------------------------------------------------

# The rendered outputs below all wrap the same call:
#   table_continuous(sochealth,
#                    select = c(bmi, wellbeing_score),
#                    by = sex)
# only `output` changes. Assign each result to a variable -- some
# engines auto-print as a console-friendly text fallback inside
# the `?` help viewer.

# Wide / long data.frame (synonyms): one row per (variable x group).
table_continuous(
  sochealth,
  select = c(bmi, wellbeing_score),
  by = sex,
  output = "data.frame"
)
#>          variable                         label  group     mean        sd  min
#> 1             bmi               Body mass index Female 25.68506  3.781113 16.0
#> 2             bmi               Body mass index   Male 26.19685  3.638092 16.0
#> 3 wellbeing_score WHO-5 wellbeing index (0-100) Female 67.16194 14.798488 19.6
#> 4 wellbeing_score WHO-5 wellbeing index (0-100)   Male 71.04879 16.227304 18.7
#>     max ci_lower ci_upper   n test_type statistic      df1 df2      p.value
#> 1  38.9 25.38588 25.98425 616   welch_t -2.377237 1184.497  NA 1.760093e-02
#> 2  37.7 25.89808 26.49563 572      <NA>        NA       NA  NA           NA
#> 3 100.0 65.99480 68.32907 620   welch_t -4.326141 1168.700  NA 1.647005e-05
#> 4 100.0 69.72540 72.37219 580      <NA>        NA       NA  NA           NA

# \donttest{
# Rendered HTML / docx objects -- best viewed inside a
# Quarto / R Markdown document or a pkgdown article.
if (requireNamespace("tinytable", quietly = TRUE)) {
  tt <- table_continuous(
    sochealth, select = c(bmi, wellbeing_score), by = sex,
    output = "tinytable"
  )
}
if (requireNamespace("gt", quietly = TRUE)) {
  tbl <- table_continuous(
    sochealth, select = c(bmi, wellbeing_score), by = sex,
    output = "gt"
  )
}
if (requireNamespace("flextable", quietly = TRUE)) {
  ft <- table_continuous(
    sochealth, select = c(bmi, wellbeing_score), by = sex,
    output = "flextable"
  )
}

# Excel and Word: write to a temporary file.
if (requireNamespace("openxlsx2", quietly = TRUE)) {
  tmp <- tempfile(fileext = ".xlsx")
  table_continuous(
    sochealth, select = c(bmi, wellbeing_score), by = sex,
    output = "excel", excel_path = tmp
  )
  unlink(tmp)
}
if (
  requireNamespace("flextable", quietly = TRUE) &&
    requireNamespace("officer", quietly = TRUE)
) {
  tmp <- tempfile(fileext = ".docx")
  table_continuous(
    sochealth, select = c(bmi, wellbeing_score), by = sex,
    output = "word", word_path = tmp
  )
  unlink(tmp)
}
# }

if (FALSE) { # \dontrun{
# Clipboard: writes to the system clipboard.
table_continuous(
  sochealth, select = c(bmi, wellbeing_score), by = sex,
  output = "clipboard"
)
} # }