Summary tables for reporting

library(spicy)

spicy’s reporting helpers cover the full APA Manual 7 table sequence used in empirical articles:

• table_categorical() and table_continuous() build Table 1 (sample characteristics) and Table 2 (group comparisons);
• table_continuous_lm() extends Table 2 to the linear-model regime when group means need robust or cluster-robust SE, covariate adjustment, or group tests under case weights — the descriptive families take weights = natively for the estimates themselves, and table_continuous() points here the moment weighted means are to be tested;
• table_continuous_svy() and table_categorical_svy() build the same Tables 1 and 2 from a survey::svydesign object, when the sample is stratified, clustered, or carried by replicate weights — design-based SEs and CIs, design degrees of freedom, design tests (see Summary tables from a survey design);
• table_regression() builds Table 3 (the coefficient table) from one or several fitted models — lm() / glm() through the 36 supported classes listed by table_regression_models().

The four functions share the same output grammar — the same output formats (default console ASCII, gt, tinytable, flextable, word, excel, clipboard), the same decimal_mark, p_digits, labels, and align arguments, and the same digits control for numeric cells (the categorical table’s cells are percentages, so it spells the argument percent_digits) — so a single reporting workflow can move smoothly from descriptive to inferential without juggling different APIs. This article focuses on that shared logic; the function-specific articles cover the methodological options in depth.

Choose the right function

Use the function that matches the unit you want to report:

Function	Reports	Selection grammar	Typical additions
table_categorical()	Categorical variables (factors, labelled)	select, by	Chi-squared test, association measure (phi, cramer_v, tau_b, …), confidence interval
table_continuous()	Numeric / continuous variables	select, by	Group-comparison test (Student / Welch t, Wilcoxon, ANOVA, Kruskal–Wallis), effect size (Hedges’ g, η², rank-biserial r, ε²)
table_continuous_lm()	Numeric outcomes through one linear model per outcome	select, by (single predictor)	Robust / cluster-robust / bootstrap / jackknife SE, case weights, additive covariate adjustment, four effect-size measures with noncentral CIs
table_outcome()	One numeric outcome across the levels of several categorical variables	outcome (one), select (many)	One group comparison per block, an Overall marginal row, the same statistic tokens as table_continuous()
table_continuous_svy(), table_categorical_svy()	The same descriptive tables from a survey::svydesign (stratified, clustered, replicate-weight samples)	design-first: design, then select, by	Design-based SE and CI, design df, Rao–Scott and design t/F tests, design effects, observed and weighted counts
table_regression()	One or several fitted models — 36 classes, from lm() / glm() to mixed, ordinal, survival and Bayesian engines (see Supported models)	Fit-first: pass the model object(s) directly, no select / by	APA-aligned coefficient table with B, β, 95% CI, p, AME, robust variance, side-by-side and hierarchical layouts

In practice, follow the APA sequence:

• start with table_categorical() for smoking, education, or activity — APA Table 1 categorical descriptors;
• use table_continuous() for BMI, well-being, or income — Table 1 continuous descriptors and Table 2 unadjusted group comparisons;
• transpose to table_outcome() when the question is about one outcome and many groupings rather than many outcomes and one grouping – a well-being score described by sex, by education and by region, block after block;
• move to table_continuous_svy() / table_categorical_svy() when the data are a survey design rather than a plain sample – the numbers then come from the survey package, with the design’s SEs, degrees of freedom and tests;
• switch to table_continuous_lm() when the same comparison must account for case weights, robust SE, or covariate adjustment;
• finish with table_regression() once the substantive model is fitted — APA Table 3 with all predictors, factor groupings, reference rows, and (optionally) standardised coefficients, marginal effects, or nested model comparisons.

The descriptive functions share one selection grammar — on a data frame, or on a survey::svydesign for the _svy twins; table_regression() is fit-first — you build the model the usual R way (lm(), glm(), or any other supported engine) and hand the object in. All of them share the post-construction grammar (output, labels, decimal_mark, align, and the digits controls), so swapping functions never breaks your rendering pipeline.

A shared interface

The examples below use sochealth, the dataset bundled with spicy: a simulated social-health survey of 1200 respondents and 24 variables, every one of them carrying a variable label (see ?sochealth).

The three descriptive functions share the same core arguments:

table_categorical(
  sochealth,
  select = c(smoking, physical_activity),
  by = education,
  labels = c(
    smoking           = "Smoking status",
    physical_activity = "Regular physical activity"
  )
)
#> Categorical table by education
#> 
#>  Variable                  │ Lower secondary n  Lower secondary % 
#> ───────────────────────────┼──────────────────────────────────────
#>  Smoking status            │                                      
#>    No                      │        179               68.6        
#>    Yes                     │         78               29.9        
#>    (Missing)               │          4                1.5        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity │                                      
#>    No                      │        177               67.8        
#>    Yes                     │         84               32.2        
#> 
#>  Variable                  │ Upper secondary n  Upper secondary %  Tertiary n 
#> ───────────────────────────┼──────────────────────────────────────────────────
#>  Smoking status            │                                                  
#>    No                      │        415               77.0            332     
#>    Yes                     │        112               20.8             59     
#>    (Missing)               │         12                2.2              9     
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity │                                                  
#>    No                      │        310               57.5            163     
#>    Yes                     │        229               42.5            237     
#> 
#>  Variable                  │ Tertiary %  Total n  Total %    p    Cramer's V 
#> ───────────────────────────┼─────────────────────────────────────────────────
#>  Smoking status            │                               <.001     .14     
#>    No                      │    83.0       926     77.2                      
#>    Yes                     │    14.8       249     20.8                      
#>    (Missing)               │     2.2        25      2.1                      
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity │                               <.001     .21     
#>    No                      │    40.8       650     54.2                      
#>    Yes                     │    59.2       550     45.8

table_continuous(
  sochealth,
  select = c(bmi, wellbeing_score, life_sat_health),
  by = education,
  labels = c(
    bmi = "Body mass index",
    wellbeing_score = "Well-being score",
    life_sat_health = "Satisfaction with health"
  )
)
#> Descriptive statistics by Highest education level
#> 
#>  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 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Well-being score         │ 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 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Satisfaction with health │ Lower secondary   2.71   1.20   1.00    5.00 
#>                           │ Upper secondary   3.53   1.19   1.00    5.00 
#>                           │ Tertiary          4.11   1.04   1.00    5.00 
#> 
#>  Variable                 │ Group            95% CI LL  95% CI UL   n     p   
#> ──────────────────────────┼───────────────────────────────────────────────────
#>  Body mass index          │ Lower secondary    27.66      28.51    260  <.001 
#>                           │ Upper secondary    25.73      26.31    534        
#>                           │ Tertiary           24.04      24.74    394        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Well-being score         │ Lower secondary    55.33      59.10    261  <.001 
#>                           │ Upper secondary    67.82      70.12    539        
#>                           │ Tertiary           75.55      78.15    400        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Satisfaction with health │ Lower secondary     2.57       2.86    259  <.001 
#>                           │ Upper secondary     3.43       3.63    534        
#>                           │ Tertiary            4.01       4.21    399        
#> 
#> Missing values removed: bmi (12), life_sat_health (8).

table_continuous_lm(
  sochealth,
  select = c(bmi, wellbeing_score, life_sat_health),
  by = education,
  weights = weight,
  vcov = "HC3"
)
#> Continuous outcomes by Highest education level
#> 
#>  Variable                       │ M (Lower secondary)  M (Upper secondary) 
#> ────────────────────────────────┼──────────────────────────────────────────
#>  Body mass index                │        27.85                25.79        
#>  WHO-5 wellbeing index (0-100)  │        57.71                68.39        
#>  Satisfaction with health (1-5) │         2.75                 3.50        
#> 
#>  Variable                       │ M (Tertiary)    p     R²    n   
#> ────────────────────────────────┼─────────────────────────────────
#>  Body mass index                │    24.23      <.001  0.13  1188 
#>  WHO-5 wellbeing index (0-100)  │    76.55      <.001  0.19  1200 
#>  Satisfaction with health (1-5) │     4.09      <.001  0.15  1192 
#> 
#> Note. Std. errors: heteroskedasticity-robust (HC3).
#> Missing values removed: bmi (12), life_sat_health (8).

Two words on the weighted example. weights supplies case weights, passed to lm(weights = ) — appropriate for weighted article tables, but not a substitute for a full complex-survey design (strata, clusters, calibration), which is the survey package’s domain. And because sochealth$weight holds calibrated sampling weights, the example pairs them with a heteroskedasticity-robust variance (vcov = "HC3"): the default "classical" WLS variance would treat the weights as precision weights, which sampling weights are not.

The same argument pattern is used in all three cases:

• select chooses the reported variables;
• by defines the grouping structure;
• labels cleans up the row labels;
• output decides how the result is rendered or exported.

For model-based continuous tables, the same pattern applies, but by must be a single predictor because one linear model is fit per outcome.

table_regression() joins the same labels / output / decimal_mark / digits grammar but is fit-first: rather than expressing model structure inline through select and by, you pass one or several already-fitted lm() or glm() objects:

fit <- lm(
  wellbeing_score ~ age + sex + smoking + physical_activity,
  data = sochealth
)
table_regression(
  fit,
  labels = c(
    age               = "Age (years)",
    sex               = "Sex",
    smoking           = "Smoking status",
    physical_activity = "Regular physical activity"
  )
)
#> Linear regression: wellbeing_score
#> 
#>  Variable                   │    B      SE       95% CI        p   
#> ────────────────────────────┼──────────────────────────────────────
#>  (Intercept)                │   64.18  1.69  [60.87, 67.49]  <.001 
#>  Age (years)                │    0.04  0.03  [-0.02,  0.10]   .171 
#>  Sex:                       │                                      
#>    Female (ref.)            │     –     –          –          –    
#>    Male                     │    3.88  0.90  [ 2.11,  5.65]  <.001 
#>  Smoking status:            │                                      
#>    No (ref.)                │     –     –          –          –    
#>    Yes                      │   -1.73  1.10  [-3.90,  0.43]   .117 
#>  Regular physical activity: │                                      
#>    No (ref.)                │     –     –          –          –    
#>    Yes                      │    2.70  0.91  [ 0.93,  4.48]   .003 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                          │ 1175                                 
#>  R²                         │    0.03                              
#>  Adj. R²                    │    0.02                              
#> 
#> Note. Linear regression.
#> Std. errors: classical (OLS).

This split is intentional. The descriptive trio (categorical, continuous, continuous_lm) reports the data — select and by describe what you want to see. table_regression() reports the model — the model formula has already declared which predictors, interactions, polynomials, transformations, splines, and contrasts to report, so passing those again through select / by would duplicate the model object’s information and risk diverging from it.

A practical reporting sequence

A common report contains both table types, often with the same grouping variable. For example, you might first summarize categorical health behaviors, then summarize continuous well-being indicators.

Categorical variables

table_categorical(
  sochealth,
  select = c(smoking, physical_activity, dentist_12m),
  by = education,
  labels = c(
    smoking           = "Smoking status",
    physical_activity = "Regular physical activity",
    dentist_12m       = "Visited a dentist in the last 12 months"
  )
)
#> Categorical table by education
#> 
#>  Variable                                │ Lower secondary n  Lower secondary % 
#> ─────────────────────────────────────────┼──────────────────────────────────────
#>  Smoking status                          │                                      
#>    No                                    │        179               68.6        
#>    Yes                                   │         78               29.9        
#>    (Missing)                             │          4                1.5        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity               │                                      
#>    No                                    │        177               67.8        
#>    Yes                                   │         84               32.2        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Visited a dentist in the last 12 months │                                      
#>    No                                    │        113               43.3        
#>    Yes                                   │        148               56.7        
#> 
#>  Variable                                │ Upper secondary n  Upper secondary % 
#> ─────────────────────────────────────────┼──────────────────────────────────────
#>  Smoking status                          │                                      
#>    No                                    │        415               77.0        
#>    Yes                                   │        112               20.8        
#>    (Missing)                             │         12                2.2        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity               │                                      
#>    No                                    │        310               57.5        
#>    Yes                                   │        229               42.5        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Visited a dentist in the last 12 months │                                      
#>    No                                    │        174               32.3        
#>    Yes                                   │        365               67.7        
#> 
#>  Variable                                │ Tertiary n  Tertiary %  Total n 
#> ─────────────────────────────────────────┼─────────────────────────────────
#>  Smoking status                          │                                 
#>    No                                    │    332         83.0       926   
#>    Yes                                   │     59         14.8       249   
#>    (Missing)                             │      9          2.2        25   
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity               │                                 
#>    No                                    │    163         40.8       650   
#>    Yes                                   │    237         59.2       550   
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Visited a dentist in the last 12 months │                                 
#>    No                                    │     67         16.8       354   
#>    Yes                                   │    333         83.2       846   
#> 
#>  Variable                                │ Total %    p    Cramer's V 
#> ─────────────────────────────────────────┼────────────────────────────
#>  Smoking status                          │          <.001     .14     
#>    No                                    │  77.2                      
#>    Yes                                   │  20.8                      
#>    (Missing)                             │   2.1                      
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity               │          <.001     .21     
#>    No                                    │  54.2                      
#>    Yes                                   │  45.8                      
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Visited a dentist in the last 12 months │          <.001     .22     
#>    No                                    │  29.5                      
#>    Yes                                   │  70.5

Continuous variables

table_continuous(
  sochealth,
  select = c(bmi, wellbeing_score, life_sat_health),
  by = education,
  labels = c(
    bmi = "Body mass index",
    wellbeing_score = "Well-being score",
    life_sat_health = "Satisfaction with health"
  ),
  p_value = TRUE,
  effect_size = TRUE
)
#> Descriptive statistics by Highest education level
#> 
#>  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 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Well-being score         │ 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 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Satisfaction with health │ Lower secondary   2.71   1.20   1.00    5.00 
#>                           │ Upper secondary   3.53   1.19   1.00    5.00 
#>                           │ Tertiary          4.11   1.04   1.00    5.00 
#> 
#>  Variable                 │ Group            95% CI LL  95% CI UL   n     p   
#> ──────────────────────────┼───────────────────────────────────────────────────
#>  Body mass index          │ Lower secondary    27.66      28.51    260  <.001 
#>                           │ Upper secondary    25.73      26.31    534        
#>                           │ Tertiary           24.04      24.74    394        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Well-being score         │ Lower secondary    55.33      59.10    261  <.001 
#>                           │ Upper secondary    67.82      70.12    539        
#>                           │ Tertiary           75.55      78.15    400        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Satisfaction with health │ Lower secondary     2.57       2.86    259  <.001 
#>                           │ Upper secondary     3.43       3.63    534        
#>                           │ Tertiary            4.01       4.21    399        
#> 
#>  Variable                 │ Group               ES     
#> ──────────────────────────┼────────────────────────────
#>  Body mass index          │ Lower secondary  η² = 0.13 
#>                           │ Upper secondary            
#>                           │ Tertiary                   
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Well-being score         │ Lower secondary  η² = 0.21 
#>                           │ Upper secondary            
#>                           │ Tertiary                   
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Satisfaction with health │ Lower secondary  η² = 0.16 
#>                           │ Upper secondary            
#>                           │ Tertiary                   
#> 
#> Missing values removed: bmi (12), life_sat_health (8).

This keeps the reporting structure consistent while still using the function that fits each variable type.

Model-based continuous variables

table_continuous_lm(
  sochealth,
  select = c(bmi, wellbeing_score, life_sat_health),
  by = sex,
  vcov = "HC3",
  statistic = TRUE
)
#> Continuous outcomes by Sex
#> 
#>  Variable                       │ M (Female)  M (Male)  Δ (Male - Female) 
#> ────────────────────────────────┼─────────────────────────────────────────
#>  Body mass index                │   25.69      26.20          0.51        
#>  WHO-5 wellbeing index (0-100)  │   67.16      71.05          3.89        
#>  Satisfaction with health (1-5) │    3.51       3.59          0.08        
#> 
#>  Variable                       │ 95% CI LL  95% CI UL   t      p     R²    n   
#> ────────────────────────────────┼───────────────────────────────────────────────
#>  Body mass index                │    0.09      0.93     2.38   .018  0.00  1188 
#>  WHO-5 wellbeing index (0-100)  │    2.12      5.65     4.32  <.001  0.02  1200 
#>  Satisfaction with health (1-5) │   -0.06      0.22     1.11   .267  0.00  1192 
#> 
#> Note. Std. errors: heteroskedasticity-robust (HC3).
#> Missing values removed: bmi (12), life_sat_health (8).

This is the better summary-table path when the article is already organized around simple linear models, weighted analyses, or robust standard errors.

A balance table instead of a significance table

When the two groups are trial arms or a treated / control contrast, the baseline table is read for balance, and the convention of that literature is the standardized mean difference rather than the p-value. Both descriptive families take smd = TRUE, with the same meaning and the same refusals:

table_continuous(
  sochealth,
  select = c(age, bmi, wellbeing_score),
  by = sex,
  smd = TRUE,
  p_value = FALSE
)
#> Descriptive statistics by Sex
#> 
#>  Variable                      │ Group     M     SD     Min    Max    95% CI LL 
#> ───────────────────────────────┼────────────────────────────────────────────────
#>  Age (years)                   │ Female  49.38  14.91  25.00   75.00    48.20   
#>                                │ Male    49.14  14.50  25.00   75.00    47.96   
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Body mass index               │ Female  25.69   3.78  16.00   38.90    25.39   
#>                                │ Male    26.20   3.64  16.00   37.70    25.90   
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  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    SMD  
#> ───────────────────────────────┼───────────────────────────────
#>  Age (years)                   │ Female    50.55    620   0.02 
#>                                │ Male      50.32    580        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Body mass index               │ Female    25.98    616  -0.14 
#>                                │ Male      26.50    572        
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  WHO-5 wellbeing index (0-100) │ Female    68.33    620  -0.25 
#>                                │ Male      72.37    580        
#> 
#> Missing values removed: bmi (12). SMD = standardized mean difference (Female - Male); |SMD| > 0.1 is the usual imbalance threshold.

table_categorical(
  sochealth,
  select = c(smoking, physical_activity),
  by = sex,
  smd = TRUE
)
#> Categorical table by sex
#> 
#>  Variable                  │ Female n  Female %  Male n  Male %  Total n 
#> ───────────────────────────┼─────────────────────────────────────────────
#>  Current smoker            │                                             
#>    No                      │   475       76.6     451     77.8     926   
#>    Yes                     │   131       21.1     118     20.3     249   
#>    (Missing)               │    14        2.3      11      1.9      25   
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity │                                             
#>    No                      │   334       53.9     316     54.5     650   
#>    Yes                     │   286       46.1     264     45.5     550   
#> 
#>  Variable                  │ Total %   p    Phi  SMD  
#> ───────────────────────────┼──────────────────────────
#>  Current smoker            │          .713  .01  0.02 
#>    No                      │  77.2                    
#>    Yes                     │  20.8                    
#>    (Missing)               │   2.1                    
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Regular physical activity │          .832  .01  0.01 
#>    No                      │  54.2                    
#>    Yes                     │  45.8                    
#> 
#> SMD = standardized mean difference (Female - Male); |SMD| > 0.1 is the usual imbalance threshold.

Exactly two groups, no confidence interval and no p-value on the column itself, and the usual rule of thumb (|SMD| > 0.1) quoted in the table note without any cell being highlighted. Note the asymmetry between the two calls: table_continuous() can drop its p column, table_categorical() currently cannot.

The coefficient table

Once the substantive model is fitted, table_regression() produces the APA Table 3 coefficient summary. The same output argument controls rendering, so the regression table sits in the same reporting pipeline as the descriptive ones above:

fit <- lm(
  wellbeing_score ~ age + sex + smoking + physical_activity,
  data = sochealth
)
table_regression(
  fit,
  standardized = "refit",
  show_columns = c("b", "beta", "ci", "p"),
  vcov = "HC3"
)
#> Linear regression: wellbeing_score
#> 
#>  Variable           │    B       β        95% CI        p   
#> ────────────────────┼───────────────────────────────────────
#>  (Intercept)        │   64.18  -0.18  [60.95, 67.42]  <.001 
#>  age                │    0.04   0.04  [-0.02,  0.10]   .169 
#>  sex:               │                                       
#>    Female (ref.)    │     –      –          –          –    
#>    Male             │    3.88   0.25  [ 2.10,  5.66]  <.001 
#>  smoking:           │                                       
#>    No (ref.)        │     –      –          –          –    
#>    Yes              │   -1.73  -0.11  [-3.92,  0.45]   .120 
#>  physical_activity: │                                       
#>    No (ref.)        │     –      –          –          –    
#>    Yes              │    2.70   0.17  [ 0.93,  4.48]   .003 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1175                                  
#>  R²                 │    0.03                               
#>  Adj. R²            │    0.02                               
#> 
#> Note. Linear regression.
#> Std. errors: heteroskedasticity-robust (HC3).
#> β = standardised coefficient ("refit": outcome and numeric predictors z-scored, factor dummies on 0/1).

The default footer documents the variance estimator, flags standardised coefficients (β = standardised coefficient), and reports any multiplicity correction, so the inferential regime is visible without leaving the table. One thing it does not carry: the name of the standardisation method. Here standardized = "refit" produced the β column, but the footer would read the same under any of the five methods, so record the method in the table note or the text of the article.

Side-by-side reporting of competing specifications (e.g., unadjusted vs. covariate-adjusted, or lm vs. glm) is supported by passing a list of fits:

fit_unadj <- lm(wellbeing_score ~ smoking, data = sochealth)
fit_adj   <- lm(
  wellbeing_score ~ smoking + age + sex + physical_activity,
  data = sochealth
)
table_regression(
  list("Unadjusted" = fit_unadj, "Adjusted" = fit_adj),
  show_columns = c("b", "ci", "p")
)
#> Linear regression comparison: wellbeing_score
#> 
#>                                 Unadjusted                   Adjusted         
#>                       ──────────────────────────────  ─────────────────────── 
#>  Variable           │    B         95% CI        p       B         95% CI     
#> ────────────────────┼─────────────────────────────────────────────────────────
#>  (Intercept)        │   69.36  [68.36, 70.37]  <.001    64.18  [60.87, 67.49] 
#>  smoking:           │                                                         
#>    No (ref.)        │     –          –          –         –          –        
#>    Yes              │   -1.72  [-3.91,  0.47]   .124    -1.73  [-3.90,  0.43] 
#>  age                │                                    0.04  [-0.02,  0.10] 
#>  sex:               │                                                         
#>    Female (ref.)    │                                     –          –        
#>    Male             │                                    3.88  [ 2.11,  5.65] 
#>  physical_activity: │                                                         
#>    No (ref.)        │                                     –          –        
#>    Yes              │                                    2.70  [ 0.93,  4.48] 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1175                            1175                    
#>  R²                 │    0.00                            0.03                 
#>  Adj. R²            │    0.00                            0.02                 
#> 
#>                       Adju… 
#>                       ───── 
#>  Variable           │ p (B) 
#> ────────────────────┼───────
#>  (Intercept)        │ <.001 
#>  smoking:           │       
#>    No (ref.)        │  –    
#>    Yes              │  .117 
#>  age                │  .171 
#>  sex:               │       
#>    Female (ref.)    │  –    
#>    Male             │ <.001 
#>  physical_activity: │       
#>    No (ref.)        │  –    
#>    Yes              │  .003 
#> 
#> Note. Linear regression models.
#> Std. errors: classical (OLS).

For binary or count outcomes, swap lm() for glm() and request response-scale reporting (odds ratios, incidence rate ratios, etc.):

fit_glm <- glm(
  smoking ~ age + sex + physical_activity,
  data = sochealth,
  family = binomial()
)
table_regression(
  fit_glm,
  exponentiate = TRUE,
  show_columns = c("b", "ci", "p", "ame", "ame_ci", "ame_p")
)
#> Logistic regression: smoking
#> 
#>  Variable           │   OR        95% CI       p     AME      95% CI        p   
#> ────────────────────┼───────────────────────────────────────────────────────────
#>  (Intercept)        │    0.21  [0.13, 0.36]  <.001                              
#>  age                │    1.01  [1.00, 1.01]   .298   0.00  [-0.00, 0.00]   .297 
#>  sex:               │                                                           
#>    Female (ref.)    │     –         –         –       –          –         –    
#>    Male             │    0.95  [0.72, 1.26]   .723  -0.01  [-0.06, 0.04]   .723 
#>  physical_activity: │                                                           
#>    No (ref.)        │     –         –         –       –          –         –    
#>    Yes              │    1.02  [0.77, 1.35]   .883   0.00  [-0.04, 0.05]   .883 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1175                                                      
#>  R² (McFadden)      │    0.00                                                   
#>  R² (Nagelkerke)    │    0.00                                                   
#>  AIC                │ 1220.5                                                    
#> 
#> Note. Logistic regression.
#> Std. errors: classical (Fisher information).
#> AME = average marginal effect; OR = odds ratio.
#> Coefficients exponentiated and displayed as OR; CI bounds exponentiated.

Average marginal effects (ame) are useful next to the odds ratio because they report a probability-scale change for each predictor — the quantity most reviewers want to interpret directly. Note the two p columns: the first tests the coefficient (the log odds ratio), ame_p tests the average marginal effect itself. The two can differ under non-linear links or interactions, which is why table_regression() warns if you request ame alongside p without also requesting ame_p.

For the epidemiological variant of Table 2 — a univariable screen of every candidate predictor set against the multivariable model — table_regression_uv() builds the whole two-part layout in one call; see the Univariable screening section of Publication-ready regression tables.

Choose the output format

All four functions support the same reporting formats:

Output	Best use
"default"	Quick console review in plain ASCII
"tinytable"	Quarto or R Markdown documents
"gt"	HTML output with styled reporting tables
"flextable"	Office-first workflows; also renders in HTML
"excel"	Spreadsheet handoff or downstream editing
"word"	Direct .docx export
"clipboard"	Fast pasting into another application

Pick the output based on where the table is going, not on the analysis itself. The underlying selection and grouping pattern stays the same.

If you want an object that fits naturally into Word and PowerPoint workflows but can also be rendered in HTML documents, flextable is a good choice:

if (requireNamespace("flextable", quietly = TRUE)) {
  table_continuous(
    sochealth,
    select = c(bmi, wellbeing_score, life_sat_health),
    by = education,
    output = "flextable"
  )
}

House styles

Everything above uses spicy’s defaults: two decimals, APA-style p values (leading zero dropped, < .001 floor), confidence intervals in brackets. Those defaults are one house style among several. spicy_style() names the others — each rule sourced from the journal’s published author guidelines:

table_categorical(sochealth, select = smoking, by = sex, style = "jama")
#> Categorical table by sex
#> 
#>  Variable       │ Female n  Female %  Male n  Male %  Total n  Total %   p  
#> ────────────────┼───────────────────────────────────────────────────────────
#>  Current smoker │                                                       .71 
#>    No           │   475       76.6     451     77.8     926     77.2        
#>    Yes          │   131       21.1     118     20.3     249     20.8        
#>    (Missing)    │    14        2.3      11      1.9      25      2.1        
#> 
#>  Variable       │ Phi 
#> ────────────────┼─────
#>  Current smoker │ .01 
#>    No           │     
#>    Yes          │     
#>    (Missing)    │

The visible change here is the p column: JAMA rounds p values to two decimals (.71), where the default reports three (.713).

Two properties make styles safe to adopt. A style only moves display defaults — decimals, p notation, interval punctuation — never the statistics underneath; and an argument you set explicitly always wins over it, so style = "jama" with p_digits = 3 keeps your three decimals. The style travels with the table: the same call rendered to Word, Excel, or HTML follows it, and options(spicy.style = "lancet") sets one for a whole document. ?spicy_style lists every style and every rule with the sentence it comes from.

Table language

A style is what a journal asks for. A language is what your reader reads. options(spicy.language = "fr") prints the table in French — headers, row labels, titles and table notes, and the numbers with them:

options(spicy.language = "fr")
table_continuous(sochealth, select = bmi, by = sex)
#> Statistiques descriptives selon Sex
#> 
#>  Variable        │ Groupe    M     ET    Min    Max   95% CI LL  95% CI UL   n  
#> ─────────────────┼──────────────────────────────────────────────────────────────
#>  Body mass index │ Female  25,69  3,78  16,00  38,90    25,39      25,98    616 
#>                  │ Male    26,20  3,64  16,00  37,70    25,90      26,50    572 
#> 
#>  Variable        │ Groupe    p   
#> ─────────────────┼───────────────
#>  Body mass index │ Female  0,018 
#>                  │ Male          
#> 
#> Valeurs manquantes retirées : bmi (12).

One gesture, one coherent table. A language brings its typography with it: the decimal comma, and the leading zero French typography keeps on a p value (0,003 where the English default writes .003). The language of a report is a property of the report, not of a call, so it is set once in the setup chunk. "en" is the default and is unchanged by any of this. The language reaches every table, the exploration pair included: freq() and cross_tab() take the comma from it too, and an argument you type wins. One boundary is worth knowing: figures are frozen when a table is built, words when it is printed, which is one more reason the language belongs in the setup chunk.

A journal style composes with the language rather than replacing it. A theme encodes only what its own author guidelines state, so whatever it leaves open the language still fills — JAMA fixes no decimal mark, and a French JAMA table keeps the comma:

table_continuous(sochealth, select = bmi, by = sex, style = "jama")
#> Statistiques descriptives selon Sex
#> 
#>  Variable        │ Groupe    M     ET    Min    Max   95% CI LL  95% CI UL   n  
#> ─────────────────┼──────────────────────────────────────────────────────────────
#>  Body mass index │ Female  25,69  3,78  16,00  38,90    25,39      25,98    616 
#>                  │ Male    26,20  3,64  16,00  37,70    25,90      26,50    572 
#> 
#>  Variable        │ Groupe   p  
#> ─────────────────┼─────────────
#>  Body mass index │ Female  ,02 
#>                  │ Male        
#> 
#> Valeurs manquantes retirées : bmi (12).

Where the two do meet, the theme wins — you asked for it by name. JAMA’s own rule drops the leading zero of a p value, so the table above writes ,02 rather than 0,02; style = "lancet" keeps the journal’s midline decimal point instead of the comma. To keep a theme’s other rules and restore the zero, compose the variant: style = spicy_style("jama", p_style = "standard").

An argument you type wins over both, which is the escape hatch for a bilingual table — French words, decimal point. It moves only the mark: the p value keeps its French leading zero (0.018), since the leading-zero rule is a style lever, not an argument:

table_continuous(sochealth, select = bmi, by = sex, decimal_mark = ".")
#> Statistiques descriptives selon Sex
#> 
#>  Variable        │ Groupe    M     ET    Min    Max   95% CI LL  95% CI UL   n  
#> ─────────────────┼──────────────────────────────────────────────────────────────
#>  Body mass index │ Female  25.69  3.78  16.00  38.90    25.39      25.98    616 
#>                  │ Male    26.20  3.64  16.00  37.70    25.90      26.50    572 
#> 
#>  Variable        │ Groupe    p   
#> ─────────────────┼───────────────
#>  Body mass index │ Female  0.018 
#>                  │ Male          
#> 
#> Valeurs manquantes retirées : bmi (12).

When one word has to change and a language does not — a questionnaire where a missing category means a refusal, not an absent value — options(spicy.labels = ) overrides labels one at a time, on top of whatever language is in force:

options(spicy.labels = list(row_missing_level = "(No answer)"))
table_categorical(sochealth, select = sex, by = smoking)
#> Categorical table by smoking
#> 
#>  Variable │ No n  No %  Yes n  Yes %  (No answer) n  (No answer) %  Total n 
#> ──────────┼─────────────────────────────────────────────────────────────────
#>  Sex      │                                                                 
#>    Female │ 475   51.3   131   52.6        14            56.0         620   
#>    Male   │ 451   48.7   118   47.4        11            44.0         580   
#> 
#>  Variable │ Total %   p    Phi 
#> ──────────┼────────────────────
#>  Sex      │          .713  .01 
#>    Female │  51.7              
#>    Male   │  48.3
options(spicy.labels = NULL)

spicy_labels() is how you find the key for a label you want to change: it returns every key with the text it currently resolves to.

Two things a language deliberately does not move. The column names of the exported frames are a contract your code indexes into, so out[["Yes %"]] resolves whatever the language — spicy translates its own vocabulary, never your data. And errors and warnings stay in English, because they are read by developers and quoted in bug reports.

The one column name that does follow the language comes from the same rule read the other way: a column named after a level of by takes that level’s spelling, and spicy’s own missing category is a level. So table_categorical(by = ) on a variable with missing values gives (Missing) n in English and (Manquant) n in French. If your code selects that column, build its name from spicy_labels()[["row_missing_level"]] rather than typing it.

Citing table values in the text

The number a sentence quotes should be the number the table prints — retyping it is how a manuscript ends up saying 3.9 where the table says 3.90, or keeping a p value a revision changed. inline() returns one cell of a spicy table as text, formatted by the same machinery that formatted the table:

fit <- lm(wellbeing_score ~ age + sex, data = sochealth)
tbl <- table_regression(fit)
inline(tbl, sex, "Male", "b")
#> [1] "3.90"

so in Quarto you write `r inline(tbl, sex, "Male", "b")` inside the sentence. A {token} pattern quotes a full fragment in one call:

inline(tbl, sex, "Male", "{b} ({ci_label} {ci}; p {p})")
#> [1] "3.90 (95% CI [2.14, 5.65]; p <.001)"

Two properties carry the guarantee. The text follows the table: under style = "jama", decimal_mark = "," or a table language, the cited string changes with the printed one. And the addressing survives relabeling: rows are found by the source variable and level — not by the displayed label — so labels = c(sex = "Administrative sex") changes the table, not your calls. Misaddressing never fails silently: an unknown variable, level, or column token errors with the list of available choices, and a reference or non-estimable cell refuses with its reason instead of pasting a dash into a sentence.

Post-process the returned table object

All four summary-table helpers return regular gt, tinytable, or flextable objects, so you can keep styling them with the native package API. This includes table_regression(): nothing about the fit-first interface changes what the rendering engine produces.

Use gt:: functions when you want to keep the gt workflow:

tab <- pkgdown_dark_gt(table_categorical(
  sochealth,
  select = c(smoking, physical_activity),
  by = education,
  labels = c(
    smoking           = "Smoking status",
    physical_activity = "Regular physical activity"
  ),
  output = "gt"
))

tab |>
  gt::tab_header(
    title = "Health behaviors by education",
    subtitle = "Categorical summary table"
  ) |>
  gt::tab_source_note(
    gt::md("*Percentages are computed within each education group.*")
  )

Health behaviors by education
Categorical summary table
Variable	Lower secondary		Upper secondary		Tertiary		Total		p	Cramer's V
	n	%	n	%	n	%	n	%
Smoking status									<.001	.14
No	179	68.6	415	77.0	332	83.0	926	77.2
Yes	78	29.9	112	20.8	59	14.8	249	20.8
(Missing)	4	1.5	12	2.2	9	2.2	25	2.1
Regular physical activity									<.001	.21
No	177	67.8	310	57.5	163	40.8	650	54.2
Yes	84	32.2	229	42.5	237	59.2	550	45.8
Percentages are computed within each education group.

Use tinytable:: functions when you want lightweight table-specific styling:

tab <- table_categorical(
  sochealth,
  select = c(smoking, physical_activity),
  by = education,
  labels = c(
    smoking           = "Smoking status",
    physical_activity = "Regular physical activity"
  ),
  output = "tinytable"
)

tab |>
  tinytable::style_tt(
    i = 2:3,
    j = 2:5,
    background = "red",
    color = "white",
    bold = TRUE
  )

Categorical table by education

Variable	Lower secondary		Upper secondary		Tertiary		Total		p	Cramer's V
	n	%	n	%	n	%	n	%
Smoking status									<.001	.14
No	179	68.6	415	77.0	332	83.0	926	77.2
Yes	78	29.9	112	20.8	59	14.8	249	20.8
(Missing)	4	1.5	12	2.2	9	2.2	25	2.1
Regular physical activity									<.001	.21
No	177	67.8	310	57.5	163	40.8	650	54.2
Yes	84	32.2	229	42.5	237	59.2	550	45.8

Use flextable:: functions when you want to keep working toward Office or HTML document output. The example is shown as code here because the dark pkgdown theme is not a reliable preview of the final flextable HTML rendering:

if (requireNamespace("flextable", quietly = TRUE)) {
  tab <- table_continuous(
    sochealth,
    select = c(bmi, wellbeing_score),
    by = education,
    output = "flextable"
  )

  tab |>
    flextable::theme_booktabs() |>
    flextable::autofit() |>
    flextable::fontsize(size = 10, part = "all")
}

Keep the detailed options in the function-specific articles

The dedicated articles go deeper into each function:

• table_categorical() covers missing values, level filtering, association measures, and one-way frequency-style tables.
• table_continuous() covers grouped descriptive statistics, parametric and nonparametric tests, and effect sizes.
• table_continuous_lm() covers estimated marginal means or slopes from linear models, robust / cluster-robust / bootstrap / jackknife variance, case weights, additive covariate adjustment (G-computation or equal-weight), and four effect-size measures with noncentral CIs.
• table_outcome() covers the outcome-first layout: block structure, the Overall row, per-block comparisons and effect sizes, and the comparison with gtsummary::tbl_continuous().
• table_continuous_svy() and table_categorical_svy() cover the survey-design regime: delegation to the survey package, design degrees of freedom, quantile rules, and the weights-vs-design estimand boundary.
• table_regression() covers single- and multi-model regression tables across 30+ model classes (the map is Supported models), five standardisation methods (four for linear models, plus the glm-only pseudo-standardisation), partial effect sizes with noncentral-F CIs, average marginal effects, hierarchical (nested = TRUE) comparisons, multiplicity correction, and response-scale reporting for GLMs.

Use this article as the final reporting overview, then consult the function-specific articles when you need the detailed controls.