Summary tables for APA-style reporting • spicy

Choose the right function

Use the function that matches the type of variables you want to report:

Function	Use for	Optional `by`	Typical additions
`table_categorical()`	Factors, labelled categorical variables, grouped frequency-style summaries	Yes	Chi-squared test, association measure, confidence interval
`table_continuous()`	Numeric or continuous variables	Yes	Group-comparison test, statistic, effect size
`table_continuous_lm()`	Continuous outcomes in a linear-model framework	No, requires a single predictor	Robust / cluster-robust / bootstrap / jackknife SE, case weights, additive covariate adjustment, four effect-size families with noncentral CIs

In practice:

use table_categorical() for smoking status, education, or activity;
use table_continuous() for BMI, income, or scale scores;
use table_continuous_lm() when the same outcomes should be reported through simple weighted or robust linear models;
keep by for the grouping variable you want to compare across.

A shared interface

Both functions use the same core arguments:

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

Variable	Lower secondary		Upper secondary		Tertiary		Total		p	Cramer's V
	n	%	n	%	n	%	n	%
Smoking status									<.001	.14
No	179	69.6	415	78.7	332	84.9	926	78.8
Yes	78	30.4	112	21.3	59	15.1	249	21.2
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

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"
  ),
  output = "tinytable"
)

Variable	Group	M	SD	Min	Max	95% CI		n	p
						LL	UL
Body mass index	Lower secondary	28.09	3.47	18.20	38.90	27.66	28.51	260	<.001
	Upper secondary	26.02	3.43	16.00	37.10	25.73	26.31	534
	Tertiary	24.39	3.52	16.00	33.00	24.04	24.74	394
Well-being score	Lower secondary	57.22	15.44	18.70	97.90	55.33	59.10	261	<.001
	Upper secondary	68.97	13.62	26.70	100.00	67.82	70.12	539
	Tertiary	76.85	13.23	40.40	100.00	75.55	78.15	400
Satisfaction with health	Lower secondary	2.71	1.20	1.00	5.00	2.57	2.86	259	<.001
	Upper secondary	3.53	1.19	1.00	5.00	3.43	3.63	534
	Tertiary	4.11	1.04	1.00	5.00	4.01	4.21	399

table_continuous_lm(
  sochealth,
  select = c(bmi, wellbeing_score, life_sat_health),
  by = education,
  weights = weight
)
#> Continuous outcomes by Highest education level
#> 
#>  Variable                       │ M (Lower secondary)  M (Upper secondary) 
#> ────────────────────────────────┼──────────────────────────────────────────
#>  Body mass index                │        25.96                23.39        
#>  WHO-5 wellbeing index (0-100)  │        67.55                80.88        
#>  Satisfaction with health (1-5) │         3.45                 4.39        
#> 
#>  Variable                       │ M (Tertiary)    p     R²    n   
#> ────────────────────────────────┼─────────────────────────────────
#>  Body mass index                │    26.16      <.001  0.13  1188 
#>  WHO-5 wellbeing index (0-100)  │    66.52      <.001  0.19  1200 
#>  Satisfaction with health (1-5) │     3.39      <.001  0.15  1192

The same argument pattern works in both 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.

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

pkgdown_dark_gt(
  table_categorical(
    sochealth,
    select = c(smoking, physical_activity, dentist_12m),
    by = education,
    labels = c(
      "Smoking status",
      "Regular physical activity",
      "Visited a dentist in the last 12 months"
    ),
    output = "gt"
  )
)

Variable	Lower secondary		Upper secondary		Tertiary		Total		p	Cramer's V
	n	%	n	%	n	%	n	%
Smoking status									<.001	.14
No	179	69.6	415	78.7	332	84.9	926	78.8
Yes	78	30.4	112	21.3	59	15.1	249	21.2
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
Visited a dentist in the last 12 months									<.001	.22
No	113	43.3	174	32.3	67	16.8	354	29.5
Yes	148	56.7	365	67.7	333	83.2	846	70.5

Continuous variables

pkgdown_dark_gt(
  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,
    output = "gt"
  )
)

Variable	Group	M	SD	Min	Max	95% CI		n	p	ES
						LL	UL
Body mass index	Lower secondary	28.09	3.47	18.20	38.90	27.66	28.51	260	<.001	η² = 0.13
	Upper secondary	26.02	3.43	16.00	37.10	25.73	26.31	534
	Tertiary	24.39	3.52	16.00	33.00	24.04	24.74	394
Well-being score	Lower secondary	57.22	15.44	18.70	97.90	55.33	59.10	261	<.001	η² = 0.21
	Upper secondary	68.97	13.62	26.70	100.00	67.82	70.12	539
	Tertiary	76.85	13.23	40.40	100.00	75.55	78.15	400
Satisfaction with health	Lower secondary	2.71	1.20	1.00	5.00	2.57	2.86	259	<.001	η² = 0.16
	Upper secondary	3.53	1.19	1.00	5.00	3.43	3.63	534
	Tertiary	4.11	1.04	1.00	5.00	4.01	4.21	399

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

Model-based continuous variables

pkgdown_dark_gt(
  table_continuous_lm(
    sochealth,
    select = c(bmi, wellbeing_score, life_sat_health),
    by = sex,
    vcov = "HC3",
    statistic = TRUE,
    output = "gt"
  )
)

Variable	M (Female)	M (Male)	Δ (Male - Female)	95% CI		t	p	R²	n
				LL	UL
Body mass index	25.69	26.20	0.51	0.09	0.93	2.38	.018	0.00	1188
WHO-5 wellbeing index (0-100)	67.16	71.05	3.89	2.12	5.65	4.32	<.001	0.02	1200
Satisfaction with health (1-5)	3.51	3.59	0.08	-0.06	0.22	1.11	.267	0.00	1192

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

Choose the output format

All three 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"
  )
}

Post-process the returned table object

Both summary-table helpers return regular gt, tinytable, or flextable objects, so you can keep styling them with the native package API.

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 status", "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.*")
  )

Variable	Lower secondary		Upper secondary		Tertiary		Total		p	Cramer's V
Health behaviors by education
Categorical summary table
	n	%	n	%	n	%	n	%
Smoking status									<.001	.14
No	179	69.6	415	78.7	332	84.9	926	78.8
Yes	78	30.4	112	21.3	59	15.1	249	21.2
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 status", "Regular physical activity"),
  output = "tinytable"
)

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

Variable	Lower secondary		Upper secondary		Tertiary		Total		p	Cramer's V
	n	%	n	%	n	%	n	%
Smoking status									<.001	.14
No	179	69.6	415	78.7	332	84.9	926	78.8
Yes	78	30.4	112	21.3	59	15.1	249	21.2
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 families with noncentral CIs.

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