Summary tables for APA-style reporting

Choose the right function

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

In practice:

• use table_categorical() for smoking status, education, or activity;
• use table_continuous() for BMI, income, or scale scores;
• 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"
)

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"
)

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.

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.

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"
  )
)

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"
  )
)

Choose the output format

Both functions support the same reporting formats:

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.*")
  )

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
  )

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.

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