# Introduction to sumExtras

    library(sumExtras)
    library(gtsummary)
    library(dplyr)

    use_jama_theme()

> All examples in this vignette use the JAMA compact theme via `use_jama_theme()`. See `vignette("themes")` to set this up.

## The `extras()` Function

If you’ve worked with `{gtsummary}` before, you’re familiar with the typical workflow of building summary tables: creating a base table with `tbl_summary()`, then progressively adding features like overall columns, p-values, and formatting tweaks. While `{gtsummary}`’s modular approach provides flexibility, the same sequence of functions appears repeatedly in analysis scripts.

`extras()` consolidates the most common `{gtsummary}` formatting steps into one call: bold labels, a clean header, an overall column, p-values, and missing value cleanup.

Standard `{gtsummary}`
    theme_gtsummary_compact("jama")

    trial |>
      tbl_summary(by = trt) |>
      add_overall() |>
      add_p() |>
      bold_labels() |>
      bold_p() |>
      modify_header(label = "")
| With `{sumExtras}`
    use_jama_theme()

    trial |>
      tbl_summary(by = trt) |>
      extras()

### Customizing Output

You can control which features are applied:

    # Without p-values
    trial |>
      tbl_summary(by = trt) |>
      extras(pval = FALSE)

| Overall N = 200 1 | Drug A N = 98 1 | Drug B N = 102 1
--- | --- | --- | ---
Age | 47 (38, 57) | 46 (37, 60) | 48 (39, 56)
Unknown | 11 | 7 | 4
Marker Level (ng/mL) | 0.64 (0.22, 1.41) | 0.84 (0.23, 1.60) | 0.52 (0.18, 1.21)
Unknown | 10 | 6 | 4
T Stage |  |  |
T1 | 53 (27%) | 28 (29%) | 25 (25%)
T2 | 54 (27%) | 25 (26%) | 29 (28%)
T3 | 43 (22%) | 22 (22%) | 21 (21%)
T4 | 50 (25%) | 23 (23%) | 27 (26%)
Grade |  |  |
I | 68 (34%) | 35 (36%) | 33 (32%)
II | 68 (34%) | 32 (33%) | 36 (35%)
III | 64 (32%) | 31 (32%) | 33 (32%)
Tumor Response | 61 (32%) | 28 (29%) | 33 (34%)
Unknown | 7 | 3 | 4
Patient Died | 112 (56%) | 52 (53%) | 60 (59%)
Months to Death/Censor | 22.4 (15.9, 24.0) | 23.5 (17.4, 24.0) | 21.2 (14.5, 24.0)
1 Median (Q1, Q3); n (%)

    # Overall column last instead of first
    trial |>
      tbl_summary(by = trt) |>
      extras(last = TRUE)

| Drug A N = 98 1 | Drug B N = 102 1 | Overall N = 200 1 | p-value 2
--- | --- | --- | --- | ---
Age | 46 (37, 60) | 48 (39, 56) | 47 (38, 57) | 0.718
Unknown | 7 | 4 | 11 |
Marker Level (ng/mL) | 0.84 (0.23, 1.60) | 0.52 (0.18, 1.21) | 0.64 (0.22, 1.41) | 0.085
Unknown | 6 | 4 | 10 |
T Stage |  |  |  | 0.866
T1 | 28 (29%) | 25 (25%) | 53 (27%) |
T2 | 25 (26%) | 29 (28%) | 54 (27%) |
T3 | 22 (22%) | 21 (21%) | 43 (22%) |
T4 | 23 (23%) | 27 (26%) | 50 (25%) |
Grade |  |  |  | 0.871
I | 35 (36%) | 33 (32%) | 68 (34%) |
II | 32 (33%) | 36 (35%) | 68 (34%) |
III | 31 (32%) | 33 (32%) | 64 (32%) |
Tumor Response | 28 (29%) | 33 (34%) | 61 (32%) | 0.530
Unknown | 3 | 4 | 7 |
Patient Died | 52 (53%) | 60 (59%) | 112 (56%) | 0.412
Months to Death/Censor | 23.5 (17.4, 24.0) | 21.2 (14.5, 24.0) | 22.4 (15.9, 24.0) | 0.145
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test

    # Custom header text
    trial |>
      tbl_summary(by = trt) |>
      extras(header = "Variable")

Variable | Overall N = 200 1 | Drug A N = 98 1 | Drug B N = 102 1 | p-value 2
--- | --- | --- | --- | ---
Age | 47 (38, 57) | 46 (37, 60) | 48 (39, 56) | 0.718
Unknown | 11 | 7 | 4 |
Marker Level (ng/mL) | 0.64 (0.22, 1.41) | 0.84 (0.23, 1.60) | 0.52 (0.18, 1.21) | 0.085
Unknown | 10 | 6 | 4 |
T Stage |  |  |  | 0.866
T1 | 53 (27%) | 28 (29%) | 25 (25%) |
T2 | 54 (27%) | 25 (26%) | 29 (28%) |
T3 | 43 (22%) | 22 (22%) | 21 (21%) |
T4 | 50 (25%) | 23 (23%) | 27 (26%) |
Grade |  |  |  | 0.871
I | 68 (34%) | 35 (36%) | 33 (32%) |
II | 68 (34%) | 32 (33%) | 36 (35%) |
III | 64 (32%) | 31 (32%) | 33 (32%) |
Tumor Response | 61 (32%) | 28 (29%) | 33 (34%) | 0.530
Unknown | 7 | 3 | 4 |
Patient Died | 112 (56%) | 52 (53%) | 60 (59%) | 0.412
Months to Death/Censor | 22.4 (15.9, 24.0) | 23.5 (17.4, 24.0) | 21.2 (14.5, 24.0) | 0.145
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test

Or pass arguments as a list for reuse across tables:

    my_args <- list(pval = TRUE, overall = TRUE, last = TRUE)

    trial |>
      select(age, grade, stage, trt) |>
      tbl_summary(by = trt) |>
      extras(.args = my_args)

| Drug A N = 98 1 | Drug B N = 102 1 | Overall N = 200 1 | p-value 2
--- | --- | --- | --- | ---
Age | 46 (37, 60) | 48 (39, 56) | 47 (38, 57) | 0.718
Unknown | 7 | 4 | 11 |
Grade |  |  |  | 0.871
I | 35 (36%) | 33 (32%) | 68 (34%) |
II | 32 (33%) | 36 (35%) | 68 (34%) |
III | 31 (32%) | 33 (32%) | 64 (32%) |
T Stage |  |  |  | 0.866
T1 | 28 (29%) | 25 (25%) | 53 (27%) |
T2 | 25 (26%) | 29 (28%) | 54 (27%) |
T3 | 22 (22%) | 21 (21%) | 43 (22%) |
T4 | 23 (23%) | 27 (26%) | 50 (25%) |
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test

On non-stratified tables, `extras()` skips `add_overall()` and `add_p()` and applies only the formatting that makes sense. It works the same way with `tbl_regression()` — bold labels, bold significant p-values (from the model), clean header, and missing value cleanup are applied automatically while irrelevant options are silently ignored. It never breaks your pipeline.

    # Regression tables work too
    glm(response ~ age + grade, data = trial, family = binomial) |>
      tbl_regression(exponentiate = TRUE) |>
      extras()

| OR | 95% CI | p-value
--- | --- | --- | ---
Age | 1.02 | 1.00, 1.04 | 0.10
Grade |  |  |
I | — | — |
II | 0.85 | 0.39, 1.85 | 0.7
III | 1.01 | 0.47, 2.16 | >0.9
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

For merged tables, call `extras()` on each sub-table before merging. All formatting (bold labels, p-values, missing symbols) carries through `tbl_merge()`, so there’s no need to call `extras()` again after:

    t1 <- trial |>
      tbl_summary(by = trt, include = c(age, grade)) |>
      extras()

    t2 <- trial |>
      tbl_summary(by = trt, include = c(marker, stage)) |>
      extras()

    tbl_merge(list(t1, t2), tab_spanner = c("**Set A**", "**Set B**"))

## Cleaning Missing Values

`clean_table()` standardizes missing or zero-count representations (`"0 (NA%)"`, `"NA (NA)"`, `"NA, NA"`, etc.) to `"---"`. It runs automatically inside `extras()`, but you can also use it on its own. The `symbol` parameter controls the replacement text (default `"---"`). You can also pass `symbol` through `extras()`.

    demo_trial <- trial |>
      mutate(
        age = if_else(trt == "Drug B", 0, age),
        marker = if_else(trt == "Drug A", NA, marker)
      ) |>
      select(trt, age, marker)

#### Without cleaning

    demo_trial |>
      tbl_summary(by = trt)

#### With clean_table()

    demo_trial |>
      tbl_summary(by = trt) |>
      clean_table()

Characteristic | Drug A N = 98 1 | Drug B N = 102 1
--- | --- | ---
age | 46 (37, 60) | 0 (0, 0)
Unknown | 7 | 0
marker | NA (NA, NA) | 0.52 (0.18, 1.21)
Unknown | 98 | 4
1 Median (Q1, Q3)

Characteristic | Drug A N = 98 1 | Drug B N = 102 1
--- | --- | ---
age | 46 (37, 60) | —
Unknown | 7 | 0
marker | — | 0.52 (0.18, 1.21)
Unknown | 98 | 4
1 Median (Q1, Q3)

## Automatic Labeling

`add_auto_labels()` applies human-readable variable labels from a dictionary. Manual labels set in `tbl_summary()` always take priority.

    dictionary <- tibble::tribble(
      ~variable,    ~description,
      "trt",        "Chemotherapy Treatment",
      "age",        "Age at Enrollment (years)",
      "marker",     "Marker Level (ng/mL)",
      "stage",      "T Stage",
      "grade",      "Tumor Grade"
    )

    trial |>
      tbl_summary(by = trt, include = c(age, grade, marker)) |>
      add_auto_labels(dictionary = dictionary) |>
      extras()

| Overall N = 200 1 | Drug A N = 98 1 | Drug B N = 102 1 | p-value 2
--- | --- | --- | --- | ---
Age | 47 (38, 57) | 46 (37, 60) | 48 (39, 56) | 0.718
Unknown | 11 | 7 | 4 |
Grade |  |  |  | 0.871
I | 68 (34%) | 35 (36%) | 33 (32%) |
II | 68 (34%) | 32 (33%) | 36 (35%) |
III | 64 (32%) | 31 (32%) | 33 (32%) |
Marker Level (ng/mL) | 0.64 (0.22, 1.41) | 0.84 (0.23, 1.60) | 0.52 (0.18, 1.21) | 0.085
Unknown | 10 | 6 | 4 |
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test

For more on label priority, pre-labeled data, and auto-discovery, see `vignette("labeling")`.

## Pipeline Order

When combining with group headers and styling, order matters:

    tbl_summary(by = ...) |>
      extras() |> # always first
      add_variable_group_header() |> # after extras()
      add_group_styling() |> # format group headers
      add_group_colors() # must be last (converts to gt)

`add_variable_group_header()` must come after `extras()`, and `add_group_colors()` must be last since it converts the table to gt.

## Other Vignettes

- `vignette("labeling")` – dictionary-based labeling
- `vignette("themes")` – JAMA compact themes for `{gtsummary}` and `{gt}` for gtsummary and gt tables
- `vignette("styling")` – group headers, formatting, and background colors
- `vignette("options")` – .Rprofile options for automatic labeling