# FAQ + Gallery

FAQ and Gallery showing various tables possible with the {gtsummary} package.

    library(gtsummary)
    library(gt)
    library(survival)
    library(dplyr)
    library(stringr)
    library(purrr)
    library(forcats)
    library(tidyr)

## Frequently Asked Questions

### Data Summary Tables

Headers, Labels and Formatting

1. How do I modify column headers in a table?

2. How do I change the value of the by levels?

3. How do I added a spanning header row to a table?

4. How do I change variable labels?

5. How do I italicize or bold labels in a table?

6. How do I italicize or bold levels in a table?

7. How do I reduce font size and cell padding in a table?

Adding and Modifying Statistics

8. How do I add the number of observations to a summary table?

9. How do I show additional summary statistics as a new row?

10. How do I include a column for missing values of a grouping variable?

11. How do I add a column with the confidence around the mean?

12. How do I add a column for the difference between groups?

13. How do I summarize a continuous variable by one, two (or more) categorical variables?

14. How do I stratify a summary table by more than one variable?

15. How do I change the p-values format?

16. How do I add a p-value for each group compared to a single reference group?

17. How do I add a correction for multiple testing?

18. How do I combine a summary table with a regression table?

Statistical Tests

19. How do I do a paired t-test or McNemar’s test?

### Regression Tables

Headers, Labels and Formatting

1. How do I change the text in a footnote?

2. How do I add significance stars for low p-values?

3. How do I reduce font size and cell padding in a table?

Creating and Combining Tables

4. How do I combine the results of two related regression models into one table?

5. How do I combine a regression table with a summary table?

6. How do I create a regression table for multiple models with the same covariate(s) and different outcomes?

Adding and Modifying Statistics

7. How do I add total event numbers to a regression table?

8. How do I add event numbers for each level of a categorical covariate?

9. How do I format a Wald confidence interval?

## Summary Tables

Add a spanning header over the group columns for increased clarity, and modify column headers. Using `bold_labels()` formats the labels as bold, but labels can also be italicized using `italicize_labels()`, or combined to format with both bold and italics.

    trial %>%
      select(trt, age, grade) %>%
      tbl_summary(
        by = trt,
        missing = "no",
        statistic = all_continuous() ~ "{median} ({p25}, {p75})"
      ) %>%
      modify_header(all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p)}%)") %>%
      add_n() %>%
      bold_labels() %>%
      modify_spanning_header(all_stat_cols() ~ "**Chemotherapy Treatment**")

Characteristic | N | Chemotherapy Treatment
--- | --- | ---
Drug A N = 98 (49%)1 | Drug B N = 102 (51%)1
Age | 189 | 46 (37, 59) | 48 (39, 56)
Grade | 200 |  |
I |  | 35 (36%) | 33 (32%)
II |  | 32 (33%) | 36 (35%)
III |  | 31 (32%) | 33 (32%)
1 Median (IQR); n (%)

Show continuous summary statistics on multiple lines. Levels are italicized here using the `italicize_levels()` function, but the `bold_levels()` function can be used instead to create bold text, or both functions can be used together to get text that is both bold and in italics.

    trial %>%
      select(trt, age, marker) %>%
      tbl_summary(
        by = trt,
        type = all_continuous() ~ "continuous2",
        statistic = all_continuous() ~ c(
          "{N_nonmiss}",
          "{mean} ({sd})",
          "{median} ({p25}, {p75})",
          "{min}, {max}"
        ),
        missing = "no"
      ) %>%
      italicize_levels()

Characteristic | Drug A, N = 98 | Drug B, N = 102
--- | --- | ---
Age |  |
N | 91 | 98
Mean (SD) | 47 (15) | 47 (14)
Median (IQR) | 46 (37, 59) | 48 (39, 56)
Range | 6, 78 | 9, 83
Marker Level (ng/mL) |  |
N | 92 | 98
Mean (SD) | 1.02 (0.89) | 0.82 (0.83)
Median (IQR) | 0.84 (0.24, 1.57) | 0.52 (0.19, 1.20)
Range | 0.00, 3.87 | 0.01, 3.64

Modify the function that formats the p-values, change variable labels, updating tumor response header, and add a correction for multiple testing.

    trial %>%
      select(response, age, grade) %>%
      mutate(response = factor(response, labels = c("No Tumor Response", "Tumor Responded"))) %>%
      tbl_summary(
        by = response,
        missing = "no",
        label = list(age ~ "Patient Age", grade ~ "Tumor Grade")
      ) %>%
      add_p(pvalue_fun = ~ style_pvalue(.x, digits = 2)) %>%
      add_q()

Characteristic | No Tumor Response, N = 1321 | Tumor Responded, N = 611 | p-value2 | q-value3
--- | --- | --- | --- | ---
Patient Age | 46 (36, 55) | 49 (43, 59) | 0.091 | 0.18
Tumor Grade |  |  | 0.93 | 0.93
I | 46 (35%) | 21 (34%) |  |
II | 44 (33%) | 19 (31%) |  |
III | 42 (32%) | 21 (34%) |  |
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test
3 False discovery rate correction for multiple testing

Include missing tumor response as column using `fct_explicit_na()`.

    trial %>%
      select(response, age, grade) %>%
      mutate(
        response = factor(response, labels = c("No Tumor Response", "Tumor Responded")) %>%
          fct_explicit_na(na_level = "Missing Response Status")
      ) %>%
      tbl_summary(
        by = response,
        label = list(age ~ "Patient Age", grade ~ "Tumor Grade")
      )

Characteristic | No Tumor Response, N = 1321 | Tumor Responded, N = 611 | Missing Response Status, N = 71
--- | --- | --- | ---
Patient Age | 46 (36, 55) | 49 (43, 59) | 52 (44, 57)
Unknown | 7 | 3 | 1
Tumor Grade |  |  |
I | 46 (35%) | 21 (34%) | 1 (14%)
II | 44 (33%) | 19 (31%) | 5 (71%)
III | 42 (32%) | 21 (34%) | 1 (14%)
1 Median (IQR); n (%)

Report treatment differences between two groups. This is often needed in randomized trials. In this example, we report the difference in tumor response and marker level between two chemotherapy treatments.

    trial %>%
      select(response, marker, trt) %>%
      tbl_summary(
        by = trt,
        statistic = list(
          all_continuous() ~ "{mean} ({sd})",
          all_categorical() ~ "{p}%"
        ),
        missing = "no"
      ) %>%
      add_difference() %>%
      add_n() %>%
      modify_header(all_stat_cols() ~ "**{level}**") %>%
      modify_footnote(all_stat_cols() ~ NA)

Characteristic | N | Drug A | Drug B | Difference1 | 95% CI1,2 | p-value1
--- | --- | --- | --- | --- | --- | ---
Tumor Response | 193 | 29% | 34% | -4.2% | -18%, 9.9% | 0.6
Marker Level (ng/mL) | 190 | 1.02 (0.89) | 0.82 (0.83) | 0.20 | -0.05, 0.44 | 0.12
1 Two sample test for equality of proportions; Welch Two Sample t-test
2 CI = Confidence Interval

Paired t-test and McNemar’s test. The data is expected in a long format with 2 rows per participant.

    # imagine that each patient received Drug A and Drug B (adding ID showing their paired measurements)
    trial_paired <-
      trial %>%
      select(trt, marker, response) %>%
      group_by(trt) %>%
      mutate(id = row_number()) %>%
      ungroup()

    # you must first delete incomplete pairs from the data, then you can build the table
    trial_paired %>%
      # delete missing values
      filter(complete.cases(.)) %>%
      # keep IDs with both measurements
      group_by(id) %>%
      filter(n() == 2) %>%
      ungroup() %>%
      # summarize data
      tbl_summary(by = trt, include = -id) %>%
      add_p(
        test = list(
          marker ~ "paired.t.test",
          response ~ "mcnemar.test"
        ),
        group = id
      )

Characteristic | Drug A, N = 831 | Drug B, N = 831 | p-value2
--- | --- | --- | ---
Marker Level (ng/mL) | 0.82 (0.22, 1.63) | 0.53 (0.18, 1.26) | 0.2
Tumor Response | 21 (25%) | 28 (34%) | 0.3
1 Median (IQR); n (%)
2 Paired t-test; McNemar’s Chi-squared test with continuity correction

Include p-values comparing all groups to a single reference group.

    # table summarizing data with no p-values
    small_trial <- trial %>% select(grade, age, response)
    t0 <- small_trial %>%
      tbl_summary(by = grade, missing = "no") %>%
      modify_header(all_stat_cols() ~ "**{level}**")

    # table comparing grade I and II
    t1 <- small_trial %>%
      filter(grade %in% c("I", "II")) %>%
      tbl_summary(by = grade, missing = "no") %>%
      add_p() %>%
      modify_header(p.value ~ md("**I vs. II**")) %>%
      # hide summary stat columns
      modify_column_hide(all_stat_cols())

    # table comparing grade I and II
    t2 <- small_trial %>%
      filter(grade %in% c("I", "III")) %>%
      tbl_summary(by = grade, missing = "no") %>%
      add_p() %>%
      modify_header(p.value ~ md("**I vs. III**")) %>%
      # hide summary stat columns
      modify_column_hide(all_stat_cols())

    # merging the 3 tables together, and adding additional gt formatting
    tbl_merge(list(t0, t1, t2)) %>%
      modify_spanning_header(
        list(
          all_stat_cols() ~ "**Tumor Grade**",
          starts_with("p.value") ~ "**p-values**"
        )
      )

Characteristic | Tumor Grade | p-values
--- | --- | ---
I1 | II1 | III1 | I vs. II2 | I vs. III2
Age | 47 (37, 56) | 49 (37, 57) | 47 (38, 58) | 0.7 | 0.5
Tumor Response | 21 (31%) | 19 (30%) | 21 (33%) | >0.9 | 0.9
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Fisher’s exact test

Add 95% confidence interval around the mean as an additional column

    trial %>%
      select(age, marker) %>%
      tbl_summary(statistic = all_continuous() ~ "{mean} ({sd})", missing = "no") %>%
      modify_header(stat_0 ~ "**Mean (SD)**") %>%
      add_ci()

Characteristic | Mean (SD)1 | 95% CI2
--- | --- | ---
Age | 47 (14) | 45, 49
Marker Level (ng/mL) | 0.92 (0.86) | 0.79, 1.0
1 Mean (SD)
2 CI = Confidence Interval

It’s often needed to summarize a continuous variable by one, two, or more categorical variables. The example below shows a table summarizing a continuous variable by two categorical variables. To summarize by more than two categorical variables, use `tbl_continuous` in conjunction with `tbl_strata` (see an example of `tbl_strata` here).

    trial %>%
      select(trt, grade, marker) %>%
      tbl_continuous(variable = marker, by = trt) %>%
      modify_spanning_header(all_stat_cols() ~ "**Treatment Assignment**")

Characteristic | Treatment Assignment
--- | ---
Drug A, N = 981 | Drug B, N = 1021
Grade |  |
I | 0.96 (0.24, 1.70) | 1.05 (0.29, 1.49)
II | 0.66 (0.31, 1.23) | 0.21 (0.10, 0.94)
III | 0.84 (0.17, 1.91) | 0.58 (0.35, 1.36)
1 Marker Level (ng/mL): Median (IQR)

Build a summary table stratified by more than one variable.

    trial %>%
      select(trt, grade, age, stage) %>%
      mutate(grade = paste("Grade", grade)) %>%
      tbl_strata(
        strata = grade,
        ~ .x %>%
          tbl_summary(by = trt, missing = "no") %>%
          modify_header(all_stat_cols() ~ "**{level}**")
      )

Characteristic | Grade I | Grade II | Grade III
--- | --- | --- | ---
Drug A1 | Drug B1 | Drug A1 | Drug B1 | Drug A1 | Drug B1
Age | 46 (36, 60) | 48 (42, 55) | 45 (31, 55) | 51 (43, 57) | 52 (42, 60) | 45 (36, 52)
T Stage |  |  |  |  |  |
T1 | 8 (23%) | 9 (27%) | 14 (44%) | 9 (25%) | 6 (19%) | 7 (21%)
T2 | 8 (23%) | 10 (30%) | 8 (25%) | 9 (25%) | 9 (29%) | 10 (30%)
T3 | 11 (31%) | 7 (21%) | 5 (16%) | 6 (17%) | 6 (19%) | 8 (24%)
T4 | 8 (23%) | 7 (21%) | 5 (16%) | 12 (33%) | 10 (32%) | 8 (24%)
1 Median (IQR); n (%)

## Regression Tables

Include number of observations and the number of events in a univariate regression table.

    trial %>%
      select(response, age, grade) %>%
      tbl_uvregression(
        method = glm,
        y = response,
        method.args = list(family = binomial),
        exponentiate = TRUE
      ) %>%
      add_nevent()

Characteristic | N | Event N | OR1 | 95% CI1 | p-value
--- | --- | --- | --- | --- | ---
Age | 183 | 58 | 1.02 | 1.00, 1.04 | 0.10
Grade | 193 | 61 |  |  |
I |  |  | — | — |
II |  |  | 0.95 | 0.45, 2.00 | 0.9
III |  |  | 1.10 | 0.52, 2.29 | 0.8
1 OR = Odds Ratio, CI = Confidence Interval

Include two related models side-by-side with descriptive statistics. We also use the compact table theme that reduces cell padding and font size.

    gt_r1 <- glm(response ~ trt + grade, trial, family = binomial) %>%
      tbl_regression(exponentiate = TRUE)
    gt_r2 <- coxph(Surv(ttdeath, death) ~ trt + grade, trial) %>%
      tbl_regression(exponentiate = TRUE)
    gt_t1 <- trial[c("trt", "grade")] %>%
      tbl_summary(missing = "no") %>%
      add_n() %>%
      modify_header(stat_0 ~ "**n (%)**") %>%
      modify_footnote(stat_0 ~ NA_character_)

    theme_gtsummary_compact()
    #> Setting theme `Compact`
    tbl_merge(
      list(gt_t1, gt_r1, gt_r2),
      tab_spanner = c(NA_character_, "**Tumor Response**", "**Time to Death**")
    )

Characteristic | N | n (%) | Tumor Response | Time to Death
--- | --- | --- | --- | ---
OR1 | 95% CI1 | p-value | HR1 | 95% CI1 | p-value
Chemotherapy Treatment | 200 |  |  |  |  |  |  |
Drug A |  | 98 (49%) | — | — |  | — | — |
Drug B |  | 102 (51%) | 1.21 | 0.66, 2.24 | 0.5 | 1.25 | 0.86, 1.81 | 0.2
Grade | 200 |  |  |  |  |  |  |
I |  | 68 (34%) | — | — |  | — | — |
II |  | 68 (34%) | 0.94 | 0.44, 1.98 | 0.9 | 1.28 | 0.80, 2.06 | 0.3
III |  | 64 (32%) | 1.09 | 0.52, 2.27 | 0.8 | 1.69 | 1.07, 2.66 | 0.024
1 OR = Odds Ratio, CI = Confidence Interval, HR = Hazard Ratio

Include the number of events at each level of a categorical predictor.

    trial %>%
      select(ttdeath, death, stage, grade) %>%
      tbl_uvregression(
        method = coxph,
        y = Surv(ttdeath, death),
        exponentiate = TRUE,
        hide_n = TRUE
      ) %>%
      add_nevent(location = "level")

Characteristic | Event N | HR1 | 95% CI1 | p-value
--- | --- | --- | --- | ---
T Stage |  |  |  |
T1 | 24 | — | — |
T2 | 27 | 1.18 | 0.68, 2.04 | 0.6
T3 | 22 | 1.23 | 0.69, 2.20 | 0.5
T4 | 39 | 2.48 | 1.49, 4.14 | <0.001
Grade |  |  |  |
I | 33 | — | — |
II | 36 | 1.28 | 0.80, 2.05 | 0.3
III | 43 | 1.69 | 1.07, 2.66 | 0.024
1 HR = Hazard Ratio, CI = Confidence Interval

Regression model where the covariate remains the same, and the outcome changes.

    trial %>%
      select(age, marker, trt) %>%
      tbl_uvregression(
        method = lm,
        x = trt,
        show_single_row = "trt",
        hide_n = TRUE
      ) %>%
      modify_header(list(
        label ~ "**Model Outcome**",
        estimate ~ "**Treatment Coef.**"
      )) %>%
      modify_footnote(estimate ~ "Values larger than 0 indicate larger values in the Drug B group.")

Model Outcome | Treatment Coef.1 | 95% CI2 | p-value
--- | --- | --- | ---
Age | 0.44 | -3.7, 4.6 | 0.8
Marker Level (ng/mL) | -0.20 | -0.44, 0.05 | 0.12
1 Values larger than 0 indicate larger values in the Drug B group.
2 CI = Confidence Interval

Implement a custom tidier to report Wald confidence intervals. The Wald confidence intervals are calculated using `confint.default()`.

    my_tidy <- function(x, exponentiate = FALSE, conf.level = 0.95, ...) {
      dplyr::bind_cols(
        broom::tidy(x, exponentiate = exponentiate, conf.int = FALSE),
        # calculate the confidence intervals, and save them in a tibble
        stats::confint.default(x) %>%
          tibble::as_tibble() %>%
          rlang::set_names(c("conf.low", "conf.high"))
      )
    }

    lm(age ~ grade + marker, trial) %>%
      tbl_regression(tidy_fun = my_tidy)

Characteristic | Beta | 95% CI1 | p-value
--- | --- | --- | ---
Grade |  |  |
I | — | — |
II | 0.64 | -4.6, 5.9 | 0.8
III | 2.4 | -2.8, 7.6 | 0.4
Marker Level (ng/mL) | -0.04 | -2.6, 2.5 | >0.9
1 CI = Confidence Interval

Use significance stars on estimates with low p-values.

    trial %>%
      select(ttdeath, death, stage, grade) %>%
      tbl_uvregression(
        method = coxph,
        y = Surv(ttdeath, death),
        exponentiate = TRUE,
      ) %>%
      add_significance_stars()

Characteristic | N | HR1,2 | SE2
--- | --- | --- | ---
T Stage | 200 |  |
T1 |  | — | —
T2 |  | 1.18 | 0.281
T3 |  | 1.23 | 0.295
T4 |  | 2.48*** | 0.260
Grade | 200 |  |
I |  | — | —
II |  | 1.28 | 0.241
III |  | 1.69* | 0.232
1 *p<0.05; **p<0.01; ***p<0.001
2 HR = Hazard Ratio, SE = Standard Error