Table Gallery

Gallery showing various tables possible with the {gtsummary} package. If you have created an interesting table using {gtsummary}, please submit it to the gallery via a pull request to the GitHub repository.

Summary Tables

Add a spanning header over the group columns for increased clarity, reporting the number of non-missing observations, and modifying column headers.

trial[c("trt", "age", "grade")] %>%
  tbl_summary(
    by = trt, 
    missing = "no",
    statistic = all_continuous() ~ "{median} ({p25}, {p75}) [N = {N_nonmiss}]"
  ) %>%
  modify_header(stat_by = md("**{level}**<br>N =  {n} ({style_percent(p)}%)")) %>%
  add_n() %>%
  bold_labels() %>%
  modify_spanning_header(starts_with("stat_") ~ "**Chemotherapy Treatment**")

Characteristic	N	Chemotherapy Treatment
Characteristic	N	Drug A N = 98 (49%)¹	Drug B N = 102 (51%)¹
Age, yrs	189	46 (37, 59) [N = 91]	48 (39, 56) [N = 98]
Grade	200
I		35 (36%)	33 (32%)
II		32 (33%)	36 (35%)
III		31 (32%)	33 (32%)
¹ Statistics presented: median (IQR) [N = N]; n (%)

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

trial[!is.na(trial$response), c("response", "age", "grade")] %>%
  mutate(response = factor(response, labels = c("No Tumor Response", "Tumor Responded"))) %>%
  tbl_summary(
    by = response, 
    missing = "no",
    label = list(age ~ "Patient Age", grade ~ "Tumor Grade")
  ) %>%
  add_p(pvalue_fun = partial(style_pvalue, digits = 2)) %>%
  add_q()
#> Adjusting p-values with
#> `stats::p.adjust(x$table_body$p.value, method = "fdr")`

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 Statistics presented: median (IQR); n (%) ² Statistical tests performed: Wilcoxon rank-sum test; chi-square test of independence ³ False discovery rate correction for multiple testing

Include missing tumor response as column using fct_explicit_na().

trial[c("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 Statistics presented: median (IQR); n (%)

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

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

# table comparing grade I and II
t1 <- trial %>%
  select(grade, age, response) %>%
  filter(grade %in% c("I", "II")) %>%
  tbl_summary(by = grade, missing = "no") %>%
  add_p() %>%
  modify_header(p.value ~ md("**I vs. II**"))

# table comparing grade I and II
t2 <- trial %>%
  select(grade, age, response) %>%
  filter(grade %in% c("I", "III")) %>%
  tbl_summary(by = grade, missing = "no") %>%
  add_p()  %>%
  modify_header(p.value ~ md("**I vs. III**"))

# merging the 3 tables together, and adding additional gt formatting
tbl_merge(list(t0, t1, t2)) %>%
  modify_spanning_header(
    list(
      starts_with("stat_") ~ "**Tumor Grade**",
      starts_with("p.value") ~ "**p-value**"
    )
  ) %>%
  as_gt(include = -tab_spanner) %>%
  # hiding repeated summary columns
  cols_hide(columns = vars(stat_1_2, stat_2_2, stat_3_2, stat_1_3, stat_2_3, stat_3_3))

Characteristic	I¹	II¹	III¹	I vs. II²	I vs. III²
Age, yrs	47 (37, 56)	48 (37, 57)	47 (38, 58)	0.7	0.5
Tumor Response	21 (31%)	19 (30%)	21 (33%)	>0.9	0.9
¹ Statistics presented: median (IQR); n (%) ² Statistical tests performed: Wilcoxon rank-sum test; Fisher's exact test

Characteristic

I¹

II¹

III¹

I vs. II²

I vs. III²

Age, yrs

47 (37, 56)

48 (37, 57)

47 (38, 58)

0.7

0.5

Tumor Response

21 (31%)

19 (30%)

21 (33%)

>0.9

0.9

¹ Statistics presented: median (IQR); n (%)

² Statistical tests performed: Wilcoxon rank-sum test; Fisher's exact test

Add additional statistics as additional columns.

# define function for lower and upper bounds of the mean CI
ll <- function(x) t.test(x)$conf.int[1]
ul <- function(x) t.test(x)$conf.int[2]

t1 <-
  trial %>%
  select(age, marker) %>%
  tbl_summary(statistic = all_continuous() ~ "{mean} ({sd})", missing = "no") %>%
  modify_header(stat_0 ~ "**Mean (SD)**")

t2 <-
  trial %>%
  select(age, marker) %>%
  tbl_summary(statistic = all_continuous() ~ "{ll}, {ul}", missing = "no") %>%
  modify_header(stat_0 ~ "**95% CI for Mean**")

tbl_merge(list(t1, t2)) %>%
  modify_footnote(everything() ~ NA_character_) %>%
  modify_spanning_header(everything() ~ NA_character_)

Characteristic	Mean (SD)	95% CI for Mean
Age, yrs	47 (14)	45, 49
Marker Level, ng/mL	0.92 (0.86)	0.79, 1.04

Regression Tables

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

trial[c("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, yrs	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.

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

tbl_merge(
  list(gt_t1, gt_r1, gt_r2),
  tab_spanner = c("**Summary Statistics**", "**Tumor Response**", "**Time to Death**")
)

Characteristic	Summary Statistics		Tumor Response			Time to Death
Characteristic	N	N = 200¹	OR²	95% CI²	p-value	HR²	95% CI²	p-value
Age, yrs	189	47 (38, 57)	1.02	1.00, 1.04	0.095	1.01	0.99, 1.02	0.4
Chemotherapy Treatment	200
Drug A		98 (49%)	—	—		—	—
Drug B		102 (51%)	1.13	0.60, 2.13	0.7	1.31	0.89, 1.93	0.2
¹ Statistics presented: median (IQR); n (%) ² OR = Odds Ratio, CI = Confidence Interval, HR = Hazard Ratio

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

gt_model <-
  trial[c("ttdeath", "death", "stage", "grade")] %>%
  tbl_uvregression(
    method = coxph,
    y = Surv(ttdeath, death), 
    exponentiate = TRUE,
    hide_n = TRUE
  )

gt_eventn <-
  trial %>%
  filter(death ==  1) %>%
  select(stage, grade) %>%
  tbl_summary(
    statistic = all_categorical() ~ "{n}",
    label = list(stage ~ "T Stage", grade ~ "Grade")
  ) %>%
  modify_header(stat_0 ~ "**Event N**") %>%
  modify_footnote(everything() ~ NA_character_)

tbl_merge(list(gt_eventn, gt_model)) %>%
  bold_labels() %>%
  italicize_levels() %>%
  modify_spanning_header(everything() ~ NA_character_)

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.

tbl_reg <-
  trial[c("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.**"
  )) 

tbl_reg %>%
  modify_footnote(estimate ~ "Values larger than 0 indicate larger values in the Drug group.")

Model Outcome	Treatment Coef.¹	95% CI²	p-value
Age, yrs	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 group. ² CI = Confidence Interval

Model Outcome

Treatment Coef.¹

95% CI²

p-value

Age, yrs

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 group.

² CI = Confidence Interval

Add descriptive statistics by treatment group to the table above to produce a table often reported two group comparisons.

gt_sum <- 
  trial[c("age", "marker", "trt")] %>%
  mutate(trt = fct_rev(trt)) %>%
  tbl_summary(by = trt, 
              statistic = all_continuous() ~ "{mean} ({sd})",
              missing = "no") %>%
  add_n() %>%
  modify_header(stat_by = md("**{level}**"))


tbl_merge(list(gt_sum, tbl_reg))  %>%
  modify_header(estimate_2 ~ "**Difference**") %>%
  modify_spanning_header(everything() ~ NA_character_)

Characteristic	N	Drug B¹	Drug A¹	Difference	95% CI²	p-value
Age, yrs	189	47 (14)	47 (15)	0.44	-3.7, 4.6	0.8
Marker Level, ng/mL	190	0.82 (0.83)	1.02 (0.89)	-0.20	-0.44, 0.05	0.12
¹ Statistics presented: mean (SD) ² CI = Confidence Interval

Characteristic

Drug B¹

Drug A¹

Difference

95% CI²

p-value

Age, yrs

189

47 (14)

47 (15)

0.44

-3.7, 4.6

0.8

Marker Level, ng/mL

190

0.82 (0.83)

1.02 (0.89)

-0.20

-0.44, 0.05

0.12

¹ Statistics presented: mean (SD)

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