## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  eval = TRUE,
  comment = "#>"
)

## ----setup, message = FALSE---------------------------------------------------
library(gtsummary); library(gt); library(survival)
library(dplyr); library(stringr); library(purrr); library(forcats)

## -----------------------------------------------------------------------------
trial[c("trt", "age", "grade")] %>%
  tbl_summary(by = trt, missing = "no") %>%
  modify_header(stat_by = md("**{level}** N =  {n} ({style_percent(p)}%)")) %>%
  add_n() %>%
  bold_labels() %>%
  as_gt() %>%
  tab_spanner(columns = starts_with("stat_"), md("**Chemotherapy Treatment**"))

## -----------------------------------------------------------------------------
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()

## -----------------------------------------------------------------------------
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")
  )  

## -----------------------------------------------------------------------------
# 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)) %>%
  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)) %>%
  # adding spanning headers for summary stats and pvalues
  tab_spanner(label = md("**Tumor Grade**"), columns = starts_with("stat_")) %>%
  tab_spanner(label = md("**p-value**"), columns = starts_with("p.value")) 

## -----------------------------------------------------------------------------
trial[c("response", "age", "grade")] %>%
  tbl_uvregression(
    method = glm,
    y = response, 
    method.args = list(family = binomial),
    exponentiate = TRUE
  ) %>%
  add_nevent()

## -----------------------------------------------------------------------------
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**")
)

## -----------------------------------------------------------------------------
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 = md("**Event N**"))

tbl_merge(list(gt_eventn, gt_model)) %>%
  bold_labels() %>%
  italicize_levels() %>%
  as_gt(include = -tab_spanner)

## -----------------------------------------------------------------------------
tbl_reg <-
  trial[c("age", "marker", "trt")] %>%
  tbl_uvregression(
    method = lm,
    x = trt,
    show_single_row = "trt",
    hide_n = TRUE
  ) %>%
  modify_header(
    label = md("**Model Outcome**"),
    estimate = md("**Treatment Coef.**")
  ) 

tbl_reg %>%
  as_gt() %>%
  tab_footnote(
    footnote = "Values larger than 0 indicate larger values in the Drug group.", 
    locations = cells_column_labels(columns = vars(estimate))
  )

## -----------------------------------------------------------------------------
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 = md("**Difference**")) %>%
  as_gt(include = -tab_spanner)

## -----------------------------------------------------------------------------
my_tidy <- function(x, exponentiate =  FALSE, conf.level = 0.95, ...) {
  dplyr::bind_cols(
    broom::tidy(x, exponentiate = exponentiate, conf.int = FALSE),
    broom::confint_tidy(x, func = stats::confint.default, conf.level = conf.level)
  )
}

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

