---
title: "Table Gallery"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Table Gallery}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  eval = TRUE,
  warning = FALSE,
  comment = "#>"
)
```

```{r, echo = FALSE, results = 'asis'}
# we do NOT want the vignette to build on CRAN...it's taking too long
if (!identical(Sys.getenv("IN_PKGDOWN"), "true") && 
    !tolower(as.list(Sys.info())$user) %in% c("sjobergd", "currym", "whitingk", "whiting")) {
  msg <- 
    paste("View this vignette on the",
          "[package website](http://www.danieldsjoberg.com/gtsummary/articles/gallery.html).")
  cat(msg)
  knitr::knit_exit()
}
```

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](https://github.com/ddsjoberg/gtsummary).

```{r setup, message = FALSE, warning=FALSE}
library(gtsummary); library(gt); library(survival)
library(dplyr); library(stringr); library(purrr); library(forcats); library(tidyr)
```

<hr>

## Summary Tables
<a id="table-header"></a>

Add a spanning header over the group columns for increased clarity, and modify column headers.

```{r}
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**")
```

<hr>
<a id="continuous2"></a>

Show continuous summary statistics on multiple lines.

```{r}
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()
```

<hr>
<a id="modify-pvalue"></a>

Modify the function that formats the p-values, change variable labels, updating tumor response header, and add a correction for multiple testing.
```{r, message = FALSE}
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()
```

<hr>
<a id="include-missing"></a>

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

```{r}
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")
  )  
```

<hr>
<a id="add_difference"></a>

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.

```{r}
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)
```

<hr>
<a id="paired-test"></a>

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

```{r}
# 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)
```

<hr>
<a id="compare-to-ref"></a>

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

```{r}
# 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**"
    )
  )
```

<hr>
<a id="mean-ci"></a>

Add additional statistics as additional columns.

```{r}
# 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_)
```

<hr>
<a id="cross_tab_continuous"></a>

It's often needed to summarize a continuous by two categorical variables.
The code below has been wrapped into a single-line solution in 
[`bstfun::tbl_2way_summary(trial, grade, trt, marker)`](http://www.danieldsjoberg.com/bstfun/dev/reference/tbl_2way_summary.html)

```{r}
trial %>%
  # nest data within tumor grade
  select(trt, grade, marker) %>%
  arrange(grade) %>%
  nest(data = -grade) %>%
  # build tbl_summary within each grade
  rowwise() %>%
  mutate(
    tbl = 
      data %>%
      # build summary table, and use the grade level as the label
      tbl_summary(by = trt, label = list(marker = as.character(grade)), missing = "no") %>%
      modify_header(list(label ~ "**Tumor Grade**", all_stat_cols() ~ "{level}")) %>%
      list()
  ) %>%
  # stack tbl_summary tables to create final tbl
  pull(tbl) %>%
  tbl_stack() %>%
  modify_spanning_header(all_stat_cols() ~ "**Treatment Assignment**")
```

<hr>
<a id="tbl_strata"></a>

Build a summary table stratified by more than one variable.

```{r}
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}**")
  )
```

<hr>

## Regression Tables
<a id="add_nevent"></a>

Include number of observations and the number of events in a univariate regression table.
```{r}
trial %>%
  select(response, age, grade) %>%
  tbl_uvregression(
    method = glm,
    y = response, 
    method.args = list(family = binomial),
    exponentiate = TRUE
  ) %>%
  add_nevent()
```

<hr>
<a id="side-by-side"></a>

Include two related models side-by-side with descriptive statistics.
We also use the compact table theme that reduces cell padding and font size.
```{r}
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()
tbl_merge(
  list(gt_t1, gt_r1, gt_r2),
  tab_spanner = c(NA_character_, "**Tumor Response**", "**Time to Death**")
)
```

```{r, echo=FALSE}
reset_gtsummary_theme()
```

<hr>
<a id="add_nevent-levels"></a>

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

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

<hr>
<a id="tbl_uvregression-x"></a>

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

```{r}
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.")
```

<hr>
<a id="wald-ci"></a>

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

```{r}
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)
```

<hr>
<a id="add_significance_stars"></a>

Use significance stars on estimates with low p-values.

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