getting-started.Rmd

---
title: "Getting Started"
---

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

Suppose you need to create the table below, and need an ARD representation of the results to get started.
Here, we will review an examples for creating a basic demographics table.

To get started, load the **{cards}** package.

```{r setup}
library(cards)
```

### Demographics

```{r, echo=FALSE, message=FALSE}
gtsummary::theme_gtsummary_compact()
ADSL |>
  dplyr::select(ARM, AGE, AGEGR1, SEX) |>
  dplyr::mutate(
    ARM = factor(ARM, levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose")),
    AGEGR1 = factor(AGEGR1, levels = c("<65", "65-80", ">80"), labels = c("&lt;65", "65-80", ">80"))
  ) |>
  gtsummary::tbl_summary(
    by = ARM,
    type = AGE ~ "continuous2",
    statistic = AGE ~ c("{median} ({p25}, {p75})", "{mean} ({sd})", "{min} - {max}"),
    value = SEX ~ "F",
    label = list(SEX = "Female", AGEGR1 = "Age Group"),
    digits = gtsummary::all_categorical() ~ 0L
  ) |>
  gtsummary::add_stat_label() |>
  gtsummary::bold_labels() |>
  gtsummary::modify_header(gtsummary::all_stat_cols() ~ "**{level}**  \nN = {n}") |>
  gtsummary::modify_column_alignment(columns = -label, "right") |>
  gtsummary::as_gt() |>
  gt::cols_width(gtsummary::all_stat_cols() ~ gt::pct(23)) |>
  gt::opt_table_font(stack = "monospace-code")
```

The table above has three types of data summaries: a **continuous** variable summary for `AGE`, a **categorical** variable summary for `AGEGR1`, and a **dichotomous** variable summary for `SEX`.

#### Continuous Summaries

To get a continuous variable summary, we will use the `ard_continuous()` function from the **{cards}** package.

```{r}
df_continuous_ard <-
  ard_continuous(
    ADSL,
    by = ARM,
    variables = AGE,
    statistic = ~ continuous_summary_fns(c("median", "p25", "p75", "mean", "sd", "min", "max"))
  )
df_continuous_ard |> head(5)
```

#### Categorical Summaries

To get the categorical variable summary, we will use the `ard_categorical()` function.

```{r}
df_categorical_ard <-
  ard_categorical(
    ADSL,
    by = ARM,
    variables = AGEGR1
  )
df_categorical_ard |> head(5)
```

#### Dichotomous Summaries

To get the dichotomous variable summary, we will use `ard_dichotomous()`.
In this case, we want to show the Female (`"F"`) level of the `SEX` variable and specify this with the `values` argument.

```{r}
df_dichotomous_ard <-
  ard_dichotomous(
    ADSL,
    by = ARM,
    variables = SEX,
    value = list(SEX = "F")
  )
df_dichotomous_ard |> head(5)
```

#### Combine Results

As a last step, you can combine all of these objects into a single object using `bind_ard()`, which is similar to `dplyr::bind_rows()` and includes additional structural checks for our results.

```{r}
bind_ard(
  df_continuous_ard,
  df_categorical_ard,
  df_dichotomous_ard
)
```

#### Shortcut

The `ard_stack()` function provides a shortcut to perform the calculations above in a single step.

In the example below, the `data` and `by` arguments are passed to each subsequent `ard_*()` function call.
Moreover, we will also be returned the univariate tabulation of the `by` variable, which would be used to add counts to the header row of the table.

```{r}
ard_stack(
  data = ADSL,
  .by = ARM,
  ard_continuous(
    variables = AGE,
    statistic = ~ continuous_summary_fns(c("median", "p25", "p75", "mean", "sd", "min", "max"))
  ),
  ard_categorical(variables = AGEGR1),
  ard_dichotomous(variables = SEX, value = list(SEX = "F"))
)
```

### Adverse Events

_Example Coming Soon!_