---
title: "Comparing Against Baselines or Control"
author: "Gabriel Becker and Adrian Waddell"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Comparing Against Baselines or Control}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---



```{r, echo=FALSE}
knitr::opts_chunk$set(comment = "")
```

```{css, echo=FALSE}

.sourcecode {
  background-color:lightblue;
}
.reveal .r code {
    white-space: pre;
}
```


## Introduction


Often the data from one column is considered the reference/baseline/comparison group and is compared to the data from the other columns.

For example, lets calculate the average age:

```{r}
library(rtables)

basic_table() %>%
  split_cols_by("ARM") %>%
  analyze("AGE") %>%
  build_table(DM)
```

and then the difference of the average `AGE` between the arms to the placebo arm:

```{r}
basic_table() %>%
  split_cols_by("ARM", ref_group = "B: Placebo") %>%
  analyze("AGE", afun = function(x, .ref_group) {
    in_rows(
      "Difference of Averages" = rcell(mean(x) - mean(.ref_group), format = "xx.xx")
    )
  }) %>%
  build_table(DM)
```

Note that the column order has changed and the reference group is displayed in the first column.

In cases where we want cells to be blank in the reference column, (e.g., "B: Placebo") we use `non_ref_rcell` instead
of `rcell`, and pass `.in_ref_col` as the second argument:

```{r}
basic_table() %>%
  split_cols_by("ARM", ref_group = "B: Placebo") %>%
  analyze("AGE", afun = function(x, .ref_group, .in_ref_col){
      in_rows("Difference of Averages" = non_ref_rcell(mean(x) - mean(.ref_group),
                                                       is_ref = .in_ref_col,
                                                       format = "xx.xx"))
  }) %>%
  build_table(DM)

basic_table() %>%
  split_cols_by("ARM", ref_group = "B: Placebo") %>%
  analyze("AGE", afun = function(x, .ref_group, .in_ref_col){
      in_rows(
          "Difference of Averages" = non_ref_rcell(mean(x) - mean(.ref_group),
                                                   is_ref = .in_ref_col,
                                                   format = "xx.xx"),
         "another row" = non_ref_rcell("aaa", .in_ref_col)
      )
  }) %>%
  build_table(DM)

```

You can see which arguments are available for `afun` in the manual fro `analyze`.

## Row Splitting

When adding row-splitting the reference data might be one represented by the column with or without row splitting. For example:

```{r}
basic_table() %>%
  split_cols_by("ARM", ref_group = "B: Placebo") %>%
  add_colcounts() %>%
  split_rows_by("SEX", split_fun = drop_split_levels) %>%
  analyze("AGE", afun = function(x, .ref_group, .ref_full, .in_ref_col){
    in_rows(
      "is reference (.in_ref_col)" = rcell(.in_ref_col),
      "ref cell N (.ref_group)" = rcell(length(.ref_group)),
      "ref column N (.ref_full)" = rcell(length(.ref_full))
    )
  }) %>%
  build_table(subset(DM, SEX %in% c("M", "F")))
```

so the data assigned to `.ref_full` is the full data of the reference column where the data assigned to `.ref_group` respects the subsetting defined by row-splitting and hence is from the same subset as the argument `x` or `df` to `afun`.


