introspecting_tables.Rmd

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

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

The packages used in this vignette are `rtables` and `dplyr`:

```{r, message=FALSE}
library(rtables)
library(dplyr)
```

## Introduction

First, let's set up a simple table.

```{r}
lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD") %>%
  split_cols_by("STRATA2") %>%
  split_rows_by("STRATA1") %>%
  add_overall_col("All") %>%
  summarize_row_groups() %>%
  analyze("AGE", afun = max, format = "xx.x")

tbl <- build_table(lyt, ex_adsl)
tbl
```

## Getting Started

We can get basic table dimensions, the number of rows, and the number of columns with the following code:

```{r}
dim(tbl)
nrow(tbl)
ncol(tbl)
```

## Detailed Table Structure

The `table_structure()` function prints a summary of a table's row
structure at one of two levels of detail. By default, it summarizes
the structure at the subtable level.

```{r}
table_structure(tbl)
```

When the `detail` argument is set to `"row"`, however, it provides a
more detailed row-level summary which acts as a useful alternative to
how we might normally use the `str()` function to interrogate compound
nested lists.

```{r}
table_structure(tbl, detail = "row")
```

The `make_row_df()` and `make_col_df()` functions each create a `data.frame` with a variety of information about 
the table's structure. Most useful for introspection purposes are the `label`, `name`, `abs_rownumber`, `path` and 
`node_class` columns (the remainder of the information in the returned `data.frame` is used for pagination)

```{r}
make_row_df(tbl)[, c("label", "name", "abs_rownumber", "path", "node_class")]
```

There is also a wrapper function, `row_paths()` available for `make_row_df` to display only the row path structure:

```{r}
row_paths(tbl)
```

By default `make_row_df()` summarizes only visible rows, but setting `visible_only` to `FALSE` gives us a structural 
summary of the table with the full hierarchy of subtables, including those that are not represented directly by any 
visible rows:

```{r}
make_row_df(tbl, visible_only = FALSE)[, c("label", "name", "abs_rownumber", "path", "node_class")]
```

`make_col_df()` similarly accepts `visible_only`, though here the meaning is slightly different, indicating whether 
only *leaf* columns should be summarized (defaults to `TRUE`) or whether higher level groups of columns - analogous to 
subtables in row space - should be summarized as well.

```{r}
make_col_df(tbl)[, c("label", "name", "abs_pos", "path", "leaf_indices")]
```

```{r}
make_col_df(tbl, visible_only = FALSE)[, c("label", "name", "abs_pos", "path", "leaf_indices")]
```

Similarly, there is wrapper function `col_paths()` available, which displays only the column structure:

```{r}
col_paths(tbl)
```

The `row_paths_summary()` and `col_paths_summary()` functions wrap the respective `make_*_df` functions, printing the 
`name`, `node_class`, and `path` information (in the row case), or the `label` and `path` information (in the column 
case), indented to illustrate table structure:

```{r}
row_paths_summary(tbl)
```

```{r}
col_paths_summary(tbl)
```

## Insights on Value Format Structure

We can gain insight into the value formatting structure of a table using `table_shell()`, which returns a table with the same output as `print()` but with the cell values replaced by their underlying format strings (e.g. instead of `40.0`, `xx.x` is displayed, and so on). This is useful for understanding the structure of the table, and for debugging purposes. Another useful tool is the `value_formats()` function which instead of a table returns a matrix of the format strings for each cell value in the table.

See below the printout for the above examples:

```{r}
table_shell(tbl)
```

```{r}
value_formats(tbl)
```

## Applications

Knowing the structure of an `rtable` object is helpful for retrieving specific values from the table. 
For examples, see the [Path Based Cell Value Accessing](https://insightsengineering.github.io/rtables/latest-tag/articles/subsetting_tables.html#path-based-cell-value-accessing)
section of the Subsetting and Manipulating Table Contents vignette.

Understanding table structure is also important for post-processing processes such as sorting and pruning. More details 
on this are covered in the [Pruning and Sorting Tables vignette](https://insightsengineering.github.io/rtables/latest-tag/articles/sorting_pruning.html)
vignette.

## Summary

In this vignette you have learned a number of utility functions that are available for examining the underlying 
structure of `rtable` objects.