# Introspecting Tables

#### Gabriel Becker and Adrian Waddell

#### 2025-11-18

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

    library(rtables)
    library(dplyr)

## Introduction

First, let’s set up a simple table.

    lyt <- basic_table() %>%
      split_cols_by("ARMCD", show_colcounts = TRUE, colcount_format = "N=xx") %>%
      split_cols_by("STRATA2", show_colcounts = TRUE) %>%
      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
    #                  ARM A                     ARM B                     ARM C
    #                  N=134                     N=134                     N=132
    #             S1           S2           S1           S2           S1           S2
    #           (N=73)       (N=61)       (N=67)       (N=67)       (N=56)       (N=76)         All
    # —————————————————————————————————————————————————————————————————————————————————————————————————
    # A       18 (24.7%)   20 (32.8%)   22 (32.8%)   22 (32.8%)   14 (25.0%)   26 (34.2%)   122 (30.5%)
    #   max      40.0         46.0         62.0         50.0         47.0         45.0         62.0
    # B       28 (38.4%)   19 (31.1%)   19 (28.4%)   26 (38.8%)   18 (32.1%)   25 (32.9%)   135 (33.8%)
    #   max      48.0         47.0         58.0         58.0         46.0         64.0         64.0
    # C       27 (37.0%)   22 (36.1%)   26 (38.8%)   19 (28.4%)   24 (42.9%)   25 (32.9%)   143 (35.8%)
    #   max      48.0         50.0         48.0         51.0         69.0         50.0         69.0

## Getting Started

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

    dim(tbl)
    # [1] 6 7
    nrow(tbl)
    # [1] 6
    ncol(tbl)
    # [1] 7

## 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.

    table_structure(tbl)
    # [TableTree] STRATA1
    #  [TableTree] A [cont: 1 x 7]
    #   [ElementaryTable] AGE (1 x 7)
    #  [TableTree] B [cont: 1 x 7]
    #   [ElementaryTable] AGE (1 x 7)
    #  [TableTree] C [cont: 1 x 7]
    #   [ElementaryTable] AGE (1 x 7)

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.

    table_structure(tbl, detail = "row") # or "subtable"
    # TableTree: [STRATA1] (STRATA1)
    #   labelrow: [STRATA1] (STRATA1) - <not visible>
    #   children:
    #     TableTree: [A] (A)
    #       labelrow: [A] (A) - <not visible>
    #       content:
    #         ElementaryTable: [A@content] ()
    #           labelrow: [] () - <not visible>
    #           children:
    #             ContentRow: [A] (A)
    #       children:
    #         ElementaryTable: [AGE] (AGE)
    #           labelrow: [AGE] (AGE) - <not visible>
    #           children:
    #             DataRow: [max] (max)
    #     TableTree: [B] (B)
    #       labelrow: [B] (B) - <not visible>
    #       content:
    #         ElementaryTable: [B@content] ()
    #           labelrow: [] () - <not visible>
    #           children:
    #             ContentRow: [B] (B)
    #       children:
    #         ElementaryTable: [AGE] (AGE)
    #           labelrow: [AGE] (AGE) - <not visible>
    #           children:
    #             DataRow: [max] (max)
    #     TableTree: [C] (C)
    #       labelrow: [C] (C) - <not visible>
    #       content:
    #         ElementaryTable: [C@content] ()
    #           labelrow: [] () - <not visible>
    #           children:
    #             ContentRow: [C] (C)
    #       children:
    #         ElementaryTable: [AGE] (AGE)
    #           labelrow: [AGE] (AGE) - <not visible>
    #           children:
    #             DataRow: [max] (max)

Similarly, for columns we can see how the tree is structured with the following call:

    coltree_structure(tbl)
    # [root] (no pos)
    #    [ARMCD] (no pos)
    #      [ARM A] (ARMCD: ARM A)
    #        [S1] (ARMCD: ARM A -> STRATA2: S1)
    #        [S2] (ARMCD: ARM A -> STRATA2: S2)
    #      [ARM B] (ARMCD: ARM B)
    #        [S1] (ARMCD: ARM B -> STRATA2: S1)
    #        [S2] (ARMCD: ARM B -> STRATA2: S2)
    #      [ARM C] (ARMCD: ARM C)
    #        [S1] (ARMCD: ARM C -> STRATA2: S1)
    #        [S2] (ARMCD: ARM C -> STRATA2: S2)
    #    [All] (no pos)
    #      [All] (All: All)

Further information about the column structure can be found in the vignette on `col_counts`.

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)

    make_row_df(tbl)[, c("label", "name", "abs_rownumber", "path", "node_class")]
    #   label name abs_rownumber         path node_class
    # 1     A    A             1 STRATA1,.... ContentRow
    # 2   max  max             2 STRATA1,....    DataRow
    # 3     B    B             3 STRATA1,.... ContentRow
    # 4   max  max             4 STRATA1,....    DataRow
    # 5     C    C             5 STRATA1,.... ContentRow
    # 6   max  max             6 STRATA1,....    DataRow

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

    row_paths(tbl)
    # [[1]]
    # [1] "STRATA1"  "A"        "@content" "A"
    #
    # [[2]]
    # [1] "STRATA1" "A"       "AGE"     "max"
    #
    # [[3]]
    # [1] "STRATA1"  "B"        "@content" "B"
    #
    # [[4]]
    # [1] "STRATA1" "B"       "AGE"     "max"
    #
    # [[5]]
    # [1] "STRATA1"  "C"        "@content" "C"
    #
    # [[6]]
    # [1] "STRATA1" "C"       "AGE"     "max"

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:

    make_row_df(tbl, visible_only = FALSE)[, c("label", "name", "abs_rownumber", "path", "node_class")]
    #    label      name abs_rownumber         path      node_class
    # 1          STRATA1            NA      STRATA1       TableTree
    # 2                A            NA   STRATA1, A       TableTree
    # 3        A@content            NA STRATA1,.... ElementaryTable
    # 4      A         A             1 STRATA1,....      ContentRow
    # 5              AGE            NA STRATA1,.... ElementaryTable
    # 6    max       max             2 STRATA1,....         DataRow
    # 7                B            NA   STRATA1, B       TableTree
    # 8        B@content            NA STRATA1,.... ElementaryTable
    # 9      B         B             3 STRATA1,....      ContentRow
    # 10             AGE            NA STRATA1,.... ElementaryTable
    # 11   max       max             4 STRATA1,....         DataRow
    # 12               C            NA   STRATA1, C       TableTree
    # 13       C@content            NA STRATA1,.... ElementaryTable
    # 14     C         C             5 STRATA1,....      ContentRow
    # 15             AGE            NA STRATA1,.... ElementaryTable
    # 16   max       max             6 STRATA1,....         DataRow

`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.

    make_col_df(tbl)[, c("label", "name", "abs_pos", "path", "leaf_indices")]
    #   label name abs_pos         path leaf_indices
    # 1    S1   S1       1 ARMCD, A....            1
    # 2    S2   S2       2 ARMCD, A....            2
    # 3    S1   S1       3 ARMCD, A....            3
    # 4    S2   S2       4 ARMCD, A....            4
    # 5    S1   S1       5 ARMCD, A....            5
    # 6    S2   S2       6 ARMCD, A....            6
    # 7   All  All       7     All, All            7

    make_col_df(tbl, visible_only = FALSE)[, c("label", "name", "abs_pos", "path", "leaf_indices")]
    #    label  name abs_pos         path leaf_indices
    # 1  ARM A ARM A      NA ARMCD, ARM A         1, 2
    # 2     S1    S1       1 ARMCD, A....            1
    # 3     S2    S2       2 ARMCD, A....            2
    # 4  ARM B ARM B      NA ARMCD, ARM B         3, 4
    # 5     S1    S1       3 ARMCD, A....            3
    # 6     S2    S2       4 ARMCD, A....            4
    # 7  ARM C ARM C      NA ARMCD, ARM C         5, 6
    # 8     S1    S1       5 ARMCD, A....            5
    # 9     S2    S2       6 ARMCD, A....            6
    # 10   All   All       7     All, All            7

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

    col_paths(tbl)
    # [[1]]
    # [1] "ARMCD"   "ARM A"   "STRATA2" "S1"
    #
    # [[2]]
    # [1] "ARMCD"   "ARM A"   "STRATA2" "S2"
    #
    # [[3]]
    # [1] "ARMCD"   "ARM B"   "STRATA2" "S1"
    #
    # [[4]]
    # [1] "ARMCD"   "ARM B"   "STRATA2" "S2"
    #
    # [[5]]
    # [1] "ARMCD"   "ARM C"   "STRATA2" "S1"
    #
    # [[6]]
    # [1] "ARMCD"   "ARM C"   "STRATA2" "S2"
    #
    # [[7]]
    # [1] "All" "All"

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:

    row_paths_summary(tbl)
    # rowname    node_class    path
    # ————————————————————————————————————————————————
    # A          ContentRow    STRATA1, A, @content, A
    #   max      DataRow       STRATA1, A, AGE, max
    # B          ContentRow    STRATA1, B, @content, B
    #   max      DataRow       STRATA1, B, AGE, max
    # C          ContentRow    STRATA1, C, @content, C
    #   max      DataRow       STRATA1, C, AGE, max

    col_paths_summary(tbl)
    # label    path
    # ——————————————————————————————————
    # ARM A    ARMCD, ARM A
    #   S1     ARMCD, ARM A, STRATA2, S1
    #   S2     ARMCD, ARM A, STRATA2, S2
    # ARM B    ARMCD, ARM B
    #   S1     ARMCD, ARM B, STRATA2, S1
    #   S2     ARMCD, ARM B, STRATA2, S2
    # ARM C    ARMCD, ARM C
    #   S1     ARMCD, ARM C, STRATA2, S1
    #   S2     ARMCD, ARM C, STRATA2, S2
    # All      All, All

## 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:

    table_shell(tbl)
    #                  ARM A                     ARM B                     ARM C
    #                  N=134                     N=134                     N=132
    #             S1           S2           S1           S2           S1           S2
    #           (N=73)       (N=61)       (N=67)       (N=67)       (N=56)       (N=76)        All
    # ————————————————————————————————————————————————————————————————————————————————————————————————
    # A       xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)
    #   max      xx.x         xx.x         xx.x         xx.x         xx.x         xx.x         xx.x
    # B       xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)
    #   max      xx.x         xx.x         xx.x         xx.x         xx.x         xx.x         xx.x
    # C       xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)   xx (xx.x%)
    #   max      xx.x         xx.x         xx.x         xx.x         xx.x         xx.x         xx.x

    value_formats(tbl)
    #     ARM A.S1     ARM A.S2     ARM B.S1     ARM B.S2     ARM C.S1
    # A   "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)"
    # max "xx.x"       "xx.x"       "xx.x"       "xx.x"       "xx.x"
    # B   "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)"
    # max "xx.x"       "xx.x"       "xx.x"       "xx.x"       "xx.x"
    # C   "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)" "xx (xx.x%)"
    # max "xx.x"       "xx.x"       "xx.x"       "xx.x"       "xx.x"
    #     ARM C.S2     All
    # A   "xx (xx.x%)" "xx (xx.x%)"
    # max "xx.x"       "xx.x"
    # B   "xx (xx.x%)" "xx (xx.x%)"
    # max "xx.x"       "xx.x"
    # C   "xx (xx.x%)" "xx (xx.x%)"
    # max "xx.x"       "xx.x"

## 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 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 vignette.

## Summary

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