Author: Gabriel Becker and Adrian Waddell
Date: 2026-04-22

# Subsetting and Manipulating Table Contents

#### Gabriel Becker and Adrian Waddell

#### 2026-04-22

## Introduction

`TableTree` objects are based on a tree data structure as the name indicates. The package is written such that the user does not need to walk trees for many basic table manipulations. Walking trees will still be necessary for certain manipulation and will be the subject of a different vignette.

In this vignette we show some methods to subset tables and to extract cell values.

We will use the following table for illustrative purposes:

    library(rtables)
    library(dplyr)

    lyt <- basic_table() %>%
      split_cols_by("ARM") %>%
      split_rows_by("SEX", split_fun = drop_split_levels) %>%
      analyze(c("AGE", "STRATA1"))

    tbl <- build_table(lyt, ex_adsl %>% filter(SEX %in% c("M", "F")))
    tbl
    #             A: Drug X   B: Placebo   C: Combination
    # ———————————————————————————————————————————————————
    # F
    #   AGE
    #     Mean      32.76       34.12          35.20
    #   STRATA1
    #     A          21           24             18
    #     B          25           27             21
    #     C          33           26             27
    # M
    #   AGE
    #     Mean      35.57       37.44          35.38
    #   STRATA1
    #     A          16           19             20
    #     B          21           17             21
    #     C          14           19             19

## Traditional Subsetting and modification with `[`

The `[` and `[<-` accessor functions operate largely the same as their `data.frame` cousins:

- `[` and `[<-` both treat Tables as rectangular objects (rather than trees)
  - In particular this means label rows are treated as rows with empty cell values, rather than rows without cells
- `[` accepts both column and row absolute position, and missing arguments mean “all indexes in that dimension”
  - multiple values can be specified in both row and column position
  - negative numeric positions are supported, though like `[.data.frame` they cannot be mixed with positive ones
- `[` always returns the same class as the object being subset unless `drop = TRUE`
- `[ , drop = TRUE` returns the raw (possibly multi-element) value associated with the cell.

Known Differences from `[.data.frame` - absolute position cannot currently be used to reorder columns or rows. Note in general the result of such an ordering is unlikely to be structurally valid. To change the order of values, please read sorting and pruning vignette or relevant function (`sort_at_path()`). - `character` indices are treated as paths, not vectors of names in both `[` and `[<-`

The `[` accessor function always returns an `TableTree` object if `drop=TRUE` is not set. The first argument are the row indices and the second argument the column indices. Alternatively logical subsetting can be used. The indices are based on visible rows and not on the tree structure. So:

    tbl[1, 1]
    #     A: Drug X
    # —————————————
    # F

is a table with an empty cell because the first row is a label row. We need to access a cell with actual cell data:

    tbl[3, 1]
    #        A: Drug X
    # ————————————————
    # Mean     32.76

To retrieve the value, we use `drop = TRUE`:

    tbl[3, 1, drop = TRUE]
    # [1] 32.75949

One can access multiple rows and columns:

    tbl[1:3, 1:2]
    #            A: Drug X   B: Placebo
    # —————————————————————————————————
    # F
    #   AGE
    #     Mean     32.76       34.12

Note that we do not repeat label rows for descending children, e.g.

    tbl[2:4, ]
    #           A: Drug X   B: Placebo   C: Combination
    # —————————————————————————————————————————————————
    # AGE
    #   Mean      32.76       34.12          35.20
    # STRATA1

does not show that the first row is derived from `AGE`. In order to repeat content/label information, one should use the pagination feature. Please read the related vignette.

Character indices are interpreted as paths (see below), NOT elements to be matched against `names(tbl)`:

    tbl[, c("ARM", "A: Drug X")]
    #             A: Drug X
    # —————————————————————
    # F
    #   AGE
    #     Mean      32.76
    #   STRATA1
    #     A          21
    #     B          25
    #     C          33
    # M
    #   AGE
    #     Mean      35.57
    #   STRATA1
    #     A          16
    #     B          21
    #     C          14

### Dealing with titles, foot notes, and top left information

As standard no additional information is kept after subsetting. Here, we show with a more complete table how it is still possible to keep the (possibly) relevant information.

    top_left(tbl) <- "SEX"
    main_title(tbl) <- "Table 1"
    subtitles(tbl) <- c("Authors:", " - Abcd Zabcd", " - Cde Zbcd")

    main_footer(tbl) <- "Please regard this table as an example of smart subsetting"
    prov_footer(tbl) <- "Do remember where you read this though"

    fnotes_at_path(tbl, rowpath = c("M", "AGE", "Mean"), colpath = c("ARM", "A: Drug X")) <- "Very important mean"

Normal subsetting loses all the information showed above.

    tbl[3, 3]
    #        C: Combination
    # —————————————————————
    # Mean       35.20

If all the rows are kept, top left information is also kept. This can be also imposed by adding `keep_topleft = TRUE` to the subsetting as follows:

    tbl[, 2:3]
    # SEX         B: Placebo   C: Combination
    # ———————————————————————————————————————
    # F
    #   AGE
    #     Mean      34.12          35.20
    #   STRATA1
    #     A           24             18
    #     B           27             21
    #     C           26             27
    # M
    #   AGE
    #     Mean      37.44          35.38
    #   STRATA1
    #     A           19             20
    #     B           17             21
    #     C           19             19
    tbl[1:3, 3, keep_topleft = TRUE]
    # SEX        C: Combination
    # —————————————————————————
    # F
    #   AGE
    #     Mean       35.20

If the referenced entry is present in the subsetting, also the referential footnote will appear. Please consider reading relevant vignette about referential footnotes. In case of subsetting, the referential footnotes are by default indexed again, as if the produced table is a new one.

    tbl[10, 1]
    #        A: Drug X
    # ————————————————
    # Mean   35.57 {1}
    # ————————————————
    #
    # {1} - Very important mean
    # ————————————————
    col_paths_summary(tbl) # Use these to find the right path to value or label
    # label             path
    # —————————————————————————————————————
    # A: Drug X         ARM, A: Drug X
    # B: Placebo        ARM, B: Placebo
    # C: Combination    ARM, C: Combination
    row_paths_summary(tbl) #
    # rowname      node_class    path
    # —————————————————————————————————————————————
    # F            LabelRow      SEX, F
    #   AGE        LabelRow      SEX, F, AGE
    #     Mean     DataRow       SEX, F, AGE, Mean
    #   STRATA1    LabelRow      SEX, F, STRATA1
    #     A        DataRow       SEX, F, STRATA1, A
    #     B        DataRow       SEX, F, STRATA1, B
    #     C        DataRow       SEX, F, STRATA1, C
    # M            LabelRow      SEX, M
    #   AGE        LabelRow      SEX, M, AGE
    #     Mean     DataRow       SEX, M, AGE, Mean
    #   STRATA1    LabelRow      SEX, M, STRATA1
    #     A        DataRow       SEX, M, STRATA1, A
    #     B        DataRow       SEX, M, STRATA1, B
    #     C        DataRow       SEX, M, STRATA1, C

    # To select column value, use `NULL` for `rowpath`
    fnotes_at_path(tbl, rowpath = NULL, colpath = c("ARM", "A: Drug X")) <- "Interesting"
    tbl[3, 1]
    #        A: Drug X {1}
    # ————————————————————
    # Mean       32.76
    # ————————————————————
    #
    # {1} - Interesting
    # ————————————————————

    # reindexing of {2} as {1}
    fnotes_at_path(tbl, rowpath = c("M", "AGE", "Mean"), colpath = NULL) <- "THIS mean"
    tbl # {1}, {2}, and {3} are present
    # Table 1
    # Authors:
    #  - Abcd Zabcd
    #  - Cde Zbcd
    #
    # ——————————————————————————————————————————————————————————
    # SEX            A: Drug X {1}   B: Placebo   C: Combination
    # ——————————————————————————————————————————————————————————
    # F
    #   AGE
    #     Mean           32.76         34.12          35.20
    #   STRATA1
    #     A               21             24             18
    #     B               25             27             21
    #     C               33             26             27
    # M
    #   AGE
    #     Mean {2}     35.57 {3}       37.44          35.38
    #   STRATA1
    #     A               16             19             20
    #     B               21             17             21
    #     C               14             19             19
    # ——————————————————————————————————————————————————————————
    #
    # {1} - Interesting
    # {2} - THIS mean
    # {3} - Very important mean
    # ——————————————————————————————————————————————————————————
    #
    # Please regard this table as an example of smart subsetting
    #
    # Do remember where you read this though
    tbl[10, 2] # only {1} which was previously {2}
    #            B: Placebo
    # —————————————————————
    # Mean {1}     37.44
    # —————————————————————
    #
    # {1} - THIS mean
    # —————————————————————

Similar to what we have used to keep top left information, we can specify to keep more information from the original table. As a standard the foot notes are always present if the titles are kept.

    tbl[1:3, 2:3, keep_titles = TRUE]
    # Table 1
    # Authors:
    #  - Abcd Zabcd
    #  - Cde Zbcd
    #
    # ——————————————————————————————————————
    #            B: Placebo   C: Combination
    # ——————————————————————————————————————
    # F
    #   AGE
    #     Mean     34.12          35.20
    # ——————————————————————————————————————
    #
    # Please regard this table as an example of smart subsetting
    #
    # Do remember where you read this though
    tbl[1:3, 2:3, keep_titles = FALSE, keep_footers = TRUE]
    #            B: Placebo   C: Combination
    # ——————————————————————————————————————
    # F
    #   AGE
    #     Mean     34.12          35.20
    # ——————————————————————————————————————
    #
    # Please regard this table as an example of smart subsetting
    #
    # Do remember where you read this though

    # Referential footnotes are not influenced by `keep_footers = FALSE`
    tbl[1:3, keep_titles = TRUE, keep_footers = FALSE]
    # Table 1
    # Authors:
    #  - Abcd Zabcd
    #  - Cde Zbcd
    #
    # ——————————————————————————————————————————————————————
    #            A: Drug X {1}   B: Placebo   C: Combination
    # ——————————————————————————————————————————————————————
    # F
    #   AGE
    #     Mean       32.76         34.12          35.20
    # ——————————————————————————————————————————————————————
    #
    # {1} - Interesting
    # ——————————————————————————————————————————————————————

## Path Based Cell Value Accessing:

Tables can be subset or modified in a structurally aware manner via pathing. See the pathing vignette for a more expansive discussion of pathing, though we reiterate the basics below.

Paths define semantically meaningful positions within a constructed table that correspond to the logic of the layout used to create it.

A path is an ordered set of split names, the names of subgroups generated by the split, and the `@content` directive, which steps into a position’s content (or row group summary) table.

We can see the row and column paths of an existing table via the `row_paths()`, `col_paths()`, `row_paths_summary()`, and `col_paths_summary()`, functions, or as a portion of the more general `make_row_df()` function output.

    lyt2 <- basic_table() %>%
      split_cols_by("ARM") %>%
      split_cols_by("SEX", split_fun = drop_split_levels) %>%
      split_rows_by("RACE", split_fun = drop_split_levels) %>%
      summarize_row_groups() %>%
      analyze(c("AGE", "STRATA1"))

    tbl2 <- build_table(lyt2, ex_adsl %>% filter(SEX %in% c("M", "F") & RACE %in% (levels(RACE)[1:3])))
    tbl2
    #                                    A: Drug X                B: Placebo              C: Combination
    #                                 F            M            F            M            F            M
    # ———————————————————————————————————————————————————————————————————————————————————————————————————————
    # ASIAN                       41 (53.9%)   25 (54.3%)   36 (52.2%)   30 (60.0%)   39 (60.9%)   32 (57.1%)
    #   AGE
    #     Mean                      31.22        34.60        35.06        38.63        36.44        37.66
    #   STRATA1
    #     A                           11           10           14           10           11           7
    #     B                           11           9            15           7            11           14
    #     C                           19           6            7            13           17           11
    # BLACK OR AFRICAN AMERICAN   18 (23.7%)   12 (26.1%)   16 (23.2%)   12 (24.0%)   14 (21.9%)   14 (25.0%)
    #   AGE
    #     Mean                      34.06        34.58        33.88        36.33        33.21        34.21
    #   STRATA1
    #     A                           5            2            5            6            3            7
    #     B                           6            5            3            4            4            4
    #     C                           7            5            8            2            7            3
    # WHITE                       17 (22.4%)   9 (19.6%)    17 (24.6%)   8 (16.0%)    11 (17.2%)   10 (17.9%)
    #   AGE
    #     Mean                      34.12        40.00        32.41        34.62        33.00        30.80
    #   STRATA1
    #     A                           5            3            3            3            3            5
    #     B                           5            4            8            4            5            2
    #     C                           7            2            6            1            3            3

So the column paths are as follows:

    col_paths_summary(tbl2)
    # label             path
    # —————————————————————————————————————————————
    # A: Drug X         ARM, A: Drug X
    #   F               ARM, A: Drug X, SEX, F
    #   M               ARM, A: Drug X, SEX, M
    # B: Placebo        ARM, B: Placebo
    #   F               ARM, B: Placebo, SEX, F
    #   M               ARM, B: Placebo, SEX, M
    # C: Combination    ARM, C: Combination
    #   F               ARM, C: Combination, SEX, F
    #   M               ARM, C: Combination, SEX, M

and the row paths are as follows:

    row_paths_summary(tbl2)
    # rowname                      node_class    path
    # ———————————————————————————————————————————————————————————————————————————————————————————————————————————————
    # ASIAN                        ContentRow    RACE, ASIAN, @content, ASIAN
    #   AGE                        LabelRow      RACE, ASIAN, AGE
    #     Mean                     DataRow       RACE, ASIAN, AGE, Mean
    #   STRATA1                    LabelRow      RACE, ASIAN, STRATA1
    #     A                        DataRow       RACE, ASIAN, STRATA1, A
    #     B                        DataRow       RACE, ASIAN, STRATA1, B
    #     C                        DataRow       RACE, ASIAN, STRATA1, C
    # BLACK OR AFRICAN AMERICAN    ContentRow    RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN
    #   AGE                        LabelRow      RACE, BLACK OR AFRICAN AMERICAN, AGE
    #     Mean                     DataRow       RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean
    #   STRATA1                    LabelRow      RACE, BLACK OR AFRICAN AMERICAN, STRATA1
    #     A                        DataRow       RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A
    #     B                        DataRow       RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B
    #     C                        DataRow       RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C
    # WHITE                        ContentRow    RACE, WHITE, @content, WHITE
    #   AGE                        LabelRow      RACE, WHITE, AGE
    #     Mean                     DataRow       RACE, WHITE, AGE, Mean
    #   STRATA1                    LabelRow      RACE, WHITE, STRATA1
    #     A                        DataRow       RACE, WHITE, STRATA1, A
    #     B                        DataRow       RACE, WHITE, STRATA1, B
    #     C                        DataRow       RACE, WHITE, STRATA1, C

To get a semantically meaningful subset of our table, then, we can use `[` (or `tt_at_path()` which underlies it)

    tbl2[c("RACE", "ASIAN"), c("ARM", "C: Combination")]
    #                 C: Combination
    #                 F            M
    # ———————————————————————————————————
    # ASIAN       39 (60.9%)   32 (57.1%)
    #   AGE
    #     Mean      36.44        37.66
    #   STRATA1
    #     A           11           7
    #     B           11           14
    #     C           17           11

We can also retrieve individual cell-values via the `value_at()` convenience function, which takes a pair of row and column paths which resolve together to an individual cell, e.g. average age for Asian female patients in arm A:

    value_at(tbl2, c("RACE", "ASIAN", "AGE", "Mean"), c("ARM", "A: Drug X", "SEX", "F"))
    # [1] 31.21951

You can also request information from non-cell specific paths with the `cell_values()` function:

    cell_values(tbl2, c("RACE", "ASIAN", "AGE", "Mean"), c("ARM", "A: Drug X"))
    # $`A: Drug X.F`
    # [1] 31.21951
    #
    # $`A: Drug X.M`
    # [1] 34.6

Note the return value of `cell_values()` is always a list even if you specify a path to a cell:

    cell_values(tbl2, c("RACE", "ASIAN", "AGE", "Mean"), c("ARM", "A: Drug X", "SEX", "F"))
    # $`A: Drug X.F`
    # [1] 31.21951