Pathing and Table Structure

Gabriel Becker

2025-06-17

Introduction

rtables models both the row- and column-structure of a table as trees. These trees collectively reflect the layout instructions used to declare the table’s structure. We can use this to describe locations within the row-, column-, or cell-space of a table in a semantically self-describing way. We call these semantically meaningful locations paths.

A path is an ordered set of names which declares both a path for traversing the tree structure for the relevant dimensions, and consequently a corresponding subset of the table in that dimension. Column paths may contain only split names and names of facets generated from those splits. Row paths, can additionally contain names of tables corresponding to analysis calls, the "@content" directive which steps from a facet into the table generated by summarize_row_groups containing its marginal summary row(s), and names of individual rows.The location of individual cells or rectangular groups of cells is then defined by a row-path column-path pair.

As of rtables version 0.6.13, any structural element of a successfully built1 table is guaranteed to correspond to a unique row path, column path, or combination thereof.

An Illustrative Example

Consider a the table with non-trivial structure in both the column and row dimensions:

library(rtables)

keep_rc <- c("ASIAN", "WHITE") ## chosen for brevity

afun <- function(x) {
  list(
    Mean = rcell(mean(x), format = "xx.x"),
    Median = rcell(median(x), format = "xx.x")
  )
}

lyt <- basic_table() |>
  split_cols_by("ARM", split_fun = keep_split_levels(c("A: Drug X", "C: Combination"))) |>
  split_cols_by("SEX", split_fun = keep_split_levels(c("F", "M"))) |>
  add_overall_col("All") |>
  split_rows_by("RACE", split_fun = keep_split_levels(keep_rc)) |>
  summarize_row_groups() |>
  split_rows_by("STRATA1") |>
  summarize_row_groups() |>
  analyze("AGE", afun = afun) |>
  analyze("BMRKR1", nested = FALSE, show_labels = "visible")

tbl <- build_table(lyt, DM)
tbl
##                     A: Drug X              C: Combination                   
##                  F            M            F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3    
##     Median      32.0         34.5         34.0         34.5         33.0    
##   C          13 (18.6%)   15 (29.4%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        36.9         35.6         33.5         31.4         33.9    
##     Median      36.0         37.0         34.0         31.0         34.0    
## WHITE        8 (11.4%)    6 (11.8%)    8 (13.1%)    10 (14.7%)   46 (12.9%) 
##   A           2 (2.9%)     1 (2.0%)     1 (1.6%)     5 (7.4%)     15 (4.2%) 
##     Mean        34.0         45.0         35.0         32.8         33.3    
##     Median      34.0         45.0         35.0         31.0         31.0    
##   B           4 (5.7%)     3 (5.9%)     3 (4.9%)     1 (1.5%)     16 (4.5%) 
##     Mean        37.0         43.7         34.3         36.0         38.3    
##     Median      38.5         44.0         35.0         36.0         38.0    
##   C           2 (2.9%)     2 (3.9%)     4 (6.6%)     4 (5.9%)     15 (4.2%) 
##     Mean        35.5         44.0         38.5         35.0         39.1    
##     Median      35.5         44.0         38.5         32.5         40.0    
## BMRKR1                                                                      
##   Mean          6.06         5.42         5.83         5.57         5.85

We can get a first look at the row- and column-structure of our table (if in different formats) with table_structure and col_info.

table_structure(tbl)
## [TableTree] root
##  [TableTree] RACE
##   [TableTree] ASIAN [cont: 1 x 5]
##    [TableTree] STRATA1
##     [TableTree] A [cont: 1 x 5]
##      [ElementaryTable] AGE (2 x 5)
##     [TableTree] B [cont: 1 x 5]
##      [ElementaryTable] AGE (2 x 5)
##     [TableTree] C [cont: 1 x 5]
##      [ElementaryTable] AGE (2 x 5)
##   [TableTree] WHITE [cont: 1 x 5]
##    [TableTree] STRATA1
##     [TableTree] A [cont: 1 x 5]
##      [ElementaryTable] AGE (2 x 5)
##     [TableTree] B [cont: 1 x 5]
##      [ElementaryTable] AGE (2 x 5)
##     [TableTree] C [cont: 1 x 5]
##      [ElementaryTable] AGE (2 x 5)
##  [ElementaryTable] BMRKR1 (1 x 5)
col_info(tbl)
## An InstantiatedColumnInfo object
## Columns:
## A: Drug X (ARM) -> F (SEX)
## A: Drug X (ARM) -> M (SEX)
## C: Combination (ARM) -> F (SEX)
## C: Combination (ARM) -> M (SEX)
## All (all)

We can use paths to declare intuitive substructures of our table. We illustrate this using [ which interpret character vector indices as individual paths in the respective dimension.

Note that while we will use these paths to subset our table for illustrative purposes, they are more often used to specify where something should happen within the larger table, which we discuss in the following section.

Row Paths

Row paths, in isolation, describe horizontal slices of our table. We can see all the valid row paths (including an optional “root” beginning value which is technically correct but not necessary to include) via row_paths_summary.

rpsummry <- row_paths_summary(tbl)
## rowname       node_class    path                                      
## ——————————————————————————————————————————————————————————————————————
## ASIAN         ContentRow    root, RACE, ASIAN, @content, ASIAN        
##   A           ContentRow    root, RACE, ASIAN, STRATA1, A, @content, A
##     Mean      DataRow       root, RACE, ASIAN, STRATA1, A, AGE, Mean  
##     Median    DataRow       root, RACE, ASIAN, STRATA1, A, AGE, Median
##   B           ContentRow    root, RACE, ASIAN, STRATA1, B, @content, B
##     Mean      DataRow       root, RACE, ASIAN, STRATA1, B, AGE, Mean  
##     Median    DataRow       root, RACE, ASIAN, STRATA1, B, AGE, Median
##   C           ContentRow    root, RACE, ASIAN, STRATA1, C, @content, C
##     Mean      DataRow       root, RACE, ASIAN, STRATA1, C, AGE, Mean  
##     Median    DataRow       root, RACE, ASIAN, STRATA1, C, AGE, Median
## WHITE         ContentRow    root, RACE, WHITE, @content, WHITE        
##   A           ContentRow    root, RACE, WHITE, STRATA1, A, @content, A
##     Mean      DataRow       root, RACE, WHITE, STRATA1, A, AGE, Mean  
##     Median    DataRow       root, RACE, WHITE, STRATA1, A, AGE, Median
##   B           ContentRow    root, RACE, WHITE, STRATA1, B, @content, B
##     Mean      DataRow       root, RACE, WHITE, STRATA1, B, AGE, Mean  
##     Median    DataRow       root, RACE, WHITE, STRATA1, B, AGE, Median
##   C           ContentRow    root, RACE, WHITE, STRATA1, C, @content, C
##     Mean      DataRow       root, RACE, WHITE, STRATA1, C, AGE, Mean  
##     Median    DataRow       root, RACE, WHITE, STRATA1, C, AGE, Median
## BMRKR1        LabelRow      root, BMRKR1                              
##   Mean        DataRow       root, BMRKR1, Mean

In addition to displaying a nicely formatted summary, this returns a data.frame containing the same information in a programmatically accessible form. In particular path is a list-valued column whose values can be used directly as row paths:

head(rpsummry)
##    label indent node_class         path
## 1  ASIAN      0 ContentRow root, RA....
## 2      A      1 ContentRow root, RA....
## 3   Mean      2    DataRow root, RA....
## 4 Median      2    DataRow root, RA....
## 5      B      1 ContentRow root, RA....
## 6   Mean      2    DataRow root, RA....
tbl[rpsummry$path[[6]], ]
##         A: Drug X      C: Combination         
##          F      M        F         M      All 
## ——————————————————————————————————————————————
## Mean   33.8    34.9    33.3      35.9     33.3

The c("RACE", "ASIAN") row path refers to the horizontal slice of our table containing all rows that represent analysis on Asian patients. We see that we get the expected subtable:

tbl[c("RACE", "ASIAN"), ]
##                     A: Drug X              C: Combination                   
##                  F            M            F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3    
##     Median      32.0         34.5         34.0         34.5         33.0    
##   C          13 (18.6%)   15 (29.4%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        36.9         35.6         33.5         31.4         33.9    
##     Median      36.0         37.0         34.0         31.0         34.0

Similarly we can get the groups summary and row for strata B of Caucasian patients via

tbl[c("RACE", "WHITE", "STRATA1", "B"), ]
##                 A: Drug X          C: Combination               
##               F          M          F          M          All   
## ————————————————————————————————————————————————————————————————
## B          4 (5.7%)   3 (5.9%)   3 (4.9%)   1 (1.5%)   16 (4.5%)
##   Mean       37.0       43.7       34.3       36.0       38.3   
##   Median     38.5       44.0       35.0       36.0       38.0

Notice this is a strict subtable in the structural sense, which means we do not get the ethnicity-level group summary here. We can see this because our structure and our path now starts with "B":

table_structure(tbl[c("RACE", "WHITE", "STRATA1", "B"), ])
## [TableTree] root
##  [TableTree] RACE
##   [TableTree] WHITE
##    [TableTree] STRATA1
##     [TableTree] B [cont: 1 x 5]
##      [ElementaryTable] AGE (2 x 5)

As mentioned above, to “path into” a group summary we use the "@content" directive:

tbl[c("RACE", "ASIAN", "@content"), ]
##                A: Drug X              C: Combination                   
##             F            M            F            M            All    
## ———————————————————————————————————————————————————————————————————————
## ASIAN   44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)

We can path down to analysis tables and then individual rows via their name, which unlike other structures tends to be identical to their label:

tbl[c("RACE", "WHITE", "STRATA1", "B", "AGE"), ]
##           A: Drug X      C: Combination         
##            F      M        F         M      All 
## ————————————————————————————————————————————————
## Mean     37.0    43.7    34.3      36.0     38.3
## Median   38.5    44.0    35.0      36.0     38.0
tbl[c("RACE", "WHITE", "STRATA1", "B", "AGE", "Median"), ]
##           A: Drug X      C: Combination         
##            F      M        F         M      All 
## ————————————————————————————————————————————————
## Median   38.5    44.0    35.0      36.0     38.0

Column Paths

Similar to row paths, we can get information about column paths via col_paths_summary:

col_paths_summary(tbl)
## label             path                       
## —————————————————————————————————————————————
## A: Drug X         ARM, A: Drug X             
##   F               ARM, A: Drug X, SEX, F     
##   M               ARM, A: Drug X, SEX, M     
## C: Combination    ARM, C: Combination        
##   F               ARM, C: Combination, SEX, F
##   M               ARM, C: Combination, SEX, M
## All               All, All

We can then describe vertical slices of our table via these paths (we use head, which subsets via absolute position to limit the amount of output here):

head(tbl[, c("ARM", "A: Drug X")])
##                     A: Drug X       
##                  F            M     
## ————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)
##   A          15 (21.4%)   12 (23.5%)
##     Mean        30.4         34.4   
##     Median      30.0         33.5   
##   B          16 (22.9%)   8 (15.7%) 
##     Mean        33.8         34.9
head(tbl[, c("ARM", "C: Combination", "SEX", "M")])
##              C: Combination
##                    M       
## ———————————————————————————
## ASIAN          44 (64.7%)  
##   A            16 (23.5%)  
##     Mean          36.2     
##     Median        33.0     
##   B            12 (17.6%)  
##     Mean          35.9
head(tbl[, c("All", "All")])
##                  All    
## ————————————————————————
## ASIAN        231 (64.9%)
##   A          78 (21.9%) 
##     Mean        34.5    
##     Median      33.0    
##   B          75 (21.1%) 
##     Mean        33.3

Note that despite being displayed next to each-other, the last two columns of our table have fundamentally different paths. This is due to add_overall_col adding a non-nested additional split rather than adding an additional implicit combination level to the ARM split.

Uniqueness of Paths

As of rtables 0.6.13, rtables enforces uniqueness of names within groups of direct sibling structures in both row and column2 space, thus guaranteeing unique paths to every substructure in the table.

In row space, it does this by appending [k] to the names of elements which would otherwise have an identical name to a previous sibling, where k is a sequence of integers such that all siblings have unique names. This affects the paths to these substructures3, as we see from the informative messages below:

lytdup <- basic_table() |>
  analyze("STRATA1") |>
  split_rows_by("STRATA1") |>
  analyze("AGE")

tbldup <- build_table(lytdup, DM)
## Modifying subtable (or row) names to ensure uniqueness among direct siblings
## [STRATA1  -> { STRATA1, STRATA1[2] }]
##   To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
tbldup
##          all obs
## ————————————————
## A          114  
## B          119  
## C          123  
## A               
##   Mean    33.74 
## B               
##   Mean    34.10 
## C               
##   Mean    34.79
row_paths_summary(tbldup)
## rowname    node_class    path                          
## ———————————————————————————————————————————————————————
## A          DataRow       root, STRATA1, A              
## B          DataRow       root, STRATA1, B              
## C          DataRow       root, STRATA1, C              
## A          LabelRow      root, STRATA1[2], A           
##   Mean     DataRow       root, STRATA1[2], A, AGE, Mean
## B          LabelRow      root, STRATA1[2], B           
##   Mean     DataRow       root, STRATA1[2], B, AGE, Mean
## C          LabelRow      root, STRATA1[2], C           
##   Mean     DataRow       root, STRATA1[2], C, AGE, Mean

This allows us to path to all elements of the row structure, which was not possible in previous (<0.6.13) rtables versions:

tbldup[c("STRATA1", "A"), ]
##     all obs
## ———————————
## A     114
tbldup[c("STRATA1[2]", "A"), ]
##          all obs
## ————————————————
## A               
##   Mean    33.74

Wildcards In Paths

Many, though not all, rtables functions which accept a row or column paths support the "*" path wildcard. Where supported, the wild-card will match any name present at that step in the table structure, leading to (potentially) multiple matches. Note "*" will never behave as the "@content" directive, which must always be used explicitly.

tbl[c("RACE", "*", "STRATA1", "B", "AGE", "Median"), ]
##           A: Drug X      C: Combination         
##            F      M        F         M      All 
## ————————————————————————————————————————————————
## Median   32.0    34.5    34.0      34.5     33.0
## Median   38.5    44.0    35.0      36.0     38.0

Multiple wildcards can appear in a path, with each wildcard applied recursively within the full combined set of matches from all wildcards earlier in the path.

tbl[c("RACE", "*", "STRATA1", "*", "AGE", "Median"), ]
##           A: Drug X      C: Combination         
##            F      M        F         M      All 
## ————————————————————————————————————————————————
## Median   30.0    33.5    38.0      33.0     33.0
## Median   32.0    34.5    34.0      34.5     33.0
## Median   36.0    37.0    34.0      31.0     34.0
## Median   34.0    45.0    35.0      31.0     31.0
## Median   38.5    44.0    35.0      36.0     38.0
## Median   35.5    44.0    38.5      32.5     40.0

Note that while the [ method does support wildcards, we are only using that to illustrate the behavior, as the tables resulting from using wildcard paths with [ are generally not going to be very useful.

For (currently only) row paths, we can resolve a path with one or more wildcards into a set of fully specified paths that match the path in our table using the tt_normalize_row_path utility function

tt_normalize_row_path(tbl, c("RACE", "*", "STRATA1", "*", "AGE", "Median"))
## $ASIAN.A
## [1] "RACE"    "ASIAN"   "STRATA1" "A"       "AGE"     "Median" 
## 
## $ASIAN.B
## [1] "RACE"    "ASIAN"   "STRATA1" "B"       "AGE"     "Median" 
## 
## $ASIAN.C
## [1] "RACE"    "ASIAN"   "STRATA1" "C"       "AGE"     "Median" 
## 
## $WHITE.A
## [1] "RACE"    "WHITE"   "STRATA1" "A"       "AGE"     "Median" 
## 
## $WHITE.B
## [1] "RACE"    "WHITE"   "STRATA1" "B"       "AGE"     "Median" 
## 
## $WHITE.C
## [1] "RACE"    "WHITE"   "STRATA1" "C"       "AGE"     "Median"

We can also test whether a row path (including those containing wildcards) exists in our table with tt_row_path_exists

tt_row_path_exists(tbl, c("RACE", "*", "STRATA1", "*", "AGE", "Median"))
## [1] TRUE
tt_row_path_exists(tbl, c("RACE", "*", "STRATA1", "*", "FAKEFAKEFAKE", "Median"))
## [1] FALSE

Note also that each "*" wildcard will only match a single step, there is not currently a directive that searches for a match anywhere in the relevant (sub)structure.

Thus we get

tt_normalize_row_path(tbl, c("*", "Mean"))
## $BMRKR1
## [1] "BMRKR1" "Mean"

Despite there being other "Mean" elements elsewhere in our row structure.

Though the above utilities don’t currently exist for column paths (which are implemented differently in ways not relevant to end users), generally those mechanisms which support wildcards in row space and also accept a column path support wildcards for column paths as well:

tbl[, c("ARM", "*", "SEX", "F")]
##              A: Drug X    C: Combination
##                  F              F       
## ————————————————————————————————————————
## ASIAN        44 (62.9%)     40 (65.6%)  
##   A          15 (21.4%)     15 (24.6%)  
##     Mean        30.4           37.4     
##     Median      30.0           38.0     
##   B          16 (22.9%)     10 (16.4%)  
##     Mean        33.8           33.3     
##     Median      32.0           34.0     
##   C          13 (18.6%)     15 (24.6%)  
##     Mean        36.9           33.5     
##     Median      36.0           34.0     
## WHITE        8 (11.4%)      8 (13.1%)   
##   A           2 (2.9%)       1 (1.6%)   
##     Mean        34.0           35.0     
##     Median      34.0           35.0     
##   B           4 (5.7%)       3 (4.9%)   
##     Mean        37.0           34.3     
##     Median      38.5           35.0     
##   C           2 (2.9%)       4 (6.6%)   
##     Mean        35.5           38.5     
##     Median      35.5           38.5     
## BMRKR1                                  
##   Mean          6.06           5.83

Operating On Tables With Paths

In addition to subsetting via paths, which as we mentioned is likely to be of limited utility, many aspects of a table can be selectively inspected or changed using paths.

We will explore some of these throughout this section

Column Counts

We can set (though, currently not get, an oversight that will likely be remedied in a future version) the visibility on a set of sibling facets.

tbl2 <- head(tbl)
facet_colcounts_visible(tbl2, c("ARM", "A: Drug X", "SEX")) <- TRUE
tbl2
##                     A: Drug X                                               
##                  F            M            C: Combination                   
##                (N=70)       (N=51)         F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3

NB: unlike virtually all functions which accept paths, facet_colcounts_visible accepts the path to the parent of the facets you’d like to change the colcount visibility for. This is because direct siblings cannot have different column count visibilities, so pathing to individual facets would lead to an invalid table.

We can also get or modify the value of any particular column count (note no s here):

facet_colcount(tbl2, c("ARM", "A: Drug X", "SEX", "M")) <- 5
tbl2
##                     A: Drug X                                               
##                  F            M            C: Combination                   
##                (N=70)       (N=5)          F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3

If we need to mix visibilty and non-visibilty of counts within a direct sibling group the best we can do is setting one to NA, which will leave a blank space there:

facet_colcount(tbl2, c("ARM", "A: Drug X", "SEX", "F")) <- NA_integer_
tbl2
##                     A: Drug X                                               
##                  F            M            C: Combination                   
##                             (N=5)          F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3

Section Dividers

Section dividers are character(s) that are printed in a line after a particular row or subtable during rendering to differentiate sections of a table (they are most often, and by default, ” ” to create a blank line).

tbl3 <- tbl

section_div_at_path(tbl3, c("RACE", "*")) <- "*"
section_div_at_path(tbl3, c("RACE", "*", "STRATA1", "B")) <- "+"
tbl3
##                     A: Drug X              C: Combination                   
##                  F            M            F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3    
##     Median      32.0         34.5         34.0         34.5         33.0    
## ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
##   C          13 (18.6%)   15 (29.4%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        36.9         35.6         33.5         31.4         33.9    
##     Median      36.0         37.0         34.0         31.0         34.0    
## ****************************************************************************
## WHITE        8 (11.4%)    6 (11.8%)    8 (13.1%)    10 (14.7%)   46 (12.9%) 
##   A           2 (2.9%)     1 (2.0%)     1 (1.6%)     5 (7.4%)     15 (4.2%) 
##     Mean        34.0         45.0         35.0         32.8         33.3    
##     Median      34.0         45.0         35.0         31.0         31.0    
##   B           4 (5.7%)     3 (5.9%)     3 (4.9%)     1 (1.5%)     16 (4.5%) 
##     Mean        37.0         43.7         34.3         36.0         38.3    
##     Median      38.5         44.0         35.0         36.0         38.0    
## ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
##   C           2 (2.9%)     2 (3.9%)     4 (6.6%)     4 (5.9%)     15 (4.2%) 
##     Mean        35.5         44.0         38.5         35.0         39.1    
##     Median      35.5         44.0         38.5         32.5         40.0    
## ****************************************************************************
## BMRKR1                                                                      
##   Mean          6.06         5.42         5.83         5.57         5.85

Section dividers have a least specific to most specific order of precedence, with only the least specific applicable section divider displayed after any given row. See ?section_div for more details.

Sorting Within An rtables Table

Sorting rows in a table occurs in a path-specific way. See the sorting section in the pruning and sorting vignette for a detailed discussion of this.

Extracting And Modifying Values

We saw that the [ method interprets character indicies as paths. Beyond that, the value_at and cell_values getters and setters accept paths as well. See the subsetting vignette.

Footnotes

Pathing can also be used to add referential footnotes to rows, columns, or cells. This is discussed in the title and footer and subsetting vignettes.

Table Structure And Technical Details

Here we will go into a bit more detail of how layouts, table structure, and pathing are related. This is largely for informational purposes and most of it will not be directly relevant to end-users who are simply creating tables.

Row Structure

rtables is row-dominant (as opposed to R’s data.frames which are column dominant). This means that tables are modelled as a (generalized) collection of rows, rather than columns. More accurately, a table is modeled as a collection of children, which can have children, etc until ultimately all of the “leaf” children in the defined tree-graph are individual rows.

We can see this using the tree_children function. The table we’ve been working with throughout this vignette has two direct children, one containing all of the structure generated underneath the initial "RACE" split, and one containing the unnested analysis of "BMRKR1":

tree_children(tbl)
## $RACE
##                     A: Drug X              C: Combination                   
##                  F            M            F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3    
##     Median      32.0         34.5         34.0         34.5         33.0    
##   C          13 (18.6%)   15 (29.4%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        36.9         35.6         33.5         31.4         33.9    
##     Median      36.0         37.0         34.0         31.0         34.0    
## WHITE        8 (11.4%)    6 (11.8%)    8 (13.1%)    10 (14.7%)   46 (12.9%) 
##   A           2 (2.9%)     1 (2.0%)     1 (1.6%)     5 (7.4%)     15 (4.2%) 
##     Mean        34.0         45.0         35.0         32.8         33.3    
##     Median      34.0         45.0         35.0         31.0         31.0    
##   B           4 (5.7%)     3 (5.9%)     3 (4.9%)     1 (1.5%)     16 (4.5%) 
##     Mean        37.0         43.7         34.3         36.0         38.3    
##     Median      38.5         44.0         35.0         36.0         38.0    
##   C           2 (2.9%)     2 (3.9%)     4 (6.6%)     4 (5.9%)     15 (4.2%) 
##     Mean        35.5         44.0         38.5         35.0         39.1    
##     Median      35.5         44.0         38.5         32.5         40.0    
## 
## $BMRKR1
##           A: Drug X      C: Combination         
##            F      M        F         M      All 
## ————————————————————————————————————————————————
## BMRKR1                                          
##   Mean   6.06    5.42    5.83      5.57     5.85

For convenience we will define a multi_step_children function which recursively retrieves children from the table, and then from those children, etc. For information purposes, we will print the “path step” taken each time, thus building up our path as we descend using the class structure.

multi_step_children <- function(tbl, indices) {
  print(obj_name(tbl))
  ret <- tree_children(tbl)
  for (i in indices) {
    print(obj_name(ret[[i]]))
    ret <- tree_children(ret[[i]])
  }
  ret
}

Thus we can see that the first of our table’s children has the path c("root", "RACE") and has children for each ethnicity in our table (recall the “root” path element is correct but optional):

multi_step_children(tbl, 1)
## [1] "root"
## [1] "RACE"
## $ASIAN
##                     A: Drug X              C: Combination                   
##                  F            M            F            M            All    
## ————————————————————————————————————————————————————————————————————————————
## ASIAN        44 (62.9%)   35 (68.6%)   40 (65.6%)   44 (64.7%)   231 (64.9%)
##   A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        30.4         34.4         37.4         36.2         34.5    
##     Median      30.0         33.5         38.0         33.0         33.0    
##   B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%) 
##     Mean        33.8         34.9         33.3         35.9         33.3    
##     Median      32.0         34.5         34.0         34.5         33.0    
##   C          13 (18.6%)   15 (29.4%)   15 (24.6%)   16 (23.5%)   78 (21.9%) 
##     Mean        36.9         35.6         33.5         31.4         33.9    
##     Median      36.0         37.0         34.0         31.0         34.0    
## 
## $WHITE
##                    A: Drug X             C: Combination                 
##                  F           M           F           M           All    
## ————————————————————————————————————————————————————————————————————————
## WHITE        8 (11.4%)   6 (11.8%)   8 (13.1%)   10 (14.7%)   46 (12.9%)
##   A          2 (2.9%)    1 (2.0%)    1 (1.6%)     5 (7.4%)    15 (4.2%) 
##     Mean       34.0        45.0        35.0         32.8         33.3   
##     Median     34.0        45.0        35.0         31.0         31.0   
##   B          4 (5.7%)    3 (5.9%)    3 (4.9%)     1 (1.5%)    16 (4.5%) 
##     Mean       37.0        43.7        34.3         36.0         38.3   
##     Median     38.5        44.0        35.0         36.0         38.0   
##   C          2 (2.9%)    2 (3.9%)    4 (6.6%)     4 (5.9%)    15 (4.2%) 
##     Mean       35.5        44.0        38.5         35.0         39.1   
##     Median     35.5        44.0        38.5         32.5         40.0

Each of these children under "RACE" is a subtable.

The children under our BMRKR1 analysis, on the other hand, are rows (in this case only one row, in fact):

multi_step_children(tbl, 2)
## [1] "root"
## [1] "BMRKR1"
## $Mean
## [DataRow indent_mod 0]: Mean   6.06   5.42   5.83   5.57   5.85

Within each race subtable, we see a table corresponding to the STRATA1 split:

multi_step_children(tbl, c(1, 1))
## [1] "root"
## [1] "RACE"
## [1] "ASIAN"
## $STRATA1
##                   A: Drug X              C: Combination                  
##                F            M            F            M           All    
## —————————————————————————————————————————————————————————————————————————
## A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%)
##   Mean        30.4         34.4         37.4         36.2         34.5   
##   Median      30.0         33.5         38.0         33.0         33.0   
## B          16 (22.9%)   8 (15.7%)    10 (16.4%)   12 (17.6%)   75 (21.1%)
##   Mean        33.8         34.9         33.3         35.9         33.3   
##   Median      32.0         34.5         34.0         34.5         33.0   
## C          13 (18.6%)   15 (29.4%)   15 (24.6%)   16 (23.5%)   78 (21.9%)
##   Mean        36.9         35.6         33.5         31.4         33.9   
##   Median      36.0         37.0         34.0         31.0         34.0
multi_step_children(tbl, c(1, 1, 1))
## [1] "root"
## [1] "RACE"
## [1] "ASIAN"
## [1] "STRATA1"
## $A
##                   A: Drug X              C: Combination                  
##                F            M            F            M           All    
## —————————————————————————————————————————————————————————————————————————
## A          15 (21.4%)   12 (23.5%)   15 (24.6%)   16 (23.5%)   78 (21.9%)
##   Mean        30.4         34.4         37.4         36.2         34.5   
##   Median      30.0         33.5         38.0         33.0         33.0   
## 
## $B
##                  A: Drug X              C: Combination                  
##                F            M           F            M           All    
## ————————————————————————————————————————————————————————————————————————
## B          16 (22.9%)   8 (15.7%)   10 (16.4%)   12 (17.6%)   75 (21.1%)
##   Mean        33.8        34.9         33.3         35.9         33.3   
##   Median      32.0        34.5         34.0         34.5         33.0   
## 
## $C
##                   A: Drug X              C: Combination                  
##                F            M            F            M           All    
## —————————————————————————————————————————————————————————————————————————
## C          13 (18.6%)   15 (29.4%)   15 (24.6%)   16 (23.5%)   78 (21.9%)
##   Mean        36.9         35.6         33.5         31.4         33.9   
##   Median      36.0         37.0         34.0         31.0         34.0

And finally within each strata facet is a table representing the analysis of AGE

multi_step_children(tbl, c(1, 1, 1, 2))
## [1] "root"
## [1] "RACE"
## [1] "ASIAN"
## [1] "STRATA1"
## [1] "B"
## $AGE
##           A: Drug X      C: Combination         
##            F      M        F         M      All 
## ————————————————————————————————————————————————
## Mean     33.8    34.9    33.3      35.9     33.3
## Median   32.0    34.5    34.0      34.5     33.0

And within each of those AGE analysis tables, like our BMRKR1 top level analysis table, we have a collection of rows:

multi_step_children(tbl, c(1, 1, 1, 2, 1))
## [1] "root"
## [1] "RACE"
## [1] "ASIAN"
## [1] "STRATA1"
## [1] "B"
## [1] "AGE"
## $Mean
## [DataRow indent_mod 0]: Mean   33.8   34.9   33.3   35.9   33.3
## 
## $Median
## [DataRow indent_mod 0]: Median   32.0   34.5   34.0   34.5   33.0

Thus we see that analyze calls create tables (called ElementaryTables) containing individual rows as children, while split_rows_by (and siblings) calls create a subtable with children that are a table for each facet declared by the split operation:

## child is AGE analysis table within RACE->WHITE->STRATA1->A
multi_step_children(tbl, c(1, 2, 1, 1))
## [1] "root"
## [1] "RACE"
## [1] "WHITE"
## [1] "STRATA1"
## [1] "A"
## $AGE
##           A: Drug X      C: Combination         
##            F      M        F         M      All 
## ————————————————————————————————————————————————
## Mean     34.0    45.0    35.0      32.8     33.3
## Median   34.0    45.0    35.0      31.0     31.0
## children are individual rows of that AGE table
multi_step_children(tbl, c(1, 2, 1, 1, 1))
## [1] "root"
## [1] "RACE"
## [1] "WHITE"
## [1] "STRATA1"
## [1] "A"
## [1] "AGE"
## $Mean
## [DataRow indent_mod 0]: Mean   34.0   45.0   35.0   32.8   33.3
## 
## $Median
## [DataRow indent_mod 0]: Median   34.0   45.0   35.0   31.0   31.0

Label And Group Summary Rows

For technical and historical reasons, label rows and so-called “content rows” (which are essentially marginal analyses at a non-leaf point in the tree graph defined by the parent-child relationships discussed above) are modeled separately.

Given a (sub)table, the content table (containing the content rows) and label can be retrieved by content_table and obj_label, respectively. Note that obj_label returns a string, not a row, as the label row is an internal detail not currently exposed.

Recall that our multi_step_children function returns the set of children at a location, so we must subset one additional time to arrive at a single child:

tb <- multi_step_children(tbl, c(1, 1, 1))[[2]] ## second ie B strata
## [1] "root"
## [1] "RACE"
## [1] "ASIAN"
## [1] "STRATA1"
tb
##                  A: Drug X              C: Combination                  
##                F            M           F            M           All    
## ————————————————————————————————————————————————————————————————————————
## B          16 (22.9%)   8 (15.7%)   10 (16.4%)   12 (17.6%)   75 (21.1%)
##   Mean        33.8        34.9         33.3         35.9         33.3   
##   Median      32.0        34.5         34.0         34.5         33.0
content_table(tb)
##           A: Drug X              C: Combination                  
##         F            M           F            M           All    
## —————————————————————————————————————————————————————————————————
## B   16 (22.9%)   8 (15.7%)   10 (16.4%)   12 (17.6%)   75 (21.1%)

Typically (ie by default) label rows for (sub)tables that have a non-empty content table are not visible when rendering, but they do still exist:

obj_label(tb)
## [1] "B"

Thus we see that:

  • split_rows_by* layout instructions create a single subtable with the split name, which contains a facet for each value of the split;
  • individual analyze instructions create a subtable containing individual rows defined by the afun used;
  • repeated or multi-var analyze instructions create a parent subtable with a child for each individual analyzed var, as above; and
  • summarize_row_groups create content tables on the children of the table for that split

Column Structure

For largely historical reasons, and due to the fact that the rtables object model is row-dominant, the exact way that column structure is modeled is an arcane implementation detail not useful to end users (much more so than the row structure explored above). Thus we will largely gloss over it here.

For our purposes here it suffices to say that the analog of the subtables representing split instructions are implicit in column space after the first split, as opposed to explicit as we saw them to be in row space. That said, the relationship between layout instructions and resulting paths in the table remains valid and useful.

We can see this by looking again at our column paths summary:

col_paths_summary(tbl)
## label             path                       
## —————————————————————————————————————————————
## A: Drug X         ARM, A: Drug X             
##   F               ARM, A: Drug X, SEX, F     
##   M               ARM, A: Drug X, SEX, M     
## C: Combination    ARM, C: Combination        
##   F               ARM, C: Combination, SEX, F
##   M               ARM, C: Combination, SEX, M
## All               All, All

Column paths have a more rigid structure than their row-based counterparts. Because column space has no analog to analyze layout instructions, All paths corresponding to facets or individual columns come in the form of one or more pairs of the form (split name, split value).

Virtually all useful column paths will be of the form above. The only exception to this is when setting column count visibility to a set of facets via facet_colcounts_visible<-, for which we path to the implicit parent structure whose children are the facets we are interested in. We saw this in action in the previous section.

To summarize, as for row space, the relationship between layout instructions and column paths as follows: column split instructions create structures pathable via (split_name, split value/facet name) pairs. Because there is no analyze analog for column splitting, this paradigm is sufficient to understand and predict all column paths.

  1. In rtables 0.6.13 table layouts which would result in non-unique paths in column space will fail to build. This will likely change to be more in line with the behavior in row space in a future release.↩︎

  2. In rtables 0.6.13 table layouts which would result in non-unique paths in column space will fail to build. This will likely change to be more in line with the behavior in row space in a future release.↩︎

  3. the result-data.frame / ARD machinery knows to remove these uniquification artifacts, so these modifications to the names will not be reflected there.↩︎