Often we want to filter or reorder elements of a table in ways that take into account the table structure. For example:
library(rtables)
library(dplyr)
raw_lyt <- basic_table() |>
split_cols_by("ARM") |>
split_cols_by("SEX") |>
split_rows_by("RACE") |>
summarize_row_groups() |>
split_rows_by("STRATA1") |>
summarize_row_groups() |>
analyze("AGE")
raw_tbl <- build_table(raw_lyt, DM)
raw_tbl
# A: Drug X B: Placebo C: Combination
# F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED
# ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 0 (NA%) 0 (NA%) 37 (66.1%) 31 (62.0%) 0 (NA%) 0 (NA%) 40 (65.6%) 44 (64.7%) 0 (NA%) 0 (NA%)
# A 15 (21.4%) 12 (23.5%) 0 (NA%) 0 (NA%) 14 (25.0%) 6 (12.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%)
# Mean 30.40 34.42 NA NA 35.43 30.33 NA NA 37.40 36.25 NA NA
# B 16 (22.9%) 8 (15.7%) 0 (NA%) 0 (NA%) 13 (23.2%) 16 (32.0%) 0 (NA%) 0 (NA%) 10 (16.4%) 12 (17.6%) 0 (NA%) 0 (NA%)
# Mean 33.75 34.88 NA NA 32.46 30.94 NA NA 33.30 35.92 NA NA
# C 13 (18.6%) 15 (29.4%) 0 (NA%) 0 (NA%) 10 (17.9%) 9 (18.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%)
# Mean 36.92 35.60 NA NA 34.00 31.89 NA NA 33.47 31.38 NA NA
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 0 (NA%) 0 (NA%) 12 (21.4%) 12 (24.0%) 0 (NA%) 0 (NA%) 13 (21.3%) 14 (20.6%) 0 (NA%) 0 (NA%)
# A 5 (7.1%) 1 (2.0%) 0 (NA%) 0 (NA%) 5 (8.9%) 2 (4.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%)
# Mean 31.20 33.00 NA NA 28.00 30.00 NA NA 30.75 36.50 NA NA
# B 7 (10.0%) 3 (5.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 6 (9.8%) 6 (8.8%) 0 (NA%) 0 (NA%)
# Mean 36.14 34.33 NA NA 29.67 32.00 NA NA 36.33 31.00 NA NA
# C 6 (8.6%) 6 (11.8%) 0 (NA%) 0 (NA%) 4 (7.1%) 7 (14.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 4 (5.9%) 0 (NA%) 0 (NA%)
# Mean 31.33 39.67 NA NA 34.50 34.00 NA NA 33.00 36.50 NA NA
# WHITE 8 (11.4%) 6 (11.8%) 0 (NA%) 0 (NA%) 7 (12.5%) 7 (14.0%) 0 (NA%) 0 (NA%) 8 (13.1%) 10 (14.7%) 0 (NA%) 0 (NA%)
# A 2 (2.9%) 1 (2.0%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 1 (1.6%) 5 (7.4%) 0 (NA%) 0 (NA%)
# Mean 34.00 45.00 NA NA 29.33 33.33 NA NA 35.00 32.80 NA NA
# B 4 (5.7%) 3 (5.9%) 0 (NA%) 0 (NA%) 1 (1.8%) 4 (8.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 1 (1.5%) 0 (NA%) 0 (NA%)
# Mean 37.00 43.67 NA NA 48.00 36.75 NA NA 34.33 36.00 NA NA
# C 2 (2.9%) 2 (3.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 0 (0.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%)
# Mean 35.50 44.00 NA NA 44.67 NA NA NA 38.50 35.00 NA NA
# AMERICAN INDIAN OR ALASKA NATIVE 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# MULTIPLE 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# OTHER 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# UNKNOWN 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NA
# C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%)
# Mean NA NA NA NA NA NA NA NA NA NA NA NATrimming represents a convenience wrapper around simple, direct subsetting of the rows of a TableTree.
We use the trim_rows() function with our table and a criteria function. All rows where the criteria function returns TRUE will be removed, and all others will be retained.
NOTE: Each row is kept or removed completely independently, with no awareness of the surrounding structure. This means, for example, that a subtree could have all its analysis rows removed and not be removed itself. This can easily result in a degenerate table which has an invalid structure (see ?validate_table_struct); for this reason trimming is generally not suggested for tables with non-trivial row structures. For structure-aware filtering of a table, we will use pruning described in the next section.
A trimming function accepts a TableRow object and returns TRUE if the row should be removed.
The default trimming function removes rows in which all columns have no values in them, i.e. that have all NA values or all 0 values:
trim_rows(raw_tbl)
# A: Drug X B: Placebo C: Combination
# F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED
# ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 0 (NA%) 0 (NA%) 37 (66.1%) 31 (62.0%) 0 (NA%) 0 (NA%) 40 (65.6%) 44 (64.7%) 0 (NA%) 0 (NA%)
# A 15 (21.4%) 12 (23.5%) 0 (NA%) 0 (NA%) 14 (25.0%) 6 (12.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%)
# Mean 30.40 34.42 NA NA 35.43 30.33 NA NA 37.40 36.25 NA NA
# B 16 (22.9%) 8 (15.7%) 0 (NA%) 0 (NA%) 13 (23.2%) 16 (32.0%) 0 (NA%) 0 (NA%) 10 (16.4%) 12 (17.6%) 0 (NA%) 0 (NA%)
# Mean 33.75 34.88 NA NA 32.46 30.94 NA NA 33.30 35.92 NA NA
# C 13 (18.6%) 15 (29.4%) 0 (NA%) 0 (NA%) 10 (17.9%) 9 (18.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%)
# Mean 36.92 35.60 NA NA 34.00 31.89 NA NA 33.47 31.38 NA NA
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 0 (NA%) 0 (NA%) 12 (21.4%) 12 (24.0%) 0 (NA%) 0 (NA%) 13 (21.3%) 14 (20.6%) 0 (NA%) 0 (NA%)
# A 5 (7.1%) 1 (2.0%) 0 (NA%) 0 (NA%) 5 (8.9%) 2 (4.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%)
# Mean 31.20 33.00 NA NA 28.00 30.00 NA NA 30.75 36.50 NA NA
# B 7 (10.0%) 3 (5.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 6 (9.8%) 6 (8.8%) 0 (NA%) 0 (NA%)
# Mean 36.14 34.33 NA NA 29.67 32.00 NA NA 36.33 31.00 NA NA
# C 6 (8.6%) 6 (11.8%) 0 (NA%) 0 (NA%) 4 (7.1%) 7 (14.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 4 (5.9%) 0 (NA%) 0 (NA%)
# Mean 31.33 39.67 NA NA 34.50 34.00 NA NA 33.00 36.50 NA NA
# WHITE 8 (11.4%) 6 (11.8%) 0 (NA%) 0 (NA%) 7 (12.5%) 7 (14.0%) 0 (NA%) 0 (NA%) 8 (13.1%) 10 (14.7%) 0 (NA%) 0 (NA%)
# A 2 (2.9%) 1 (2.0%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 1 (1.6%) 5 (7.4%) 0 (NA%) 0 (NA%)
# Mean 34.00 45.00 NA NA 29.33 33.33 NA NA 35.00 32.80 NA NA
# B 4 (5.7%) 3 (5.9%) 0 (NA%) 0 (NA%) 1 (1.8%) 4 (8.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 1 (1.5%) 0 (NA%) 0 (NA%)
# Mean 37.00 43.67 NA NA 48.00 36.75 NA NA 34.33 36.00 NA NA
# C 2 (2.9%) 2 (3.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 0 (0.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%)
# Mean 35.50 44.00 NA NA 44.67 NA NA NA 38.50 35.00 NA NAThere are currently no special utilities for trimming columns but we can remove the empty columns with fairly straightforward column subsetting using the col_counts() function:
coltrimmed <- raw_tbl[, col_counts(raw_tbl) > 0]
h_coltrimmed <- head(coltrimmed, n = 14)
h_coltrimmed
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50Now, it is interesting to see how this table is structured:
table_structure(h_coltrimmed)
# [TableTree] RACE
# [TableTree] ASIAN [cont: 1 x 6]
# [TableTree] STRATA1
# [TableTree] A [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] B [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] C [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 6]
# [TableTree] STRATA1
# [TableTree] A [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] B [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] C [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)For a deeper understanding of the fundamental structures in rtables, we suggest taking a look at slides 69-76 of this Slide deck.
In brief, it is important to notice how [TableTree] RACE is the root of the table that is split (with split_rows_by("RACE") |>) into two subtables: [TableTree] ASIAN [cont: 1 x 6] and [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 6]. These are then “described” with summarize_row_groups() |>, which creates for every split a “content” table containing 1 row (the 1 in cont: 1 x 6), which when rendered takes the place of LabelRow.
Each of these two subtables then contain a STRATA1 table, representing the further split_rows_by("STRATA1") in the layout, which, similar to the RACE table, is split into subtables: one for each strata which have similar content tables; Each individual strata subtable, then, contains an ElementaryTable (whose children are individual rows) generated by the analyze("AGE") layout directive, i.e. [ElementaryTable] AGE (1 x 6).
This subtable and row structure is very important for both sorting and pruning; values in “content” (ContentRow) and “value” (DataRow) rows use different access functions and they should be treated differently.
Another interesting function that can be used to understand the connection between row names and their representational path is the following:
row_paths_summary(h_coltrimmed)
# rowname node_class path
# ———————————————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN ContentRow RACE, ASIAN, @content, ASIAN
# A ContentRow RACE, ASIAN, STRATA1, A, @content, A
# Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean
# B ContentRow RACE, ASIAN, STRATA1, B, @content, B
# Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean
# C ContentRow RACE, ASIAN, STRATA1, C, @content, C
# Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean
# BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN
# A ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, @content, A
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean
# B ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, @content, B
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean
# C ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, @content, C
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, MeanPruning is similar in outcome to trimming, but more powerful and more complex, as it takes structure into account.
Pruning is applied recursively, in that at each structural unit (subtable, row) it applies the pruning function both at that level and to all it’s children (up to a user-specifiable maximum depth).
The default pruning function, for example, determines if a subtree is empty by:
NAsNAspruned <- prune_table(coltrimmed)
pruned
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00We can also use the low_obs_pruner() pruning function constructor to create a pruning function which removes subtrees with content summaries whose first entries for each column sum or average are below a specified number. (In the default summaries the first entry per column is the count).
pruned2 <- prune_table(coltrimmed, low_obs_pruner(10, "mean"))
pruned2
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ——————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38Note that because the pruning is being applied recursively, only the ASIAN subtree remains because even though the full BLACK OR AFRICAN AMERICAN subtree encompassed enough observations, the strata within it did not. We can take care of this by setting the stop_depth for pruning to 1.
pruned3 <- prune_table(coltrimmed, low_obs_pruner(10, "sum"), stop_depth = 1)
pruned3
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00We can also see that pruning to a lower number of observations, say, to a total of 16, with no stop_depth removes some but not all of the strata from our third race (WHITE).
pruned4 <- prune_table(coltrimmed, low_obs_pruner(16, "sum"))
pruned4
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00Sorting of an rtables table is done at a path, meaning a sort operation will occur at a particular location within the table, and the direct children of the element at that path will be reordered. This occurs whether those children are subtables themselves, or individual rows. Sorting is done via the sort_at_path() function, which accepts both a (row) path and a scoring function. See the pathing vignette for details about paths.
A score function accepts a subtree or TableRow and returns a single orderable (typically numeric) value. Within the subtable currently being sorted, the children are then reordered by the value of the score function. Importantly, “content” (ContentRow) and “values” (DataRow) need to be treated differently in the scoring function as they are retrieved: the content of a subtable is retrieved via the content _table accessor.
The cont_n_allcols() scoring function provided by rtables, works by scoring subtables by the sum of the first elements in the first row of the subtable’s content table. Note that this function fails if the child being scored does not have a content function (i.e., if summarize_row_groups() was not used at the corresponding point in the layout). We can see this in it’s definition, below:
cont_n_allcols
# function (tt)
# {
# ctab <- content_table(tt)
# if (NROW(ctab) == 0) {
# stop("cont_n_allcols score function used at subtable [",
# obj_name(tt), "] that has no content table.")
# }
# sum(sapply(row_values(tree_children(ctab)[[1]]), function(cv) cv[1]))
# }
# <bytecode: 0x55721ead9dc0>
# <environment: namespace:rtables>Therefore, a fundamental difference between pruning and sorting is that sorting occurs at particular places in the table, as defined by a path.
For example, we can sort the strata values (ContentRow) by observation counts within just the ASIAN subtable:
sort_at_path(pruned, path = c("RACE", "ASIAN", "STRATA1"), scorefun = cont_n_allcols)
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00
# B and C are swapped as the global count (sum of all column counts) of strata C is higher than the one of strata BA sorting path can contain one or more instances of the "*" wildcard. Each of these indicates that the children of each subtable matching this * element of the path should be sorted separately as indicated by the remainder of the path after the * and the score function.
Thus we can extend our sorting of strata within the ASIAN subtable to all race-specific subtables by using the wildcard:
sort_at_path(pruned, path = c("RACE", "*", "STRATA1"), scorefun = cont_n_allcols)
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00
# All subtables, i.e. ASIAN, BLACK..., and WHITE, are reordered separatelyThe above is equivalent to separately calling the following:
tmptbl <- sort_at_path(pruned, path = c("RACE", "ASIAN", "STRATA1"), scorefun = cont_n_allcols)
tmptbl <- sort_at_path(tmptbl, path = c("RACE", "BLACK OR AFRICAN AMERICAN", "STRATA1"), scorefun = cont_n_allcols)
tmptbl <- sort_at_path(tmptbl, path = c("RACE", "WHITE", "STRATA1"), scorefun = cont_n_allcols)
tmptbl
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00It is possible to understand better pathing with table_structure() that highlights the tree-like structure and the node names:
table_structure(pruned)
# [TableTree] RACE
# [TableTree] ASIAN [cont: 1 x 6]
# [TableTree] STRATA1
# [TableTree] A [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] B [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] C [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 6]
# [TableTree] STRATA1
# [TableTree] A [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] B [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] C [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] WHITE [cont: 1 x 6]
# [TableTree] STRATA1
# [TableTree] A [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] B [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)
# [TableTree] C [cont: 1 x 6]
# [ElementaryTable] AGE (1 x 6)or with row_paths_summary:
row_paths_summary(pruned)
# rowname node_class path
# ———————————————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN ContentRow RACE, ASIAN, @content, ASIAN
# A ContentRow RACE, ASIAN, STRATA1, A, @content, A
# Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean
# B ContentRow RACE, ASIAN, STRATA1, B, @content, B
# Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean
# C ContentRow RACE, ASIAN, STRATA1, C, @content, C
# Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean
# BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN
# A ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, @content, A
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean
# B ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, @content, B
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean
# C ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, @content, C
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean
# WHITE ContentRow RACE, WHITE, @content, WHITE
# A ContentRow RACE, WHITE, STRATA1, A, @content, A
# Mean DataRow RACE, WHITE, STRATA1, A, AGE, Mean
# B ContentRow RACE, WHITE, STRATA1, B, @content, B
# Mean DataRow RACE, WHITE, STRATA1, B, AGE, Mean
# C ContentRow RACE, WHITE, STRATA1, C, @content, C
# Mean DataRow RACE, WHITE, STRATA1, C, AGE, MeanNote in the latter we see content rows as those with paths following @content, e.g., ASIAN, @content, ASIAN. The first of these at a given path (i.e., <path>, @content, <> are the rows which will be used by the scoring functions which begin with cont_.
We can directly sort the ethnicity by observations in increasing order:
ethsort <- sort_at_path(pruned, path = c("RACE"), scorefun = cont_n_allcols, decreasing = FALSE)
ethsort
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38Within each ethnicity separately, sort the strata by number of females in arm C (i.e. column position 5):
sort_at_path(pruned, path = c("RACE", "*", "STRATA1"), cont_n_onecol(5))
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80When sorting within an analysis subtable (e.g., the subtable generated when your analysis function generates more than one row per group of data), the name of that subtable (generally the name of the variable being analyzed) must appear in the path, even if the variable label is not displayed when the table is printed.
To show the differences between sorting an analysis subtable (DataRow), and a content subtable (ContentRow), we modify and prune (as before) a similar raw table as before:
more_analysis_fnc <- function(x) {
in_rows(
"median" = median(x),
"mean" = mean(x),
.formats = "xx.x"
)
}
raw_lyt <- basic_table() |>
split_cols_by("ARM") |>
split_rows_by(
"RACE",
split_fun = drop_and_remove_levels("WHITE") # dropping WHITE levels
) |>
summarize_row_groups() |>
split_rows_by("STRATA1") |>
summarize_row_groups() |>
analyze("AGE", afun = more_analysis_fnc)
tbl <- build_table(raw_lyt, DM) |>
prune_table() |>
print()
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————————
# ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%)
# A 27 (22.3%) 20 (18.9%) 31 (24.0%)
# median 30.0 33.0 36.0
# mean 32.2 33.9 36.8
# B 24 (19.8%) 29 (27.4%) 22 (17.1%)
# median 32.5 32.0 34.0
# mean 34.1 31.6 34.7
# C 28 (23.1%) 19 (17.9%) 31 (24.0%)
# median 36.5 34.0 33.0
# mean 36.2 33.0 32.4
# BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%)
# A 6 (5.0%) 7 (6.6%) 8 (6.2%)
# median 32.0 29.0 32.5
# mean 31.5 28.6 33.6
# B 10 (8.3%) 6 (5.7%) 12 (9.3%)
# median 33.0 30.0 33.5
# mean 35.6 30.8 33.7
# C 12 (9.9%) 11 (10.4%) 7 (5.4%)
# median 33.0 36.0 32.0
# mean 35.5 34.2 35.0What should we do now if we want to sort each median and mean in each of the strata variables? We need to write a custom score function as the ready-made ones at the moment work only with content nodes (content_table() access function for cont_n_allcols() and cont_n_onecol(), of which we will talk in a moment). But before that, we need to think about what are we ordering, i.e. we need to specify the right path. We suggest looking at the structure first with table_structure() or row_paths_summary().
table_structure(tbl) # Direct inspection into the tree-like structure of rtables
# [TableTree] RACE
# [TableTree] ASIAN [cont: 1 x 3]
# [TableTree] STRATA1
# [TableTree] A [cont: 1 x 3]
# [ElementaryTable] AGE (2 x 3)
# [TableTree] B [cont: 1 x 3]
# [ElementaryTable] AGE (2 x 3)
# [TableTree] C [cont: 1 x 3]
# [ElementaryTable] AGE (2 x 3)
# [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 3]
# [TableTree] STRATA1
# [TableTree] A [cont: 1 x 3]
# [ElementaryTable] AGE (2 x 3)
# [TableTree] B [cont: 1 x 3]
# [ElementaryTable] AGE (2 x 3)
# [TableTree] C [cont: 1 x 3]
# [ElementaryTable] AGE (2 x 3)We see that to order all of the AGE nodes we need to get there with something like this: RACE, ASIAN, STRATA1, A, AGE and no more as the next level is what we need to sort. But we see now that this path would sort only the first group. We need wildcards: RACE, *, STRATA1, *, AGE.
Now, we have found a way to select relevant paths that we want to sort. We want to construct a scoring function that works on the median and mean and sort them. To do so, we may want to enter our scoring function with browser() to see what is fed to it and try to retrieve the single value that is to be returned to do the sorting. We allow the user to experiment with this, while here we show a possible solution that considers summing all the column values that are retrieved with row_values(tt) from the subtable that is fed to the function itself. Note that any score function should be defined as having a subtable tt as a unique input parameter and a single numeric value as output.
scorefun <- function(tt) {
# Here we could use browser()
sum(unlist(row_values(tt)))
}
sort_at_path(tbl, c("RACE", "*", "STRATA1", "*", "AGE"), scorefun)
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————————
# ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%)
# A 27 (22.3%) 20 (18.9%) 31 (24.0%)
# mean 32.2 33.9 36.8
# median 30.0 33.0 36.0
# B 24 (19.8%) 29 (27.4%) 22 (17.1%)
# mean 34.1 31.6 34.7
# median 32.5 32.0 34.0
# C 28 (23.1%) 19 (17.9%) 31 (24.0%)
# median 36.5 34.0 33.0
# mean 36.2 33.0 32.4
# BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%)
# A 6 (5.0%) 7 (6.6%) 8 (6.2%)
# mean 31.5 28.6 33.6
# median 32.0 29.0 32.5
# B 10 (8.3%) 6 (5.7%) 12 (9.3%)
# mean 35.6 30.8 33.7
# median 33.0 30.0 33.5
# C 12 (9.9%) 11 (10.4%) 7 (5.4%)
# mean 35.5 34.2 35.0
# median 33.0 36.0 32.0To help the user visualize what is happening in the score function we show here an example of its exploration from the debugging:
> sort_at_path(tbl, c("RACE", "*", "STRATA1", "*", "AGE"), scorefun)
Called from: scorefun(x)
Browse[1]> tt ### THIS IS THE LEAF LEVEL -> DataRow ###
[DataRow indent_mod 0]: median 30.0 33.0 36.0
Browse[1]> row_values(tt) ### Extraction of values -> It will be a named list! ###
$`A: Drug X`
[1] 30
$`B: Placebo`
[1] 33
$`C: Combination`
[1] 36
Browse[1]> sum(unlist(row_values(tt))) ### Final value we want to give back to sort_at_path ###
[1] 99We can see how powerful and pragmatic it might be to change the sorting principles from within the custom scoring function. We show this by selecting a specific column to sort. Looking at the pre-defined function cont_n_onecol() gives us an insight into how to proceed.
cont_n_onecol
# function (j)
# {
# function(tt) {
# ctab <- content_table(tt)
# if (NROW(ctab) == 0) {
# stop("cont_n_allcols score function used at subtable [",
# obj_name(tt), "] that has no content table.")
# }
# row_values(tree_children(ctab)[[1]])[[j]][1]
# }
# }
# <bytecode: 0x557217ae34d8>
# <environment: namespace:rtables>We see that a similar function to cont_n_allcols() is wrapped by one that allows a parameter j to be used to select a specific column. We will do the same here for selecting which column we want to sort.
scorefun_onecol <- function(colpath) {
function(tt) {
# Here we could use browser()
unlist(cell_values(tt, colpath = colpath), use.names = FALSE)[1] # Modified to lose the list names
}
}
sort_at_path(tbl, c("RACE", "*", "STRATA1", "*", "AGE"), scorefun_onecol(colpath = c("ARM", "A: Drug X")))
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————————
# ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%)
# A 27 (22.3%) 20 (18.9%) 31 (24.0%)
# mean 32.2 33.9 36.8
# median 30.0 33.0 36.0
# B 24 (19.8%) 29 (27.4%) 22 (17.1%)
# mean 34.1 31.6 34.7
# median 32.5 32.0 34.0
# C 28 (23.1%) 19 (17.9%) 31 (24.0%)
# median 36.5 34.0 33.0
# mean 36.2 33.0 32.4
# BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%)
# A 6 (5.0%) 7 (6.6%) 8 (6.2%)
# median 32.0 29.0 32.5
# mean 31.5 28.6 33.6
# B 10 (8.3%) 6 (5.7%) 12 (9.3%)
# mean 35.6 30.8 33.7
# median 33.0 30.0 33.5
# C 12 (9.9%) 11 (10.4%) 7 (5.4%)
# mean 35.5 34.2 35.0
# median 33.0 36.0 32.0In the above table we see that the mean and median rows are reordered by their values in the first column, compared to the raw table, as desired.
With this function we can also do the same for columns that are nested within larger splits:
# Simpler table
tbl <- basic_table() |>
split_cols_by("ARM") |>
split_cols_by("SEX",
split_fun = drop_and_remove_levels(c("U", "UNDIFFERENTIATED"))
) |>
analyze("AGE", afun = more_analysis_fnc) |>
build_table(DM) |>
prune_table() |>
print()
# A: Drug X B: Placebo C: Combination
# F M F M F M
# —————————————————————————————————————————————————————————
# median 32.0 35.0 33.0 31.0 35.0 32.0
# mean 33.7 36.5 33.8 32.1 34.9 34.3
sort_at_path(tbl, c("AGE"), scorefun_onecol(colpath = c("ARM", "B: Placebo", "SEX", "F")))
# A: Drug X B: Placebo C: Combination
# F M F M F M
# —————————————————————————————————————————————————————————
# mean 33.7 36.5 33.8 32.1 34.9 34.3
# median 32.0 35.0 33.0 31.0 35.0 32.0Pruning criteria and scoring functions map TableTree or TableRow objects to a Boolean value (for pruning criteria) or a sortable scalar value (scoring functions). To do this we currently need to interact with the structure of the objects more than usual. Indeed, we showed already how sorting can be very complicated if the concept of tree-like structure and pathing is not well understood. It is important though to have in mind the following functions that can be used in each pruning or sorting function to retrieve the relevant information from the table.
cell_values() - Retrieves a named list of a TableRow or TableTree object’s values
rowpath and colpath to restrict which cell values are returnedobj_name() - Retrieves the name of an object. Note this can differ from the label that is displayed (if any is) when printing. This will match the element in the path.obj_label() - Retrieves the display label of an object. Note this can differ from the name that appears in the path.content_table() - Retrieves a TableTree object’s content table (which contains its summary rows).tree_children() - Retrieves a TableTree object’s direct children (either subtables, rows or possibly a mix thereof, though that should not happen in practice)In this case, for convenience/simplicity, we use the name of the table element but any logic which returns a single string could be used here.
We sort the ethnicity by alphabetical order (in practice undoing our previous sorting by ethnicity above).
silly_name_scorer <- function(tt) {
nm <- obj_name(tt)
print(nm)
nm
}
sort_at_path(ethsort, "RACE", silly_name_scorer) # Now, it is sorted alphabetically!
# [1] "WHITE"
# [1] "BLACK OR AFRICAN AMERICAN"
# [1] "ASIAN"
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00NOTE: Generally this would be more appropriately done using the reorder_split_levels() function within the layout rather than as a sort post-processing step, but other character scorers may or may not map as easily to layouting directives.
We need the F and M percents, only for Arm C (i.e. columns 5 and 6), differenced.
We will sort the strata within each ethnicity by the percent difference in counts between males and females in arm C.
Note: this is not statistically meaningful at all, and is in fact a terrible idea because it reorders the strata seemingly (but not) at random within each race, but illustrates the various things we need to do inside custom sorting functions.
silly_gender_diffcount <- function(tt) {
## (1st) content row has same name as object (STRATA1 level)
rpath <- c(obj_name(tt), "@content", obj_name(tt))
## the [1] below is cause these are count (pct%) cells
## and we only want the count part!
mcount <- unlist(cell_values(
tt,
rowpath = rpath,
colpath = c("ARM", "C: Combination", "SEX", "M")
))[1]
fcount <- unlist(cell_values(
tt,
rowpath = rpath,
colpath = c("ARM", "C: Combination", "SEX", "F")
))[1]
(mcount - fcount) / fcount
}
sort_at_path(pruned, c("RACE", "*", "STRATA1"), silly_gender_diffcount)
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%)
# B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%)
# Mean 33.75 34.88 32.46 30.94 33.30 35.92
# A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%)
# Mean 30.40 34.42 35.43 30.33 37.40 36.25
# C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%)
# Mean 36.92 35.60 34.00 31.89 33.47 31.38
# BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%)
# C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%)
# Mean 31.33 39.67 34.50 34.00 33.00 36.50
# A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%)
# Mean 31.20 33.00 28.00 30.00 30.75 36.50
# B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%)
# Mean 36.14 34.33 29.67 32.00 36.33 31.00
# WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%)
# A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%)
# Mean 34.00 45.00 29.33 33.33 35.00 32.80
# C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%)
# Mean 35.50 44.00 44.67 NA 38.50 35.00
# B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%)
# Mean 37.00 43.67 48.00 36.75 34.33 36.00