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. 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.
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: 0x5629c4990728>
# <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 BUnlike other uses of pathing (currently), a sorting path can contain
“*“. This indicates that the children of each subtable matching he
* 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: 0x5629c77506d0>
# <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