TableTree objects are based on a tree data structure as
the name indicates. The package is written such that the user does not
need to walk trees for many basic table manipulations. Walking trees
will still be necessary for certain manipulation and will be the subject
of a different vignette.
In this vignette we show some methods to subset tables and to extract cell values.
We will use the following table for illustrative purposes:
library(rtables)
library(dplyr)
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("SEX", split_fun = drop_split_levels) %>%
analyze(c("AGE", "STRATA1"))
tbl <- build_table(lyt, ex_adsl %>% filter(SEX %in% c("M", "F")))
tbl# A: Drug X B: Placebo C: Combination
# ———————————————————————————————————————————————————
# F
# AGE
# Mean 32.76 34.12 35.20
# STRATA1
# A 21 24 18
# B 25 27 21
# C 33 26 27
# M
# AGE
# Mean 35.57 37.44 35.38
# STRATA1
# A 16 19 20
# B 21 17 21
# C 14 19 19[The [ and [<- accessor functions operate
largely the same as their data.frame cousins:
[ and [<- both treat Tables as
rectangular objects (rather than trees)
[ accepts both column and row absolute position, and
missing arguments mean “all indexes in that dimension”
[.data.frame they cannot be mixed with positive ones[ always returns the same class as the object being
subset unless drop = TRUE[ , drop = TRUE returns the raw (possibly
multi-element) value associated with the cell.Known Differences from [.data.frame -
absolute position cannot currently be used to reorder columns or rows.
Note in general the result of such an ordering is unlikely to be
structurally valid. To change the order of values, please read sorting
and pruning vignette or relevant function
(sort_at_path()). - character indices are
treated as paths, not vectors of names in both [ and
[<-
The [ accessor function always returns an
TableTree object if drop=TRUE is not set. The
first argument are the row indices and the second argument the column
indices. Alternatively logical subsetting can be used. The indices are
based on visible rows and not on the tree structure. So:
# A: Drug X
# —————————————
# Fis a table with an empty cell because the first row is a label row. We need to access a cell with actual cell data:
# A: Drug X
# ————————————————
# Mean 32.76To retrieve the value, we use drop = TRUE:
# [1] 32.75949One can access multiple rows and columns:
# A: Drug X B: Placebo
# —————————————————————————————————
# F
# AGE
# Mean 32.76 34.12Note that we do not repeat label rows for descending children, e.g.
# A: Drug X B: Placebo C: Combination
# —————————————————————————————————————————————————
# AGE
# Mean 32.76 34.12 35.20
# STRATA1does not show that the first row is derived from AGE. In
order to repeat content/label information, one should use the pagination
feature. Please read the related vignette.
Character indices are interpreted as paths (see below), NOT elements
to be matched against names(tbl):
# A: Drug X
# —————————————————————
# F
# AGE
# Mean 32.76
# STRATA1
# A 21
# B 25
# C 33
# M
# AGE
# Mean 35.57
# STRATA1
# A 16
# B 21
# C 14As standard no additional information is kept after subsetting. Here, we show with a more complete table how it is still possible to keep the (possibly) relevant information.
top_left(tbl) <- "SEX"
main_title(tbl) <- "Table 1"
subtitles(tbl) <- c("Authors:", " - Abcd Zabcd", " - Cde Zbcd")
main_footer(tbl) <- "Please regard this table as an example of smart subsetting"
prov_footer(tbl) <- "Do remember where you read this though"
fnotes_at_path(tbl, rowpath = c("M", "AGE", "Mean"), colpath = c("ARM", "A: Drug X")) <- "Very important mean"Normal subsetting loses all the information showed above.
# C: Combination
# —————————————————————
# Mean 35.20If all the rows are kept, top left information is also kept. This can
be also imposed by adding keep_topleft = TRUE to the
subsetting as follows:
# SEX B: Placebo C: Combination
# ———————————————————————————————————————
# F
# AGE
# Mean 34.12 35.20
# STRATA1
# A 24 18
# B 27 21
# C 26 27
# M
# AGE
# Mean 37.44 35.38
# STRATA1
# A 19 20
# B 17 21
# C 19 19# SEX C: Combination
# —————————————————————————
# F
# AGE
# Mean 35.20If the referenced entry is present in the subsetting, also the referential footnote will appear. Please consider reading relevant vignette about referential footnotes. In case of subsetting, the referential footnotes are by default indexed again, as if the produced table is a new one.
# A: Drug X
# ————————————————
# Mean 35.57 {1}
# ————————————————
#
# {1} - Very important mean
# ————————————————# label path
# —————————————————————————————————————
# A: Drug X ARM, A: Drug X
# B: Placebo ARM, B: Placebo
# C: Combination ARM, C: Combination# rowname node_class path
# —————————————————————————————————————————————
# F LabelRow SEX, F
# AGE LabelRow SEX, F, AGE
# Mean DataRow SEX, F, AGE, Mean
# STRATA1 LabelRow SEX, F, STRATA1
# A DataRow SEX, F, STRATA1, A
# B DataRow SEX, F, STRATA1, B
# C DataRow SEX, F, STRATA1, C
# M LabelRow SEX, M
# AGE LabelRow SEX, M, AGE
# Mean DataRow SEX, M, AGE, Mean
# STRATA1 LabelRow SEX, M, STRATA1
# A DataRow SEX, M, STRATA1, A
# B DataRow SEX, M, STRATA1, B
# C DataRow SEX, M, STRATA1, C# To select column value, use `NULL` for `rowpath`
fnotes_at_path(tbl, rowpath = NULL, colpath = c("ARM", "A: Drug X")) <- "Interesting"
tbl[3, 1]# A: Drug X {1}
# ————————————————————
# Mean 32.76
# ————————————————————
#
# {1} - Interesting
# ————————————————————# reindexing of {2} as {1}
fnotes_at_path(tbl, rowpath = c("M", "AGE", "Mean"), colpath = NULL) <- "THIS mean"
tbl # {1}, {2}, and {3} are present# Table 1
# Authors:
# - Abcd Zabcd
# - Cde Zbcd
#
# ——————————————————————————————————————————————————————————
# SEX A: Drug X {1} B: Placebo C: Combination
# ——————————————————————————————————————————————————————————
# F
# AGE
# Mean 32.76 34.12 35.20
# STRATA1
# A 21 24 18
# B 25 27 21
# C 33 26 27
# M
# AGE
# Mean {2} 35.57 {3} 37.44 35.38
# STRATA1
# A 16 19 20
# B 21 17 21
# C 14 19 19
# ——————————————————————————————————————————————————————————
#
# {1} - Interesting
# {2} - THIS mean
# {3} - Very important mean
# ——————————————————————————————————————————————————————————
#
# Please regard this table as an example of smart subsetting
#
# Do remember where you read this though# B: Placebo
# —————————————————————
# Mean {1} 37.44
# —————————————————————
#
# {1} - THIS mean
# —————————————————————Similar to what we have used to keep top left information, we can specify to keep more information from the original table. As a standard the foot notes are always present if the titles are kept.
# Table 1
# Authors:
# - Abcd Zabcd
# - Cde Zbcd
#
# ——————————————————————————————————————
# B: Placebo C: Combination
# ——————————————————————————————————————
# F
# AGE
# Mean 34.12 35.20
# ——————————————————————————————————————
#
# Please regard this table as an example of smart subsetting
#
# Do remember where you read this though# B: Placebo C: Combination
# ——————————————————————————————————————
# F
# AGE
# Mean 34.12 35.20
# ——————————————————————————————————————
#
# Please regard this table as an example of smart subsetting
#
# Do remember where you read this though# Referential footnotes are not influenced by `keep_footers = FALSE`
tbl[1:3, keep_titles = TRUE, keep_footers = FALSE]# Table 1
# Authors:
# - Abcd Zabcd
# - Cde Zbcd
#
# ——————————————————————————————————————————————————————
# A: Drug X {1} B: Placebo C: Combination
# ——————————————————————————————————————————————————————
# F
# AGE
# Mean 32.76 34.12 35.20
# ——————————————————————————————————————————————————————
#
# {1} - Interesting
# ——————————————————————————————————————————————————————Tables can be subset or modified in a structurally aware manner via pathing.
Paths define semantically meaningful positions within a constructed table that correspond to the logic of the layout used to create it.
A path is an ordered set of split names, the names of subgroups
generated by the split, and the @content directive, which
steps into a position’s content (or row group summary) table.
We can see the row and column paths of an existing table via the
row_paths(), col_paths(),
row_paths_summary(), and col_paths_summary(),
functions, or as a portion of the more general
make_row_df() function output.
lyt2 <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by("SEX", split_fun = drop_split_levels) %>%
split_rows_by("RACE", split_fun = drop_split_levels) %>%
summarize_row_groups() %>%
analyze(c("AGE", "STRATA1"))
tbl2 <- build_table(lyt2, ex_adsl %>% filter(SEX %in% c("M", "F") & RACE %in% (levels(RACE)[1:3])))
tbl2# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%)
# AGE
# Mean 31.22 34.60 35.06 38.63 36.44 37.66
# STRATA1
# A 11 10 14 10 11 7
# B 11 9 15 7 11 14
# C 19 6 7 13 17 11
# BLACK OR AFRICAN AMERICAN 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%)
# AGE
# Mean 34.06 34.58 33.88 36.33 33.21 34.21
# STRATA1
# A 5 2 5 6 3 7
# B 6 5 3 4 4 4
# C 7 5 8 2 7 3
# WHITE 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%)
# AGE
# Mean 34.12 40.00 32.41 34.62 33.00 30.80
# STRATA1
# A 5 3 3 3 3 5
# B 5 4 8 4 5 2
# C 7 2 6 1 3 3So the column paths are as follows:
# label path
# —————————————————————————————————————————————
# A: Drug X ARM, A: Drug X
# F ARM, A: Drug X, SEX, F
# M ARM, A: Drug X, SEX, M
# B: Placebo ARM, B: Placebo
# F ARM, B: Placebo, SEX, F
# M ARM, B: Placebo, SEX, M
# C: Combination ARM, C: Combination
# F ARM, C: Combination, SEX, F
# M ARM, C: Combination, SEX, Mand the row paths are as follows:
# rowname node_class path
# ———————————————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN ContentRow RACE, ASIAN, @content, ASIAN
# AGE LabelRow RACE, ASIAN, AGE
# Mean DataRow RACE, ASIAN, AGE, Mean
# STRATA1 LabelRow RACE, ASIAN, STRATA1
# A DataRow RACE, ASIAN, STRATA1, A
# B DataRow RACE, ASIAN, STRATA1, B
# C DataRow RACE, ASIAN, STRATA1, C
# BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN
# AGE LabelRow RACE, BLACK OR AFRICAN AMERICAN, AGE
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean
# STRATA1 LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1
# A DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A
# B DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B
# C DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C
# WHITE ContentRow RACE, WHITE, @content, WHITE
# AGE LabelRow RACE, WHITE, AGE
# Mean DataRow RACE, WHITE, AGE, Mean
# STRATA1 LabelRow RACE, WHITE, STRATA1
# A DataRow RACE, WHITE, STRATA1, A
# B DataRow RACE, WHITE, STRATA1, B
# C DataRow RACE, WHITE, STRATA1, CTo get a semantically meaningful subset of our table, then, we can
use [ (or tt_at_path() which underlies it)
# C: Combination
# F M
# ———————————————————————————————————
# ASIAN 39 (60.9%) 32 (57.1%)
# AGE
# Mean 36.44 37.66
# STRATA1
# A 11 7
# B 11 14
# C 17 11We can also retrieve individual cell-values via the
value_at() convenience function, which takes a pair of row
and column paths which resolve together to an individual cell,
e.g. average age for Asian female patients in arm A:
# [1] 31.21951You can also request information from non-cell specific paths with
the cell_values() function:
# $`A: Drug X.F`
# [1] 31.21951
#
# $`A: Drug X.M`
# [1] 34.6Note the return value of cell_values() is always a list
even if you specify a path to a cell:
# $`A: Drug X.F`
# [1] 31.21951