Main sorting function to order the substructure of a TableTree
at a particular Path in the table tree.
Arguments
- tt
TableTree(or related class). ATableTreeobject representing a populated table.- path
character. A vector path for a position within the structure of a
tabletree. Each element represents a subsequent choice amongst the children of the previous choice.- scorefun
function. Scoring function, should accept the type of children directly under the position at
path(eitherVTableTree,VTableRow, orVTableNodeInfo, which covers both) and return a numeric value to be sorted.- decreasing
logical(1). Should the the scores generated by
scorefunbe sorted in decreasing order. If unset (the default ofNA), it is set toTRUEif the generated scores are numeric andFALSEif they are characters.- na.pos
character(1). What should be done with children (sub-trees/rows) with
NAscores. Defaults to"omit", which removes them, other allowed values are"last"and"first"which indicate where they should be placed in the order.- .prev_path
character. Internal detail, do not set manually.
Value
A TableTree with the same structure as tt with the exception
that the requested sorting has been done at path.
Details
The path here can include the "wildcard" "*" as a step,
which translates roughly to any node/branching element and means
that each child at that step will be separately sorted based on
scorefun and the remaining path entries. This can occur
multiple times in a path.
Note that sorting needs a deeper understanding of table structure in
rtables. Please consider reading related vignette
(Sorting and Pruning)
and explore table structure with useful functions like table_structure()
and row_paths_summary(). It is also very important to understand the
difference between "content" rows and "data" rows. The first one analyzes
and describes the split variable generally and is generated with
summarize_row_groups(), while the second one is commonly produced by
calling one of the various analyze() instances.
Built-in score functions are cont_n_allcols() and cont_n_onecol().
They are both working with content rows (coming from summarize_row_groups())
while a custom score function needs to be used on DataRows. Here, some
useful descriptor and accessor functions (coming from related vignette):
cell_values()- Retrieves a named list of aTableRoworTableTreeobject's values.obj_name()- Retrieves the name of an object. Note this can differ from the label that is displayed (if any is) when printing.obj_label()- Retrieves the display label of an object. Note this can differ from the name that appears in the path.content_table()- Retrieves aTableTreeobject's content table (which contains its summary rows).tree_children()- Retrieves aTableTreeobject's direct children (either subtables, rows or possibly a mix thereof, though that should not happen in practice).
Examples
# Creating a table to sort
# Function that gives two statistics per table-tree "leaf"
more_analysis_fnc <- function(x) {
in_rows(
"median" = median(x),
"mean" = mean(x),
.formats = "xx.x"
)
}
# Main layout of the table
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)
# Creating the table and pruning empty and NAs
tbl <- build_table(raw_lyt, DM) %>%
prune_table()
# Peek at the table structure to understand how it is built
table_structure(tbl)
#> [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)
# Sorting only ASIAN sub-table, or, in other words, sorting STRATA elements for
# the ASIAN group/row-split. This uses content_table() accessor function as it
# is a "ContentRow". In this case, we also base our sorting only on the second column.
sort_at_path(tbl, c("ASIAN", "STRATA1"), cont_n_onecol(2))
#> A: Drug X B: Placebo C: Combination
#> ————————————————————————————————————————————————————————————————————
#> ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%)
#> B 24 (19.8%) 29 (27.4%) 22 (17.1%)
#> median 32.5 32.0 34.0
#> mean 34.1 31.6 34.7
#> A 27 (22.3%) 20 (18.9%) 31 (24.0%)
#> median 30.0 33.0 36.0
#> mean 32.2 33.9 36.8
#> 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 35.0
# Custom scoring function that is working on "DataRow"s
scorefun <- function(tt) {
# Here we could use browser()
sum(unlist(row_values(tt))) # Different accessor function
}
# Sorting mean and median for all the AGE leaves!
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 32.0