all_zero_or_na | R Documentation |
Criteria functions (and constructors thereof) for trimming and pruning tables.
all_zero_or_na(tr)
all_zero(tr)
content_all_zeros_nas(tt, criteria = all_zero_or_na)
prune_empty_level(tt)
prune_zeros_only(tt)
low_obs_pruner(min, type = c("sum", "mean"))
tr |
( |
tt |
( |
criteria |
( |
min |
( |
type |
( |
all_zero_or_na
returns TRUE
(and thus indicates trimming/pruning) for any non-LabelRow
TableRow
which contain only any mix of NA
(including NaN
), 0
, Inf
and -Inf
values.
all_zero
returns TRUE
for any non-LabelRow
which contains only (non-missing) zero values.
content_all_zeros_nas
prunes a subtable if both of the following are true:
It has a content table with exactly one row in it.
all_zero_or_na
returns TRUE
for that single content row. In practice, when the default summary/content
function is used, this represents pruning any subtable which corresponds to an empty set of the input data
(e.g. because a factor variable was used in split_rows_by()
but not all levels were present in the data).
prune_empty_level
combines all_zero_or_na
behavior for TableRow
objects, content_all_zeros_nas
on
content_table(tt)
for TableTree
objects, and an additional check that returns TRUE
if the tt
has no
children.
prune_zeros_only
behaves as prune_empty_level
does, except that like all_zero
it prunes
only in the case of all non-missing zero values.
low_obs_pruner
is a constructor function which, when called, returns a pruning criteria function which
will prune on content rows by comparing sum or mean (dictated by type
) of the count portions of the cell
values (defined as the first value per cell regardless of how many values per cell there are) against min
.
A logical value indicating whether tr
should be included (TRUE
) or pruned (FALSE
) during pruning.
prune_table()
, trim_rows()
adsl <- ex_adsl
levels(adsl$SEX) <- c(levels(ex_adsl$SEX), "OTHER")
adsl$AGE[adsl$SEX == "UNDIFFERENTIATED"] <- 0
adsl$BMRKR1 <- 0
tbl_to_prune <- basic_table() %>%
analyze("BMRKR1") %>%
split_cols_by("ARM") %>%
split_rows_by("SEX") %>%
summarize_row_groups() %>%
split_rows_by("STRATA1") %>%
summarize_row_groups() %>%
analyze("AGE") %>%
build_table(adsl)
tbl_to_prune %>% prune_table(all_zero_or_na)
tbl_to_prune %>% prune_table(all_zero)
tbl_to_prune %>% prune_table(content_all_zeros_nas)
tbl_to_prune %>% prune_table(prune_empty_level)
tbl_to_prune %>% prune_table(prune_zeros_only)
min_prune <- low_obs_pruner(70, "sum")
tbl_to_prune %>% prune_table(min_prune)
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