| 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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