Collection of utilities to extract data.frame
objects from TableTree
objects.
Usage
as_result_df(
tt,
spec = NULL,
data_format = c("full_precision", "strings", "numeric"),
make_ard = FALSE,
expand_colnames = FALSE,
keep_label_rows = FALSE,
simplify = FALSE,
...
)
path_enriched_df(tt, path_fun = collapse_path, value_fun = collapse_values)
Arguments
- tt
(
TableTree
or related class)
aTableTree
object representing a populated table.- spec
(
function
)
function that generates the result data frame from a table (TableTree
). It defaults toNULL
, for standard processing.- data_format
(
string
)
the format of the data in the result data frame. It can be one value between"full_precision"
(default),"strings"
, and"numeric"
. The last two values show the numeric data with the visible precision.- make_ard
(
flag
)
whenTRUE
, the result data frame will have only one statistic per row.- expand_colnames
(
flag
)
whenTRUE
, the result data frame will have expanded column names above the usual output. This is useful when the result data frame is used for further processing.- keep_label_rows
(
flag
)
whenTRUE
, the result data frame will have all labels as they appear in the final table.- simplify
(
flag
)
whenTRUE
, the result data frame will have only visible labels and result columns. Consider showing also label rows withkeep_label_rows = TRUE
. This output can be used again to create aTableTree
object withdf_to_tt()
.- ...
additional arguments passed to spec-specific result data frame function (
spec
).- path_fun
(
function
)
function to transform paths into single-string row/column names.- value_fun
(
function
)
function to transform cell values into cells of adata.frame
. Defaults tocollapse_values
, which creates strings where multi-valued cells are collapsed together, separated by|
.
Value
as_result_df
returns a resultdata.frame
.
path_enriched_df()
returns adata.frame
oftt
's cell values (processed byvalue_fun
, with columns named by the full column paths (processed bypath_fun
and an additionalrow_path
column with the row paths (processed bypath_fun
).
See also
df_to_tt()
when using simplify = TRUE
and formatters::make_row_df()
to have a
comprehensive view of the hierarchical structure of the rows.
Examples
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("STRATA1") %>%
analyze(c("AGE", "BMRKR2"))
tbl <- build_table(lyt, ex_adsl)
as_result_df(tbl, simplify = TRUE)
#> label_name A: Drug X B: Placebo C: Combination
#> 1 Mean 33.07895 35.11364 34.225
#> 2 LOW 12 16 14
#> 3 MEDIUM 10 17 13
#> 4 HIGH 16 11 13
#> 5 Mean 33.85106 36 36.32558
#> 6 LOW 19 13 10
#> 7 MEDIUM 13 22 16
#> 8 HIGH 15 10 17
#> 9 Mean 34.22449 35.17778 35.63265
#> 10 LOW 19 16 16
#> 11 MEDIUM 14 17 13
#> 12 HIGH 16 12 20
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
analyze(c("AGE", "BMRKR2"))
tbl <- build_table(lyt, ex_adsl)
path_enriched_df(tbl)
#> row_path ARM|A: Drug X ARM|B: Placebo ARM|C: Combination
#> 1 ma_AGE_BMRKR2|AGE|Mean 33.76866 35.43284 35.43182
#> 2 ma_AGE_BMRKR2|BMRKR2|LOW 50.00000 45.00000 40.00000
#> 3 ma_AGE_BMRKR2|BMRKR2|MEDIUM 37.00000 56.00000 42.00000
#> 4 ma_AGE_BMRKR2|BMRKR2|HIGH 47.00000 33.00000 50.00000