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Generate a result data frame

Source: R/tt_as_df.R
data.frame_export.Rd

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,
  add_tbl_name_split = FALSE,
  simplify = FALSE,
  verbose = FALSE,
  ...
)

path_enriched_df(tt, path_fun = collapse_path, value_fun = collapse_values)

Arguments

tt

(TableTree or related class)
a TableTree object representing a populated table.

spec

(function)
function that generates the result data frame from a table (TableTree). It defaults to NULL, 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)
when TRUE, the result data frame will have only one statistic per row.

expand_colnames

(flag)
when TRUE, 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)
when TRUE, the result data frame will have all labels as they appear in the final table.

add_tbl_name_split

(flag)
when TRUE and when the table has more than one analyze(table_names = "<diff_names>"), the table names will be present as a group split named "<analysis_spl_tbl_name>".

simplify

(flag)
when TRUE, the result data frame will have only visible labels and result columns. Consider showing also label rows with keep_label_rows = TRUE. This output can be used again to create a TableTree object with df_to_tt().

verbose

(flag)
when TRUE, the function will print additional information for data_format != "full_precision".

...

additional arguments passed to spec-specific result data frame function (spec). When using make_ard = TRUE, it is possible to turn off the extraction of the exact string decimals printed by the table with add_tbl_str_decimals = FALSE.

path_fun

(function)
function to transform paths into single-string row/column names.

value_fun

(function)
function to transform cell values into cells of a data.frame. Defaults to collapse_values, which creates strings where multi-valued cells are collapsed together, separated by |.

Value

  • as_result_df returns a result data.frame.

  • path_enriched_df() returns a data.frame of tt's cell values (processed by value_fun, with columns named by the full column paths (processed by path_fun and an additional row_path column with the row paths (processed by path_fun).

Functions

  • path_enriched_df(): Transform a TableTree object to a path-enriched data.frame.

Note

When parent_name is used when constructing a layout to directly control the name of subtables in a table, that will be reflected in the 'group' values returned in the result dataframe/ard. When automatic de-duplication of sibling names is performed by rtables, that is automatically undone during the result df creation process, so the group values will be as if the relevant siblings had identical names.

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