Summarize cumulative counts of a numeric vector that is less than, less or equal to,
greater than, or greater or equal to user-specific thresholds.
Usage
count_cumulative(
  lyt,
  vars,
  thresholds,
  lower_tail = TRUE,
  include_eq = TRUE,
  var_labels = vars,
  show_labels = "visible",
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
s_count_cumulative(
  x,
  thresholds,
  lower_tail = TRUE,
  include_eq = TRUE,
  .N_col,
  ...
)
a_count_cumulative(
  x,
  thresholds,
  lower_tail = TRUE,
  include_eq = TRUE,
  .N_col,
  ...
)Arguments
- lyt
 (
PreDataTableLayouts)
layout that analyses will be added to.- vars
 (
character)
variable names for the primary analysis variable to be iterated over.- thresholds
 (
numeric)
vector of cutoff values for the counts.- lower_tail
 (
flag)
whether to count lower tail, default isTRUE.- include_eq
 (
flag)
whether to include value equal to thethresholdin count, default isTRUE.- var_labels
 (
character)
variable labels.- show_labels
 (
string)
label visibility: one of "default", "visible" and "hidden".- na_str
 (
string)
string used to replace allNAor empty values in the output.- nested
 (
flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.- ...
 additional arguments for the lower level functions.
- table_names
 (
character)
this can be customized in the case that the samevarsare analyzed multiple times, to avoid warnings fromrtables.- .stats
 (
character)
statistics to select for the table. Runget_stats("count_cumulative")to see available statistics for this function.- .formats
 (named
characterorlist)
formats for the statistics. See Details inanalyze_varsfor more information on the"auto"setting.- .labels
 (named
character)
labels for the statistics (without indent).- .indent_mods
 (named
integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.- x
 (
numeric)
vector of numbers we want to analyze.- .N_col
 (
integer(1))
column-wise N (column count) for the full column being analyzed that is typically passed byrtables.
Value
count_cumulative()returns a layout object suitable for passing to further layouting functions, or tortables::build_table(). Adding this function to anrtablelayout will add formatted rows containing the statistics froms_count_cumulative()to the table layout.
s_count_cumulative()returns a named list ofcount_fractions: a list with eachthresholdsvalue as a component, each component containing a vector for the count and fraction.
a_count_cumulative()returns the corresponding list with formattedrtables::CellValue().
Functions
count_cumulative(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper forrtables::analyze().s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds.a_count_cumulative(): Formatted analysis function which is used asafunincount_cumulative().
See also
Relevant helper function h_count_cumulative(), and descriptive function d_count_cumulative().
Examples
basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_cumulative(
    vars = "AGE",
    thresholds = c(40, 60)
  ) %>%
  build_table(tern_ex_adsl)
#>           A: Drug X    B: Placebo   C: Combination
#>             (N=69)       (N=73)         (N=58)    
#> ——————————————————————————————————————————————————
#> AGE                                               
#>   <= 40   52 (75.4%)   58 (79.5%)     41 (70.7%)  
#>   <= 60   69 (100%)    73 (100%)      58 (100%)   
