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Cumulative counts of numeric variable by thresholds

Source: R/count_cumulative.R
count_cumulative.Rd

[Stable]

The analyze function count_cumulative() creates a layout element to calculate cumulative counts of values in a numeric variable that are less than, less or equal to, greater than, or greater or equal to user-specified threshold values.

This function analyzes numeric variable vars against the threshold values supplied to the thresholds argument as a numeric vector. Whether counts should include the threshold values, and whether to count values lower or higher than the threshold values can be set via the include_eq and lower_tail parameters, respectively.

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 is TRUE.

include_eq

(flag)
whether to include value equal to the threshold in count, default is TRUE.

var_labels

(character)
variable labels.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

na_str

(string)
string used to replace all NA or 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 same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table. Run get_stats("count_cumulative") to see available statistics for this function.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for 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 by rtables.

Value

  • count_cumulative() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout.

  • s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction.

Functions

  • count_cumulative(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::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 as afun in count_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%)