Counting Specific Values

Usage

s_count_values(
  x,
  values,
  na.rm = TRUE,
  .N_col,
  .N_row,
  denom = c("n", "N_row", "N_col")
)

# S3 method for character
s_count_values(x, values = "Y", na.rm = TRUE, ...)

# S3 method for factor
s_count_values(x, values = "Y", ...)

# S3 method for logical
s_count_values(x, values = TRUE, ...)

a_count_values(
  x,
  values,
  na.rm = TRUE,
  .N_col,
  .N_row,
  denom = c("n", "N_row", "N_col")
)

count_values(
  lyt,
  vars,
  values,
  ...,
  table_names = vars,
  .stats = "count_fraction",
  .formats = NULL,
  .labels = c(count_fraction = paste(values, collapse = ", ")),
  .indent_mods = NULL
)

Arguments

x

(numeric)
vector of numbers we want to analyze.

values

(character)
specific values that should be counted.

na.rm

(flag)
whether NA values should be removed from x prior to analysis.

.N_col

(count)
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is passed by rtables.

.N_row

(count)
column-wise N (column count) for the full column that is passed by rtables.

denom

(string)
choice of denominator for proportion. Options are:

• n: number of values in this row and column intersection.

• N_row: total number of values in this row across columns.

• N_col: total number of values in this column across rows.

...

additional arguments for the lower level functions.

lyt

(layout)
input layout where analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

table_names

(character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels.

Value

See s_summary.logical() for the returned statistics, as this is used inside.

Functions

• s_count_values(): Statistics Function which is a generic function to count values.

• s_count_values(character): Method for character vectors x.

• s_count_values(factor): method for factor vectors x. This checks whether values are all included in the levels of x and fails otherwise. It then proceeds by converting to character and calling s_count_values.character.

• s_count_values(logical): method for logical vectors x.

• a_count_values(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

• count_values(): Analyze Function which adds the counting analysis to the input layout. Note that additional formatting arguments can be used here.

Note

Variable labels are shown when there is more than one element in vars, otherwise they are hidden.

Examples

# `s_count_values.character`
s_count_values(x = c("a", "b", "a"), values = "a")
#> $n
#> [1] 3
#> 
#> $count
#> [1] 2
#> 
#> $count_fraction
#> [1] 2.0000000 0.6666667
#> 
#> $n_blq
#> [1] 0
#> 
s_count_values(x = c("a", "b", "a", NA, NA), values = "b", na.rm = FALSE)
#> $n
#> [1] 5
#> 
#> $count
#> [1] 1
#> 
#> $count_fraction
#> [1] 1.0 0.2
#> 
#> $n_blq
#> [1] 0
#> 
# `s_count_values.factor`
s_count_values(x = factor(c("a", "b", "a")), values = "a")
#> $n
#> [1] 3
#> 
#> $count
#> [1] 2
#> 
#> $count_fraction
#> [1] 2.0000000 0.6666667
#> 
#> $n_blq
#> [1] 0
#> 
# `s_count_values.logical`
s_count_values(x = c(TRUE, FALSE, TRUE))
#> $n
#> [1] 3
#> 
#> $count
#> [1] 2
#> 
#> $count_fraction
#> [1] 2.0000000 0.6666667
#> 
#> $n_blq
#> [1] 0
#> 
# `a_count_values`
a_count_values(x = factor(c("a", "b", "a")), values = "a", .N_col = 10, .N_row = 10)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>         row_name formatted_cell indent_mod      row_label
#> 1              n              3          0              n
#> 2          count              2          0          count
#> 3 count_fraction     2 (66.67%)          0 count_fraction
#> 4          n_blq              0          0          n_blq
# `count_values`
basic_table() %>%
  count_values("Species", values = "setosa") %>%
  build_table(iris)
#>            all obs  
#> ————————————————————
#> setosa   50 (33.33%)