make_afun {rtables}

Create a custom analysis function wrapping an existing function

Description

Create a custom analysis function wrapping an existing function

Usage

make_afun(
  fun,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  .ungroup_stats = NULL,
  .format_na_strs = NULL,
  ...,
  .null_ref_cells = ".in_ref_col" %in% names(formals(fun))
)

Arguments

fun

(function)
the function to be wrapped in a new customized analysis function. fun should return a named list.

.stats

(character)
names of elements to keep from fun's full output.

.formats

(ANY)
vector or list of formats to override any defaults applied by fun.

.labels

(character)
vector of labels to override defaults returned by fun.

.indent_mods

(integer)
named vector of indent modifiers for the generated rows.

.ungroup_stats

(character)
vector of names, which must match elements of .stats.

.format_na_strs

(ANY)
vector/list of NA strings to override any defaults applied by fun.

...

additional arguments to fun which effectively become new defaults. These can still be overridden by extra_args within a split.

.null_ref_cells

(flag)
whether cells for the reference column should be NULL-ed by the returned analysis function. Defaults to TRUE if fun accepts .in_ref_col as a formal argument. Note this argument occurs after ... so it must be fully specified by name when set.

Value

A function suitable for use in analyze() with element selection, reformatting, and relabeling performed automatically.

Note

Setting .ungroup_stats to non-NULL changes the structure of the value(s) returned by fun, rather than just labeling (.labels), formatting (.formats), and selecting amongst (.stats) them. This means that subsequent make_afun calls to customize the output further both can and must operate on the new structure, not the original structure returned by fun. See the final pair of examples below.

See Also

analyze()

Examples

s_summary <- function(x) {
  stopifnot(is.numeric(x))

  list(
    n = sum(!is.na(x)),
    mean_sd = c(mean = mean(x), sd = sd(x)),
    min_max = range(x)
  )
}

s_summary(iris$Sepal.Length)

a_summary <- make_afun(
  fun = s_summary,
  .formats = c(n = "xx", mean_sd = "xx.xx (xx.xx)", min_max = "xx.xx - xx.xx"),
  .labels = c(n = "n", mean_sd = "Mean (sd)", min_max = "min - max")
)

a_summary(x = iris$Sepal.Length)

a_summary2 <- make_afun(a_summary, .stats = c("n", "mean_sd"))

a_summary2(x = iris$Sepal.Length)

a_summary3 <- make_afun(a_summary, .formats = c(mean_sd = "(xx.xxx, xx.xxx)"))

s_foo <- function(df, .N_col, a = 1, b = 2) {
  list(
    nrow_df = nrow(df),
    .N_col = .N_col,
    a = a,
    b = b
  )
}

s_foo(iris, 40)

a_foo <- make_afun(s_foo,
  b = 4,
  .formats = c(nrow_df = "xx.xx", ".N_col" = "xx.", a = "xx", b = "xx.x"),
  .labels = c(
    nrow_df = "Nrow df",
    ".N_col" = "n in cols", a = "a value", b = "b value"
  ),
  .indent_mods = c(nrow_df = 2L, a = 1L)
)

a_foo(iris, .N_col = 40)
a_foo2 <- make_afun(a_foo, .labels = c(nrow_df = "Number of Rows"))
a_foo2(iris, .N_col = 40)

# grouping and further customization
s_grp <- function(df, .N_col, a = 1, b = 2) {
  list(
    nrow_df = nrow(df),
    .N_col = .N_col,
    letters = list(
      a = a,
      b = b
    )
  )
}
a_grp <- make_afun(s_grp,
  b = 3,
  .labels = c(
    nrow_df = "row count",
    .N_col = "count in column"
  ),
  .formats = c(nrow_df = "xx.", .N_col = "xx."),
  .indent_mods = c(letters = 1L),
  .ungroup_stats = "letters"
)
a_grp(iris, 40)
a_aftergrp <- make_afun(a_grp,
  .stats = c("nrow_df", "b"),
  .formats = c(b = "xx.")
)
a_aftergrp(iris, 40)

s_ref <- function(x, .in_ref_col, .ref_group) {
  list(
    mean_diff = mean(x) - mean(.ref_group)
  )
}

a_ref <- make_afun(s_ref,
  .labels = c(mean_diff = "Mean Difference from Ref")
)
a_ref(iris$Sepal.Length, .in_ref_col = TRUE, 1:10)
a_ref(iris$Sepal.Length, .in_ref_col = FALSE, 1:10)