Test for tabular S7 class instances

Class predicates returning a single logical indicating whether x inherits from the corresponding tabular S7 class. Use them to gate user-side code that branches on what a verb has returned, to write defensive helpers that wrap tabular pipelines, or to assert intermediate shapes during pipeline debugging.

Usage

is_tabular_spec(x)

is_tabular_grid(x)

is_col_spec(x)

is_header_node(x)

is_sort_spec(x)

is_style_node(x)

is_style_layer(x)

is_style_spec(x)

is_pagination_spec(x)

is_preset_spec(x)

is_subgroup_spec(x)

is_inline_ast(x)

Arguments

x

Object to test. Any R value. Each predicate returns TRUE if x inherits from the named class, FALSE otherwise.

Value

A single TRUE / FALSE. Use in if / stopifnot guards, or chain into validation helpers.

A length-1 logical — TRUE or FALSE. Never NA.

Details

Twelve predicates cover the full S7 surface:

predicate	tests for	produced by
is_tabular_spec()	tabular_spec	tabular() and every build verb
is_tabular_grid()	tabular_grid	as_grid()
is_col_spec()	col_spec	col_spec()
is_header_node()	header_node	headers() (internal nodes)
is_sort_spec()	sort_spec	sort_rows()
is_style_node()	style_node	style() (per-cell style)
is_style_layer()	style_layer	style() (one per call)
is_style_spec()	style_spec	style() (the cascade root)
is_pagination_spec()	pagination_spec	paginate()
is_preset_spec()	preset_spec	preset(), set_preset()
is_subgroup_spec()	subgroup_spec	subgroup()
is_inline_ast()	inline_ast	parse_inline() (post-format)

Predicates never error — they return FALSE for NULL, vectors, objects of any other class, and S7 objects from other packages. Use them at any layer of a user's pipeline without a defensive tryCatch().

See also

Class definitions: tabular_classes.

Verbs producing each class: tabular(), col_spec(), headers(), sort_rows(), style(), paginate(), preset(), as_grid().

Examples

# ---- Example 1: Gate user-side code on the spec class ----
#
# A user-side helper that pre-validates its input before piping
# into a downstream tabular chain. The predicate returns FALSE
# for any non-spec input without raising, so the helper can emit
# a friendlier error than tabular's own S7 validator would.
add_safety_footnote <- function(spec) {
  if (!is_tabular_spec(spec)) {
    stop("`spec` must be a tabular_spec; build one with tabular().")
  }
  spec
}

demo <- tabular(cdisc_saf_demo, titles = "Demographics")
is_tabular_spec(demo)         # TRUE
#> [1] TRUE
is_tabular_spec("not a spec") # FALSE — does not raise
#> [1] FALSE
add_safety_footnote(demo)
 
Demographics
 

variable
stat_label
placebo
drug_50
drug_100
Total
Age (years)
n
86
96
72
254
Age (years)
Mean (SD)
75.2 (8.59)
76.0 (8.11)
73.8 (7.94)
75.1 (8.25)
Age (years)
Median
76.0
78.0
75.5
77.0
Age (years)
Q1, Q3
69.2, 81.8
71.0, 82.0
70.5, 79.0
70.0, 81.0
Age (years)
Min, Max
52, 89
51, 88
56, 88
51, 89
Sex, n (%)
F
53 (61.6)
55 (57.3)
35 (48.6)
143 (56.3)
Sex, n (%)
M
33 (38.4)
41 (42.7)
37 (51.4)
111 (43.7)
Race, n (%)
WHITE
78 (90.7)
90 (93.8)
62 (86.1)
230 (90.6)
Race, n (%)
BLACK OR AFRICAN AMERICAN
8 (9.3)
6 (6.2)
9 (12.5)
23 (9.1)
Race, n (%)
ASIAN
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
Race, n (%)
AMERICAN INDIAN OR ALASKA NATIVE
0 (0.0)
0 (0.0)
1 (1.4)
1 (0.4)

# ---- Example 2: Assert intermediate shapes during debugging ----
#
# When chaining many verbs, dropping `stopifnot()` between verbs
# gives a clear stack trace if a verb silently returns the wrong
# type. Predicates are cheap (single S7 dispatch each) and never
# error, so they are safe to leave in pipelines during dev.
n <- stats::setNames(cdisc_saf_n$n, cdisc_saf_n$arm_short)

spec <- tabular(
  cdisc_saf_demo,
  titles = c("Table 14.1.1", "Demographics",
             "Safety Population")
) |>
  cols(
    variable   = col_spec(usage = "group", label = "Characteristic"),
    stat_label = col_spec(label = "Statistic"),
    placebo  = col_spec(label = "Placebo\nN={n['placebo']}",  align = "decimal"),
    drug_50  = col_spec(label = "Drug 50\nN={n['drug_50']}",  align = "decimal"),
    drug_100 = col_spec(label = "Drug 100\nN={n['drug_100']}", align = "decimal"),
    Total    = col_spec(label = "Total\nN={n['Total']}",    align = "decimal")
  ) |>
  sort_rows(by = c("variable", "stat_label"))

stopifnot(
  is_tabular_spec(spec),
  is_col_spec(spec@cols[["placebo"]]),
  is_sort_spec(spec@sort)
)

grid <- as_grid(spec)
stopifnot(is_tabular_grid(grid))