The tab_stub_indent() function

Using a subset of the photolysis dataset within a gt table, we can provide some indentation to all of the row labels in the stub via tab_stub_indent(). Here we provide an indent value of 3 for a very prominent indentation that clearly shows that the row labels are subordinate to the two row group labels in this table ("inorganic reactions" and "carbonyls").

photolysis |>
  dplyr::select(cmpd_name, products, type, l, m, n) |>
  dplyr::slice_head(n = 10) |>
  gt(groupname_col = "type", rowname_col = "cmpd_name") |>
  fmt_chem(columns = products) |>
  fmt_scientific(columns = l) |>
  tab_stub_indent(
    rows = everything(),
    indent = 3
  )

	products	l	m	n
inorganic reactions
ozone	O(1  D) + O2	6.07 × 10−5	1.743	0.474
ozone	O(3  P) + O2	4.78 × 10−4	0.298	0.080
hydrogen peroxide	OH + OH	1.04 × 10−5	0.723	0.279
nitrogen dioxide	NO + O(3  P)	1.17 × 10−2	0.244	0.267
nitrate radical	NO + O2	2.49 × 10−2	0.168	0.108
nitrate radical	NO2 + O(3  P)	1.75 × 10−1	0.155	0.125
nitrous acid	OH + NO	2.64 × 10−3	0.261	0.288
nitric acid	OH + NO2	9.31 × 10−7	1.230	0.307
carbonyls
formaldeyde	HCO + H	4.64 × 10−5	0.762	0.353
formaldeyde	CO + H2	6.85 × 10−5	0.477	0.323

Let’s use a summarized version of the pizzaplace dataset to create a another gt table with row groups and row labels. With summary_rows(), we’ll generate summary rows at the top of each row group. Using tab_stub_indent() we can add indentation to the row labels in the stub.

pizzaplace |>
  dplyr::group_by(type, size) |>
  dplyr::summarize(
    sold = dplyr::n(),
    income = sum(price),
    .groups = "drop"
  ) |>
  gt(rowname_col = "size", groupname_col = "type") |>
  tab_header(title = "Pizzas Sold in 2015") |>
  fmt_integer(columns = sold) |>
  fmt_currency(columns = income) |>
  summary_rows(
    fns = list(label = "All Sizes", fn = "sum"),
    side = "top",
    fmt = list(
      ~ fmt_integer(., columns = sold),
      ~ fmt_currency(., columns = income)
    )
  ) |>
  tab_options(
    summary_row.background.color = "gray95",
    row_group.background.color = "#FFEFDB",
    row_group.as_column = TRUE
  ) |>
  tab_stub_indent(
    rows = everything(),
    indent = 2
  )

Pizzas Sold in 2015
		sold	income
chicken	All Sizes	11,050	$195,919.50
	L	4,932	$102,339.00
	M	3,894	$65,224.50
	S	2,224	$28,356.00
classic	All Sizes	14,888	$220,053.10
	L	4,057	$74,518.50
	M	4,112	$60,581.75
	S	6,139	$69,870.25
	XL	552	$14,076.00
	XXL	28	$1,006.60
supreme	All Sizes	11,987	$208,197.00
	L	4,564	$94,258.50
	M	4,046	$66,475.00
	S	3,377	$47,463.50
veggie	All Sizes	11,649	$193,690.45
	L	5,403	$104,202.70
	M	3,583	$57,101.00
	S	2,663	$32,386.75

Indentation of entries in the stub can be controlled by values within a column. Here’s an example of that using the constants dataset, where variations of a row label are mutated to eliminate the common leading text (replacing it with "..."). At the same time, the indentation for those rows is set to 4 in the indent column (value is 0 otherwise). The tab_stub_indent() statement uses from_column(), which passes values from the indent column to the namesake argument. We hide the indent column from view by use of cols_hide().

constants |>
  dplyr::select(name, value, uncert, units) |>
  dplyr::filter(
    grepl("^atomic mass constant", name) |
      grepl("^Rydberg constant", name) |
      grepl("^Bohr magneton", name)
  ) |>
  dplyr::mutate(
    indent = ifelse(grepl("constant |magneton ", name), 4, 0),
    name = gsub(".*constant |.*magneton ", "...", name)
  ) |>
  gt(rowname_col = "name") |>
  tab_stubhead(label = "Physical Constant") |>
  tab_stub_indent(
    rows = everything(),
    indent = from_column(column = "indent")
  ) |>
  fmt_scientific(columns = c(value, uncert)) |>
  fmt_units(columns = units) |>
  cols_hide(columns = indent) |>
  cols_label(
    value = "Value",
    uncert = "Uncertainty",
    units = "Units"
  ) |>
  cols_width(
    stub() ~ px(250),
    c(value, uncert) ~ px(150),
    units ~ px(80)
  ) |>
  tab_style(
    style = cell_text(indent = px(10)),
    locations = list(
      cells_column_labels(columns = units),
      cells_body(columns = units)
    )
  )

Physical Constant	Value	Uncertainty	Units
atomic mass constant	1.66 × 10−27	5.00 × 10−37	kg
...energy equivalent	1.49 × 10−10	4.50 × 10−20	J
...energy equivalent in MeV	9.31 × 102	2.80 × 10−7	MeV
Bohr magneton	9.27 × 10−24	2.80 × 10−33	J T−1
...in eV/T	5.79 × 10−5	1.70 × 10−14	eV T−1
...in Hz/T	1.40 × 1010	4.20	Hz T−1
...in inverse meter per tesla	4.67 × 101	1.40 × 10−8	m−1 T−1
...in K/T	6.72 × 10−1	2.00 × 10−10	K T−1
Rydberg constant	1.10 × 107	2.10 × 10−5	m−1
...times c in Hz	3.29 × 1015	6.40 × 103	Hz
...times hc in eV	1.36 × 101	2.60 × 10−11	eV
...times hc in J	2.18 × 10−18	4.20 × 10−30	J