Spanning labels to identify groups of rows or columns

Description

Spanning labels to identify groups of rows or columns

Usage

group_tt(x, i = NULL, j = NULL, indent = 1, ...)

Arguments

x	A data frame or data table to be rendered as a table.
i	A vector of labels with length equal to the number of rows in x, or a named list of row indices to group. The names of the list will be used as labels. The indices represent the position where labels should be inserted in the original table. For example, i=list(“Hello”=5): insert the "Hello" label after the 4th row in the original table. i=list(“Hello”=2, “World”=2): insert the two labels consecutively after the 1st row in the original table. i=list(“Foo Bar”=0): insert the label in the first row after the header.
j	A named list of column indices to group. The names of the list will be used as labels. See examples below. Note: empty labels must be a space: " ".
indent	integer number of pt to use when indenting the non-labelled rows.
…	Other arguments are ignored.

Details

Warning: The style_tt() can normally be used to style the group headers, as expected, but that feature is not available for Markdown and Word tables.

Value

An object of class tt representing the table.

Word and Markdown limitations

Markdown and Word tables only support these styles: italic, bold, strikeout. The width argument is also unavailable Moreover, the style_tt() function cannot be used to style headers inserted by the group_tt() function; instead, you should style the headers directly in the header definition using markdown syntax: group_tt(i = list(“italic header” = 2)). These limitations are due to the fact that there is no markdown syntax for the other options, and that we create Word documents by converting a markdown table to .docx via the Pandoc software.

Examples

library("tinytable")


# vector of row labels
dat <- data.frame(
  label = c("a", "a", "a", "b", "b", "c", "a", "a"),
  x1 = rnorm(8),
  x2 = rnorm(8))
tt(dat[, 2:3]) |> group_tt(i = dat$label)

x1	x2
0.1499096	2.3998253
-2.0563054	-1.4617276
0.1925065	2.1456133
-1.3696832	0.3520885
1.8249699	0.4654031
1.0855862	0.3003040
2.1227166	-0.4138763
-0.6386533	1.4779297

# named lists of labels
tt(mtcars[1:10, 1:5]) |>
  group_tt(
    i = list(
      "Hello" = 3,
      "World" = 8),
    j = list(
      "Foo" = 2:3,
      "Bar" = 4:5))

	Foo		Bar
mpg	cyl	disp	hp	drat
21.0	6	160.0	110	3.90
21.0	6	160.0	110	3.90
22.8	4	108.0	93	3.85
21.4	6	258.0	110	3.08
18.7	8	360.0	175	3.15
18.1	6	225.0	105	2.76
14.3	8	360.0	245	3.21
24.4	4	146.7	62	3.69
22.8	4	140.8	95	3.92
19.2	6	167.6	123	3.92

dat <- mtcars[1:9, 1:8]
tt(dat) |>
  group_tt(i = list(
    "I like (fake) hamburgers" = 3,
    "She prefers halloumi" = 4,
    "They love tofu" = 7))

mpg	cyl	disp	hp	drat	wt	qsec	vs
21.0	6	160.0	110	3.90	2.620	16.46	0
21.0	6	160.0	110	3.90	2.875	17.02	0
22.8	4	108.0	93	3.85	2.320	18.61	1
21.4	6	258.0	110	3.08	3.215	19.44	1
18.7	8	360.0	175	3.15	3.440	17.02	0
18.1	6	225.0	105	2.76	3.460	20.22	1
14.3	8	360.0	245	3.21	3.570	15.84	0
24.4	4	146.7	62	3.69	3.190	20.00	1
22.8	4	140.8	95	3.92	3.150	22.90	1

tt(dat) |>
  group_tt(
    j = list(
      "Hamburgers" = 1:3,
      "Halloumi" = 4:5,
      "Tofu" = 7))

Hamburgers			Halloumi			Tofu
mpg	cyl	disp	hp	drat	wt	qsec	vs
21.0	6	160.0	110	3.90	2.620	16.46	0
21.0	6	160.0	110	3.90	2.875	17.02	0
22.8	4	108.0	93	3.85	2.320	18.61	1
21.4	6	258.0	110	3.08	3.215	19.44	1
18.7	8	360.0	175	3.15	3.440	17.02	0
18.1	6	225.0	105	2.76	3.460	20.22	1
14.3	8	360.0	245	3.21	3.570	15.84	0
24.4	4	146.7	62	3.69	3.190	20.00	1
22.8	4	140.8	95	3.92	3.150	22.90	1

x <- mtcars[1:5, 1:6]
tt(x) |>
  group_tt(j = list("Hello" = 1:2, "World" = 3:4, "Hello" = 5:6)) |>
  group_tt(j = list("Foo" = 1:3, "Bar" = 4:6))

Foo			Bar
Hello		World		Hello
mpg	cyl	disp	hp	drat	wt
21.0	6	160	110	3.90	2.620
21.0	6	160	110	3.90	2.875
22.8	4	108	93	3.85	2.320
21.4	6	258	110	3.08	3.215
18.7	8	360	175	3.15	3.440