The cols_units() function

Let’s analyze some pizzaplace data with dplyr and then make a gt table. Here we are separately defining new column labels with cols_label() and then defining the units (to combine to those labels) through cols_units(). The default pattern for combination is "{1, {2}“} which is acceptable here.

pizzaplace |>
  dplyr::mutate(month = lubridate::month(date, label = TRUE, abbr = TRUE)) |>
  dplyr::group_by(month) |>
  dplyr::summarize(
    n_sold = dplyr::n(),
    rev = sum(price)
  ) |>
  dplyr::mutate(chg = (rev - dplyr::lag(rev)) / dplyr::lag(rev)) |>
  dplyr::mutate(month = as.character(month)) |>
  gt(rowname_col = "month") |>
  fmt_integer(columns = n_sold) |>
  fmt_currency(columns = rev, use_subunits = FALSE) |>
  fmt_percent(columns = chg) |>
  sub_missing() |>
  cols_label(
    n_sold = "Number of Pizzas Sold",
    rev = "Revenue Generated",
    chg = "Monthly Changes in Revenue"
  ) |>
  cols_units(
    n_sold = "units month^-1",
    rev = "USD month^-1",
    chg = "% change *m*/*m*"
  ) |>
  cols_width(
    stub() ~ px(40),
    everything() ~ px(200)
  )

	Number of Pizzas Sold, units month−1	Revenue Generated, USD month−1	Monthly Changes in Revenue, % change m/m
Jan	4,232	$69,793	—
Feb	3,961	$65,160	−6.64%
Mar	4,261	$70,397	8.04%
Apr	4,151	$68,737	−2.36%
May	4,328	$71,403	3.88%
Jun	4,107	$68,230	−4.44%
Jul	4,392	$72,558	6.34%
Aug	4,168	$68,278	−5.90%
Sep	3,890	$64,180	−6.00%
Oct	3,883	$64,028	−0.24%
Nov	4,266	$70,395	9.95%
Dec	3,935	$64,701	−8.09%

The sza dataset has a wealth of information and here we’ll generate a smaller table that contains the average solar zenith angles at noon for different months and at different northern latitudes. The column labels are numbers representing the latitudes and it’s convenient to apply units of ‘degrees north’ to each of them with cols_units(). The extra thing we wanted to do here was to ensure that the units are placed directly after the column labels, and we do that with .units_pattern = "{1{2}“}. This append the units ("{2”}) right to the column label ("{1“}).

sza |>
  dplyr::filter(tst == "1200") |>
  dplyr::select(-tst) |>
  dplyr::arrange(desc(latitude)) |>
  tidyr::pivot_wider(
    names_from = latitude,
    values_from = sza
  ) |>
  gt(rowname_col = "month") |>
  cols_units(
    everything() ~ ":degree:N",
    .units_pattern = "{1}{2}"
  ) |>
  tab_spanner(
    label = "Solar Zenith Angle",
    columns = everything()
  ) |>
  text_transform(
    fn = toupper,
    locations = cells_stub()
  ) |>
  tab_style(
    style = cell_text(align = "right"),
    locations = cells_stub()
  )

	Solar Zenith Angle
	50°N	40°N	30°N	20°N
JAN	73.0	63.0	53.0	43.0
FEB	67.2	57.2	47.2	37.2
MAR	57.7	47.7	37.7	27.7
APR	45.5	35.5	25.5	15.5
MAY	35.0	25.0	15.0	5.0
JUN	28.0	18.0	8.0	2.0
JUL	26.9	16.9	6.9	3.1
AUG	31.9	21.9	11.9	1.9
SEP	41.6	31.6	21.6	11.6
OCT	53.1	43.1	33.1	23.1
NOV	64.4	54.5	44.4	34.4
DEC	71.8	61.8	51.8	41.8

Taking a portion of the towny dataset, let’s use spanners to describe what’s in the columns and use only measurement units for the column labels. The columns labels that have to do with population and density information will be replaced with units defined in cols_units(). We’ll use a .units_pattern value of "{2“}, which means that only the units will be present (the "{1”}, representing the column label text, is omitted). Spanners added through several invocations of tab_spanner() will declare what the last four columns contain.

towny |>
  dplyr::select(
    name, land_area_km2,
    ends_with("2016"), ends_with("2021")
  ) |>
  dplyr::slice_max(population_2021, n = 10) |>
  gt(rowname_col = "name") |>
  tab_stubhead(label = "City") |>
  fmt_integer() |>
  cols_label(
    land_area_km2 ~ "Area, {{km^2}}",
    starts_with("population") ~ "",
    starts_with("density") ~ ""
  ) |>
  cols_units(
    starts_with("population") ~ "*ppl*",
    starts_with("density") ~ "*ppl* km^-2",
    .units_pattern = "{2}"
  ) |>
  tab_spanner(
    label = "Population",
    columns = starts_with("population"),
    gather = FALSE
  ) |>
  tab_spanner(
    label = "Density",
    columns = starts_with("density"),
    gather = FALSE
  ) |>
  tab_spanner(
    label = "2016",
    columns = ends_with("2016"),
    gather = FALSE
  ) |>
  tab_spanner(
    label = "2021",
    columns = ends_with("2021"),
    gather = FALSE
  ) |>
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(
      c(starts_with("population"), starts_with("density"))
    )
  ) |>
  cols_width(everything() ~ px(120)) |>
  opt_horizontal_padding(scale = 3)

		2016		2021
City	Area, km2	Population	Density	Population	Density
		ppl	ppl km−2	ppl	ppl km−2
Toronto	631	2,731,571	4,328	2,794,356	4,428
Ottawa	2,788	934,243	335	1,017,449	365
Mississauga	293	721,599	2,465	717,961	2,453
Brampton	266	593,638	2,233	656,480	2,469
Hamilton	1,118	536,917	480	569,353	509
London	420	383,822	913	422,324	1,004
Markham	211	328,966	1,560	338,503	1,605
Vaughan	272	306,233	1,124	323,103	1,186
Kitchener	137	233,222	1,705	256,885	1,878
Windsor	146	217,188	1,487	229,660	1,573