gt-fmt_integer – The gt package

The `fmt_integer()` function

For this example, we’ll use two columns from the exibble dataset and create a simple gt table. With fmt_integer(), we’ll format the num column as integer values having no digit separators (with the use_seps = FALSE option).

exibble |>
  dplyr::select(num, char) |>
  gt() |>
  fmt_integer(use_seps = FALSE)

num	char
0	apricot
2	banana
33	coconut
444	durian
5550	NA
NA	fig
777000	grapefruit
8880000	honeydew

Let’s use a modified version of the countrypops dataset to create a gt table with row labels. We will format all numeric columns with fmt_integer() and scale all values by 1 / 1E6, giving us integer values representing millions of people. We can make clear what the values represent with an informative spanner label via tab_spanner().

countrypops |>
  dplyr::select(country_code_3, year, population) |>
  dplyr::filter(country_code_3 %in% c("CHN", "IND", "USA", "PAK", "IDN")) |>
  dplyr::filter(year > 1975 & year %% 5 == 0) |>
  tidyr::pivot_wider(names_from = year, values_from = population) |>
  dplyr::arrange(desc(`2015`)) |>
  gt(rowname_col = "country_code_3") |>
  fmt_integer(scale_by = 1 / 1E6) |>
  tab_spanner(label = "Millions of People", columns = everything())

	Millions of People
	1980	1985	1990	1995	2000	2005	2010	2015	2020
CHN	981	1,051	1,135	1,205	1,263	1,304	1,338	1,380	1,411
IND	697	780	870	964	1,060	1,155	1,241	1,323	1,396
USA	227	238	250	266	282	296	309	321	332
IDN	148	166	182	198	214	229	244	259	272
PAK	81	97	115	133	154	174	194	211	227

Using a subset of the towny dataset, we can do interesting things with integer values. Through cols_add() we’ll add the difference column (which calculates the difference between 2021 and 2001 populations). All numeric values will be formatted with a first pass of fmt_integer(); a second pass of fmt_integer() focuses on the difference column and here we use the force_sign = TRUE option to draw attention to positive and negative difference values.

towny |>
  dplyr::select(name, population_2001, population_2021) |>
  dplyr::slice_tail(n = 10) |>
  gt() |>
  cols_add(difference = population_2021 - population_2001) |>
  fmt_integer() |>
  fmt_integer(columns = difference, force_sign = TRUE) |>
  cols_label_with(fn = function(x) gsub("population_", "", x)) |>
  tab_style(
    style = cell_fill(color = "gray90"),
    locations = cells_body(columns = difference)
  )

name	2001	2021	difference
Whitchurch-Stouffville	22,859	49,864	+27,005
White River	993	557	−436
Whitestone	853	1,075	+222
Whitewater	6,520	7,225	+705
Wilmot	14,866	21,429	+6,563
Windsor	208,402	229,660	+21,258
Wollaston	679	721	+42
Woodstock	33,061	46,705	+13,644
Woolwich	18,201	26,999	+8,798
Zorra	8,052	8,628	+576

The fmt_integer() function

The `fmt_integer()` function