The fmt_number() function

Let’s use the exibble dataset to create a gt table. With fmt_number(), we’ll format the num column to have three decimal places (with decimals = 3) and omit the use of digit separators (with use_seps = FALSE).

exibble |>
  gt() |>
  fmt_number(
    columns = num,
    decimals = 3,
    use_seps = FALSE
  )

num	char	fctr	date	time	datetime	currency	row	group
0.111	apricot	one	2015-01-15	13:35	2018-01-01 02:22	49.950	row_1	grp_a
2.222	banana	two	2015-02-15	14:40	2018-02-02 14:33	17.950	row_2	grp_a
33.330	coconut	three	2015-03-15	15:45	2018-03-03 03:44	1.390	row_3	grp_a
444.400	durian	four	2015-04-15	16:50	2018-04-04 15:55	65100.000	row_4	grp_a
5550.000	NA	five	2015-05-15	17:55	2018-05-05 04:00	1325.810	row_5	grp_b
NA	fig	six	2015-06-15	NA	2018-06-06 16:11	13.255	row_6	grp_b
777000.000	grapefruit	seven	NA	19:10	2018-07-07 05:22	NA	row_7	grp_b
8880000.000	honeydew	eight	2015-08-15	20:20	NA	0.440	row_8	grp_b

Use a modified version of the countrypops dataset to create a gt table with row labels. Format all columns to use large-number suffixing (e.g., where "10,000,000" becomes "10M") with the suffixing = TRUE option.

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::spread(year, population) |>
  dplyr::arrange(desc(`2015`)) |>
  gt(rowname_col = "country_code_3") |>
  fmt_number(suffixing = TRUE)

	1980	1985	1990	1995	2000	2005	2010	2015	2020
CHN	981.23M	1.05B	1.14B	1.20B	1.26B	1.30B	1.34B	1.38B	1.41B
IND	696.83M	780.24M	870.45M	964.28M	1.06B	1.15B	1.24B	1.32B	1.40B
USA	227.22M	237.92M	249.62M	266.28M	282.16M	295.52M	309.33M	320.74M	331.51M
IDN	148.18M	165.79M	182.16M	198.14M	214.07M	228.81M	244.02M	259.09M	271.86M
PAK	80.62M	97.12M	115.41M	133.12M	154.37M	174.37M	194.45M	210.97M	227.20M

In a variation of the previous table, we can combine large-number suffixing with a declaration of the number of significant digits to use. With things like population figures, n_sigfig = 3 is a very good option.

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::spread(year, population) |>
  dplyr::arrange(desc(`2015`)) |>
  gt(rowname_col = "country_code_3") |>
  fmt_number(suffixing = TRUE, n_sigfig = 3)

	1980	1985	1990	1995	2000	2005	2010	2015	2020
CHN	981M	1.05B	1.14B	1.20B	1.26B	1.30B	1.34B	1.38B	1.41B
IND	697M	780M	870M	964M	1.06B	1.15B	1.24B	1.32B	1.40B
USA	227M	238M	250M	266M	282M	296M	309M	321M	332M
IDN	148M	166M	182M	198M	214M	229M	244M	259M	272M
PAK	80.6M	97.1M	115M	133M	154M	174M	194M	211M	227M

There can be cases where you want to show numbers to a large number of decimal places but also drop the unnecessary trailing zeros for low-precision values. Let’s take a portion of the towny dataset and format the latitude and longitude columns with fmt_number(). We’ll have up to 5 digits displayed as decimal values, but we’ll also unconditionally drop any runs of trailing zeros in the decimal part with drop_trailing_zeros = TRUE.

towny |>
  dplyr::select(name, latitude, longitude) |>
  dplyr::slice_head(n = 10) |>
  gt() |>
  fmt_number(decimals = 5, drop_trailing_zeros = TRUE) |>
  cols_merge(columns = -name, pattern = "{1}, {2}") |>
  cols_label(
    name ~ "Municipality",
    latitude = "Location"
  )

Municipality	Location
Addington Highlands	45, −77.25
Adelaide Metcalfe	42.95, −81.7
Adjala-Tosorontio	44.13333, −79.93333
Admaston/Bromley	45.52917, −76.89694
Ajax	43.85833, −79.03639
Alberton	48.6, −93.53333
Alfred and Plantagenet	45.56667, −74.91667
Algonquin Highlands	45.4, −78.75
Alnwick/Haldimand	44.08333, −78.03333
Amaranth	43.98333, −80.23333

Another strategy for dealing with precision of decimals is to have a separate column of values that specify how many decimal digits to retain. Such a column can be added via cols_add() or it can be part of the input table for gt(). With that column available, it can be referenced in the decimals argument with from_column(). This approach yields a display of coordinate values that reflects the measurement precision of each value.

towny |>
  dplyr::select(name, latitude, longitude) |>
  dplyr::slice_head(n = 10) |>
  gt() |>
  cols_add(dec_digits = c(1, 2, 2, 5, 5, 2, 3, 2, 3, 3)) |>
  fmt_number(decimals = from_column(column = "dec_digits")) |>
  cols_merge(columns = -name, pattern = "{1}, {2}") |>
  cols_label(
    name ~ "Municipality",
    latitude = "Location"
  )

Municipality	Location
Addington Highlands	45.0, −77.2
Adelaide Metcalfe	42.95, −81.70
Adjala-Tosorontio	44.13, −79.93
Admaston/Bromley	45.52917, −76.89694
Ajax	43.85833, −79.03639
Alberton	48.60, −93.53
Alfred and Plantagenet	45.567, −74.917
Algonquin Highlands	45.40, −78.75
Alnwick/Haldimand	44.083, −78.033
Amaranth	43.983, −80.233