The text_case_match() function

Let’s use the exibble dataset to create a simple, two-column gt table (keeping only the char and fctr columns). In the char column, we’ll transform the NA value to "elderberry" using the text_case_match() function. Over in the fctr column, some more sophisticated matches will be performed using text_case_match(). That column has spelled out numbers and we can produce these on the LHS with help from vec_fmt_spelled_num(). The replacements will contain descriptive text. In this last call of text_case_match(), we use a .default to replace text for any of those non-matched cases.

exibble |>
  dplyr::select(char, fctr) |>
  gt() |>
  text_case_match(
    NA ~ "elderberry",
    .locations = cells_body(columns = char)
  ) |>
  text_case_match(
    vec_fmt_spelled_num(1:4) ~ "one to four",
    vec_fmt_spelled_num(5:6) ~ "five or six",
    .default = "seven or more",
    .locations = cells_body(columns = fctr)
  )

char	fctr
apricot	one to four
banana	one to four
coconut	one to four
durian	one to four
elderberry	five or six
fig	five or six
grapefruit	seven or more
honeydew	seven or more

Next, let’s use a transformed version of the towny dataset to create a gt table. Transform the text in the csd_type column using two-sided formulas supplied to text_case_match(). We can replace matches on the LHS with Fontawesome icons furnished by the fontawesome R package.

towny |>
  dplyr::select(name, csd_type, population_2021) |>
  dplyr::filter(csd_type %in% c("city", "town")) |>
  dplyr::slice_max(population_2021, n = 5, by = csd_type) |>
  dplyr::arrange(csd_type) |>
  gt() |>
  fmt_integer() |>
  text_case_match(
    "city" ~ fontawesome::fa("city"),
    "town" ~ fontawesome::fa("house-chimney")
  ) |>
  cols_label(
    name = "City/Town",
    csd_type = "",
    population_2021 = "Population"
  )

City/Town		Population
Toronto		2,794,356
Ottawa		1,017,449
Mississauga		717,961
Brampton		656,480
Hamilton		569,353
Oakville		213,759
Whitby		138,501
Milton		132,979
Ajax		126,666
Newmarket		87,942