fmt_country | R Documentation |
Tables that have comparable data between countries often need to have the
country name included. While this seems like a fairly simple task, being
consistent with country names is surprisingly difficult. The fmt_country()
function can help in this regard by supplying a country name based on a
2- or 3-letter ISO 3166-1 country code (e.g., Singapore has the "SG"
country code). The resulting country names have been obtained from the
Unicode CLDR (Common Locale Data Repository), which is a good source since
all country names are agreed upon by consensus. Furthermore, the country
names can be localized through the locale
argument (either in this function
or through the initial gt()
call).
Multiple country names can be included per cell by separating country codes
with commas (e.g., "RO,BM"
). And it is okay if the codes are set in either
uppercase or lowercase letters. The sep
argument allows for a common
separator to be applied between country names.
fmt_country(
data,
columns = everything(),
rows = everything(),
pattern = "{x}",
sep = " ",
locale = NULL
)
data |
The gt table data object
This is the gt table object that is commonly created through use of the
|
columns |
Columns to target
Can either be a series of column names provided in |
rows |
Rows to target
In conjunction with |
pattern |
Specification of the formatting pattern
A formatting pattern that allows for decoration of the formatted value. The
formatted value is represented by the |
sep |
Separator between country names
In the output of country names within a body cell, |
locale |
Locale identifier
An optional locale identifier that can be used for formatting values
according to the locale's rules. Examples include |
An object of class gt_tbl
.
fmt_country()
function is compatible with body cells that are of the
"character"
or "factor"
types. Any other types of body cells are ignored
during formatting. This is to say that cells of incompatible data types may
be targeted, but there will be no attempt to format them.
from_column()
helper functionfrom_column()
can be used with certain arguments of fmt_country()
to
obtain varying parameter values from a specified column within the table.
This means that each row could be formatted a little bit differently. These
arguments provide support for from_column()
:
pattern
sep
locale
Please note that for each of the aforementioned arguments, a from_column()
call needs to reference a column that has data of the correct type (this is
different for each argument). Additional columns for parameter values can be
generated with cols_add()
(if not already present). Columns that contain
parameter data can also be hidden from final display with cols_hide()
.
Finally, there is no limitation to how many arguments the from_column()
helper is applied so long as the arguments belong to this closed set.
The following 242 regions (most of which comprise countries) are supported
with names across 574 locales: "AD"
, "AE"
, "AF"
, "AG"
, "AI"
,
"AL"
, "AM"
, "AO"
, "AR"
, "AS"
, "AT"
, "AU"
, "AW"
, "AX"
,
"AZ"
, "BA"
, "BB"
, "BD"
, "BE"
, "BF"
, "BG"
, "BH"
, "BI"
,
"BJ"
, "BL"
, "BM"
, "BN"
, "BO"
, "BR"
, "BS"
, "BT"
, "BW"
,
"BY"
, "BZ"
, "CA"
, "CC"
, "CD"
, "CF"
, "CG"
, "CH"
, "CI"
,
"CK"
, "CL"
, "CM"
, "CN"
, "CO"
, "CR"
, "CU"
, "CV"
, "CW"
,
"CY"
, "CZ"
, "DE"
, "DJ"
, "DK"
, "DM"
, "DO"
, "DZ"
, "EC"
,
"EE"
, "EG"
, "EH"
, "ER"
, "ES"
, "ET"
, "EU"
, "FI"
, "FJ"
,
"FK"
, "FM"
, "FO"
, "FR"
, "GA"
, "GB"
, "GD"
, "GE"
, "GF"
,
"GG"
, "GH"
, "GI"
, "GL"
, "GM"
, "GN"
, "GP"
, "GQ"
, "GR"
,
"GS"
, "GT"
, "GU"
, "GW"
, "GY"
, "HK"
, "HN"
, "HR"
, "HT"
,
"HU"
, "ID"
, "IE"
, "IL"
, "IM"
, "IN"
, "IO"
, "IQ"
, "IR"
,
"IS"
, "IT"
, "JE"
, "JM"
, "JO"
, "JP"
, "KE"
, "KG"
, "KH"
,
"KI"
, "KM"
, "KN"
, "KP"
, "KR"
, "KW"
, "KY"
, "KZ"
, "LA"
,
"LB"
, "LC"
, "LI"
, "LK"
, "LR"
, "LS"
, "LT"
, "LU"
, "LV"
,
"LY"
, "MA"
, "MC"
, "MD"
, "ME"
, "MF"
, "MG"
, "MH"
, "MK"
,
"ML"
, "MM"
, "MN"
, "MO"
, "MP"
, "MQ"
, "MR"
, "MS"
, "MT"
,
"MU"
, "MV"
, "MW"
, "MX"
, "MY"
, "MZ"
, "NA"
, "NC"
, "NE"
,
"NF"
, "NG"
, "NI"
, "NL"
, "NO"
, "NP"
, "NR"
, "NU"
, "NZ"
,
"OM"
, "PA"
, "PE"
, "PF"
, "PG"
, "PH"
, "PK"
, "PL"
, "PM"
,
"PN"
, "PR"
, "PS"
, "PT"
, "PW"
, "PY"
, "QA"
, "RE"
, "RO"
,
"RS"
, "RU"
, "RW"
, "SA"
, "SB"
, "SC"
, "SD"
, "SE"
, "SG"
,
"SI"
, "SK"
, "SL"
, "SM"
, "SN"
, "SO"
, "SR"
, "SS"
, "ST"
,
"SV"
, "SX"
, "SY"
, "SZ"
, "TC"
, "TD"
, "TF"
, "TG"
, "TH"
,
"TJ"
, "TK"
, "TL"
, "TM"
, "TN"
, "TO"
, "TR"
, "TT"
, "TV"
,
"TW"
, "TZ"
, "UA"
, "UG"
, "US"
, "UY"
, "UZ"
, "VA"
, "VC"
,
"VE"
, "VG"
, "VI"
, "VN"
, "VU"
, "WF"
, "WS"
, "YE"
, "YT"
,
"ZA"
, "ZM"
, and "ZW"
.
The peeps
dataset will be used to generate a small gt table
containing only the people born in the 1980s. The country
column contains
3-letter country codes and those will be transformed to country names with
fmt_country()
.
peeps |> dplyr::filter(grepl("198", dob)) |> dplyr::select(name_given, name_family, country, dob) |> dplyr::arrange(country, name_family) |> gt() |> fmt_country(columns = country) |> cols_merge(columns = c(name_given, name_family)) |> opt_vertical_padding(scale = 0.5) |> tab_options(column_labels.hidden = TRUE)
Use the countrypops
dataset to create a gt table. We will only
include a few columns and rows from that table. The country_code_3
column
has 3-letter country codes in the format required for fmt_country()
and
using that function transforms the codes to country names.
countrypops |> dplyr::filter(year == 2021) |> dplyr::filter(grepl("^S", country_name)) |> dplyr::arrange(country_name) |> dplyr::select(-country_name, -year) |> dplyr::slice_head(n = 10) |> gt() |> fmt_integer() |> fmt_flag(columns = country_code_2) |> fmt_country(columns = country_code_3) |> cols_label( country_code_2 = "", country_code_3 = "Country", population = "Population (2021)" )
The country names derived from country codes can be localized. Let's
translate some of those country names into three different languages using
different locale
values in separate calls of fmt_country()
.
countrypops |> dplyr::filter(year == 2021) |> dplyr::arrange(desc(population)) |> dplyr::filter( dplyr::row_number() > max(dplyr::row_number()) - 5 | dplyr::row_number() <= 5 ) |> dplyr::select( country_code_fl = country_code_2, country_code_2a = country_code_2, country_code_2b = country_code_2, country_code_2c = country_code_2, population ) |> gt(rowname_col = "country_code_fl") |> fmt_integer() |> fmt_flag(columns = stub()) |> fmt_country(columns = ends_with("a")) |> fmt_country(columns = ends_with("b"), locale = "ja") |> fmt_country(columns = ends_with("c"), locale = "ar") |> cols_label( ends_with("a") ~ "`en`", ends_with("b") ~ "`ja`", ends_with("c") ~ "`ar`", population = "Population", .fn = md ) |> tab_spanner( label = "Country name in specified locale", columns = matches("2a|2b|2c") ) |> cols_align(align = "center", columns = matches("2a|2b|2c")) |> opt_horizontal_padding(scale = 2)
Let's make another gt table, this time using the films
dataset. The
countries_of_origin
column contains 2-letter country codes and some cells
contain multiple countries (separated by commas). We'll use fmt_country()
on that column and also specify that the rendered country names should be
separated by a comma and a space character. Also note that historical
country codes like "SU"
('USSR'), "CS"
('Czechoslovakia'), and "YU"
('Yugoslavia') are permitted as inputs for fmt_country()
.
films |> dplyr::filter(year == 1959) |> dplyr::select( contains("title"), run_time, director, countries_of_origin, imdb_url ) |> gt() |> tab_header(title = "Feature Films in Competition at the 1959 Festival") |> fmt_country(columns = countries_of_origin, sep = ", ") |> fmt_url( columns = imdb_url, label = fontawesome::fa("imdb", fill = "black") ) |> cols_merge( columns = c(title, original_title, imdb_url), pattern = "{1}<< ({2})>> {3}" ) |> cols_label( title = "Film", run_time = "Length", director = "Director", countries_of_origin = "Country" ) |> opt_vertical_padding(scale = 0.5) |> opt_table_font(stack = "classical-humanist", weight = "bold") |> opt_stylize(style = 1, color = "gray") |> tab_options(heading.title.font.size = px(26))
Country names can sometimes pair nicely with flag-based graphics. In this
example (using a different portion of the films
dataset) we use
fmt_country()
along with fmt_flag()
. The formatted country names are then
merged into the same cells as the icons via cols_merge()
.
films |> dplyr::filter(director == "Jean-Pierre Dardenne, Luc Dardenne") |> dplyr::select(title, year, run_time, countries_of_origin) |> gt() |> tab_header(title = "In Competition Films by the Dardenne Bros.") |> cols_add(country_flag = countries_of_origin) |> fmt_flag(columns = country_flag) |> fmt_country(columns = countries_of_origin, sep = ", ") |> cols_merge( columns = c(countries_of_origin, country_flag), pattern = "{2}<br>{1}" ) |> tab_style( style = cell_text(size = px(9)), locations = cells_body(columns = countries_of_origin) ) |> cols_merge(columns = c(title, year), pattern = "{1} ({2})") |> opt_vertical_padding(scale = 0.5) |> opt_horizontal_padding(scale = 3) |> opt_table_font(font = google_font("PT Sans")) |> opt_stylize(style = 1, color = "blue") |> tab_options( heading.title.font.size = px(26), column_labels.hidden = TRUE )
3-25
v0.11.0
(July 9, 2024)
Other data formatting functions:
data_color()
,
fmt()
,
fmt_auto()
,
fmt_bins()
,
fmt_bytes()
,
fmt_chem()
,
fmt_currency()
,
fmt_date()
,
fmt_datetime()
,
fmt_duration()
,
fmt_email()
,
fmt_engineering()
,
fmt_flag()
,
fmt_fraction()
,
fmt_icon()
,
fmt_image()
,
fmt_index()
,
fmt_integer()
,
fmt_markdown()
,
fmt_number()
,
fmt_partsper()
,
fmt_passthrough()
,
fmt_percent()
,
fmt_roman()
,
fmt_scientific()
,
fmt_spelled_num()
,
fmt_tf()
,
fmt_time()
,
fmt_units()
,
fmt_url()
,
sub_large_vals()
,
sub_missing()
,
sub_small_vals()
,
sub_values()
,
sub_zero()
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