fmt_country: Generate country names from their corresponding country codes

In gt: Easily Create Presentation-Ready Display Tables

Generate country names from their corresponding country codes

Description

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.

Usage

fmt_country(
  data,
  columns = everything(),
  rows = everything(),
  pattern = "{x}",
  sep = " ",
  locale = NULL
)

Arguments

data	The gt table data object ⁠obj:<gt_tbl>⁠ // required This is the gt table object that is commonly created through use of the gt() function.
columns	Columns to target ⁠<column-targeting expression>⁠ // default: everything() Can either be a series of column names provided in c(), a vector of column indices, or a select helper function (e.g. starts_with(), ends_with(), contains(), matches(), num_range() and everything()).
rows	Rows to target ⁠<row-targeting expression>⁠ // default: everything() In conjunction with columns, we can specify which of their rows should undergo formatting. The default everything() results in all rows in columns being formatted. Alternatively, we can supply a vector of row captions within c(), a vector of row indices, or a select helper function (e.g. starts_with(), ends_with(), contains(), matches(), num_range(), and everything()). We can also use expressions to filter down to the rows we need (e.g., ⁠[colname_1] > 100 & [colname_2] < 50⁠).
pattern	Specification of the formatting pattern ⁠scalar<character>⁠ // default: "{x}" A formatting pattern that allows for decoration of the formatted value. The formatted value is represented by the {x} (which can be used multiple times, if needed) and all other characters will be interpreted as string literals.
sep	Separator between country names ⁠scalar<character>⁠ // default: " " In the output of country names within a body cell, sep provides the separator between each instance. By default, this is a single space character (" ").
locale	Locale identifier ⁠scalar<character>⁠ // default: NULL (optional) An optional locale identifier that can be used for formatting values according the locale's rules. Examples include "en" for English (United States) and "fr" for French (France). We can call info_locales() for a useful reference for all of the locales that are supported. A locale ID can be also set in the initial gt() function call (where it would be used automatically by any function with a locale argument) but a locale value provided here will override that global locale.

Value

An object of class gt_tbl.

Compatibility of formatting function with data values

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.

Targeting cells with columns and rows

Targeting of values is done through columns and additionally by rows (if nothing is provided for rows then entire columns are selected). The columns argument allows us to target a subset of cells contained in the resolved columns. We say resolved because aside from declaring column names in c() (with bare column names or names in quotes) we can use tidyselect-style expressions. This can be as basic as supplying a select helper like starts_with(), or, providing a more complex incantation like

where(~ is.numeric(.x) && max(.x, na.rm = TRUE) > 1E6)

which targets numeric columns that have a maximum value greater than 1,000,000 (excluding any NAs from consideration).

By default all columns and rows are selected (with the everything() defaults). Cell values that are incompatible with a given formatting function will be skipped over, like character values and numeric ⁠fmt_*()⁠ functions. So it's safe to select all columns with a particular formatting function (only those values that can be formatted will be formatted), but, you may not want that. One strategy is to format the bulk of cell values with one formatting function and then constrain the columns for later passes with other types of formatting (the last formatting done to a cell is what you get in the final output).

Once the columns are targeted, we may also target the rows within those columns. This can be done in a variety of ways. If a stub is present, then we potentially have row identifiers. Those can be used much like column names in the columns-targeting scenario. We can use simpler tidyselect-style expressions (the select helpers should work well here) and we can use quoted row identifiers in c(). It's also possible to use row indices (e.g., c(3, 5, 6)) though these index values must correspond to the row numbers of the input data (the indices won't necessarily match those of rearranged rows if row groups are present). One more type of expression is possible, an expression that takes column values (can involve any of the available columns in the table) and returns a logical vector. This is nice if you want to base formatting on values in the column or another column, or, you'd like to use a more complex predicate expression.

Compatibility of arguments with the from_column() helper function

from_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.

Supported regions

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".

Examples

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
  )

Function ID

3-25

Function Introduced

v0.11.0

See Also

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()

gt documentation built on Sept. 11, 2024, 5:15 p.m.