Enhancing Tables with Flags Using the gt Package in R

Introduction

In this blog post, we’ll explore how to use the gt package in R to create visually appealing tables that include country flags and other enhancements. We’ll also cover data manipulation using tidyverse and rvest to prepare our data.

Step 1: Install and Load Required Packages

To get started, you’ll need several R packages: sf, tidyverse, rvest, gt, and gtsummary. Install and load these packages with the following commands:

require(sf)
require(tidyverse)
require(rvest)
require(gt)
require(gtsummary)

Step 2: Extract and Clean Data

We’ll extract population data from a Wikipedia page listing African countries by population. Using rvest, we can scrape this data into R:

africa = read_html("https://en.wikipedia.org/wiki/List_of_African_countries_by_population") |> 
  html_table()

We then select the first table from the list:

africa.pop = africa |> 
  purrr::pluck(1)

Step 3: Prepare Data for gt

Before we format our table, we need to clean and prepare our data. We’ll use janitor to clean column names and dplyr to select and join relevant columns:

codes = countrypops |> 
  distinct(country_name, .keep_all = T) |> 
  select(1:3)

africa.pop.code = africa.pop |> 
  janitor::clean_names() |> 
  rename(country_name = country) |> 
  select(country_name, pop = 4, growth_rate = 3) |> 
  left_join(codes) |> 
  filter(!country_name == "Total")

Step 4: Format Table with gt

Now we’re ready to create a table with flags using the gt package. First, we check available flag formatting options with:

gt::info_flags()

Complete List of Flag Icons Usable in gt
Flags like these can be used with the fmt_flag() function.
	Entity	Code
	Andorra	AD
	United Arab Emirates	AE
	Afghanistan	AF
	Antigua and Barbuda	AG
	Albania	AL
	Armenia	AM
	Angola	AO
	Argentina	AR
	American Samoa	AS
	Austria	AT
	Australia	AU
	Aruba	AW
	Azerbaijan	AZ
	Bosnia and Herzegovina	BA
	Barbados	BB
	Bangladesh	BD
	Belgium	BE
	Burkina Faso	BF
	Bulgaria	BG
	Bahrain	BH
	Burundi	BI
	Benin	BJ
	Bermuda	BM
	Brunei Darussalam	BN
	Bolivia	BO
	Brazil	BR
	Bahamas	BS
	Bhutan	BT
	Botswana	BW
	Belarus	BY
	Belize	BZ
	Canada	CA
	Congo (Democratic Republic)	CD
	Central African Republic	CF
	Congo	CG
	Switzerland	CH
	Cote d'Ivoire	CI
	Chile	CL
	Cameroon	CM
	China	CN
	Colombia	CO
	Costa Rica	CR
	Cuba	CU
	Cabo Verde	CV
	Curacao	CW
	Cyprus	CY
	Czechia	CZ
	Germany	DE
	Djibouti	DJ
	Denmark	DK
	Dominica	DM
	Dominican Republic	DO
	Algeria	DZ
	Ecuador	EC
	Estonia	EE
	Egypt	EG
	Eritrea	ER
	Spain	ES
	Ethiopia	ET
	European Union	EU
	Finland	FI
	Fiji	FJ
	Micronesia (Federated States)	FM
	Faroe Islands	FO
	France	FR
	Gabon	GA
	United Kingdom	GB
	Grenada	GD
	Georgia	GE
	Ghana	GH
	Gibraltar	GI
	Greenland	GL
	Gambia, The	GM
	Guinea	GN
	Equatorial Guinea	GQ
	Greece	GR
	Guatemala	GT
	Guam	GU
	Guinea-Bissau	GW
	Guyana	GY
	Hong Kong	HK
	Honduras	HN
	Croatia	HR
	Haiti	HT
	Hungary	HU
	Indonesia	ID
	Ireland	IE
	Israel	IL
	Isle of Man	IM
	India	IN
	Iraq	IQ
	Iran (Islamic Republic)	IR
	Iceland	IS
	Italy	IT
	Jamaica	JM
	Jordan	JO
	Japan	JP
	Kenya	KE
	Kyrgyz Republic	KG
	Cambodia	KH
	Kiribati	KI
	Comoros	KM
	St. Kitts and Nevis	KN
	Korea, Dem. People's Rep.	KP
	Korea, Rep.	KR
	Kuwait	KW
	Cayman Islands	KY
	Kazakhstan	KZ
	Lao PDR	LA
	Lebanon	LB
	St. Lucia	LC
	Liechtenstein	LI
	Sri Lanka	LK
	Liberia	LR
	Lesotho	LS
	Lithuania	LT
	Luxembourg	LU
	Latvia	LV
	Libya	LY
	Morocco	MA
	Monaco	MC
	Moldova	MD
	Montenegro	ME
	St. Martin (French part)	MF
	Madagascar	MG
	Marshall Islands	MH
	North Macedonia	MK
	Mali	ML
	Myanmar	MM
	Mongolia	MN
	Macao	MO
	Northern Mariana Islands	MP
	Mauritania	MR
	Malta	MT
	Mauritius	MU
	Maldives	MV
	Malawi	MW
	Mexico	MX
	Malaysia	MY
	Mozambique	MZ
	Namibia	NA
	New Caledonia	NC
	Niger	NE
	Nigeria	NG
	Nicaragua	NI
	Netherlands	NL
	Norway	NO
	Nepal	NP
	Nauru	NR
	New Zealand	NZ
	Oman	OM
	Panama	PA
	Peru	PE
	French Polynesia	PF
	Papua New Guinea	PG
	Philippines	PH
	Pakistan	PK
	Poland	PL
	Puerto Rico	PR
	West Bank and Gaza	PS
	Portugal	PT
	Palau	PW
	Paraguay	PY
	Qatar	QA
	Romania	RO
	Serbia	RS
	Russian Federation	RU
	Rwanda	RW
	Saudi Arabia	SA
	Solomon Islands	SB
	Seychelles	SC
	Sudan	SD
	Sweden	SE
	Singapore	SG
	Slovenia	SI
	Slovak Republic	SK
	Sierra Leone	SL
	San Marino	SM
	Senegal	SN
	Somalia	SO
	Suriname	SR
	South Sudan	SS
	Sao Tome and Principe	ST
	El Salvador	SV
	Sint Maarten (Dutch part)	SX
	Syrian Arab Republic	SY
	Eswatini	SZ
	Turks and Caicos Islands	TC
	Chad	TD
	Togo	TG
	Thailand	TH
	Tajikistan	TJ
	Timor-Leste	TL
	Turkmenistan	TM
	Tunisia	TN
	Tonga	TO
	Turkiye	TR
	Trinidad and Tobago	TT
	Tuvalu	TV
	Tanzania	TZ
	Ukraine	UA
	Uganda	UG
	United States	US
	Uruguay	UY
	Uzbekistan	UZ
	St. Vincent and the Grenadines	VC
	Venezuela, RB	VE
	British Virgin Islands	VG
	Virgin Islands (U.S.)	VI
	Vietnam	VN
	Vanuatu	VU
	Samoa	WS
	Yemen	YE
	South Africa	ZA
	Zambia	ZM
	Zimbabwe	ZW

To format our table with country flags, we use the fmt_flag() function. Here’s how to apply it:

africa.pop.code |> 
  select(country_code_2, country_name, pop, growth_rate) |> 
  gt() |> 
  fmt_flag(columns = country_code_2) |>
  cols_label(
    country_code_2 = "Flag",
    country_name = "Country",
    pop = "Population (2024)",
    growth_rate = "Growth rate"
  )

Flag	Country	Population (2024)	Growth rate
	Nigeria	227,882,945	15.4%
	Ethiopia	128,691,692	8.7%
	Egypt	114,535,772	7.7%
NA	DR Congo	105,789,731	7.1%
	Tanzania	66,617,606	4.5%
	South Africa	63,212,384	4.3%
	Kenya	55,339,003	3.7%
	Sudan	50,042,791	3.4%
	Uganda	48,656,601	3.3%
	Algeria	46,164,219	3.1%
	Morocco	37,712,505	2.5%
	Angola	36,749,906	2.5%
	Ghana	33,787,914	2.3%
	Mozambique	33,635,160	2.3%
	Madagascar	31,195,932	2.1%
NA	Ivory Coast	31,165,654	2.1%
	Cameroon	28,372,687	1.9%
	Niger	26,159,867	1.9%
	Mali	23,769,127	1.8%
	Burkina Faso	23,025,776	1.6%
	Malawi	21,104,482	1.4%
	Zambia	20,723,965	1.4%
	Chad	19,319,064	1.3%
	Somalia	18,358,615	1.2%
	Senegal	18,077,573	1.2%
	Zimbabwe	16,340,822	1.1%
	Guinea	14,405,465	1.0%
	Benin	14,111,034	1.0%
	Rwanda	13,954,471	1.0%
	Burundi	13,689,450	0.9%
	Tunisia	12,200,431	0.9%
	South Sudan	11,483,374	0.8%
	Togo	9,304,337	0.6%
	Sierra Leone	8,460,512	0.6%
	Libya	7,305,659	0.5%
NA	Republic of the Congo	6,182,885	0.4%
	Liberia	5,493,031	0.4%
	Central African Republic	5,152,421	0.4%
	Mauritania	5,022,441	0.3%
	Eritrea	3,470,390	0.3%
	Namibia	2,963,095	0.2%
NA	Gambia	2,697,845	0.2%
	Gabon	2,484,789	0.2%
	Botswana	2,480,244	0.2%
	Lesotho	2,311,472	0.2%
	Guinea-Bissau	2,153,339	0.1%
	Equatorial Guinea	1,847,549	0.1%
	Mauritius	1,273,588	0.1%
	Eswatini	1,230,506	0.1%
	Djibouti	1,152,944	0.1%
	Comoros	850,387	0.1%
NA	Western Sahara	579,729	0.04%
NA	Cape Verde	522,331	0.04%
NA	São Tomé and Príncipe	230,871	0.02%
	Seychelles	127,951	0.01%

Displaying national flags by region

To create a table displaying national flags by region, you first need to prepare and clean your data. Start by filtering the dataset to include only African countries. We use the spData package to access the world dataset, filter for Africa, and remove any geometry columns to focus on the tabular data. Next, we select relevant columns, including country codes, names, and regions.

afcon = spData::world |> 
  filter(continent == "Africa") |> 
  st_drop_geometry() |> 
  as_tibble() |> 
  select(country_code_2=1, country = name_long, region = subregion)

Once the data is prepared, join it with a dataset containing country codes. This dataset should map each country to its flag. Using dplyr, group the data by region and then summarize it to create a comma-separated list of country codes (representing flags) for each region. Rename this summarized column to “flag” for clarity.

afcon.tb = afcon |> 
  left_join(codes) |> 
  dplyr::group_by(region) |>
  dplyr::summarize(
    countries = paste0(country_code_2, collapse = ",")
  ) |> 
  rename(flag = countries)

Then, use the gt package to format and display the table. Create a table object with gt(), and then apply the fmt_flag() function to format the flag column so that it displays the flags associated with each country code. Label the columns appropriately using cols_label() to make the table more readable, with clear names like “Region” and “National Flags.”

afcon.tb |> 
  gt() |> 
  gt::fmt_flag(columns = flag) |>
  cols_label(
    region = "Region",
    flag = "Nationa Flags"
  )

Region	Nationa Flags
Eastern Africa
Middle Africa
Northern Africa
Southern Africa
Western Africa

Conclusion

Using the gt package, we can enhance tables by including flags and applying various formatting options. This approach not only makes data more engaging but also improves readability. Experiment with different formatting functions to further customize your tables!