Appendix A: Regions and their Countries

Objectives

Country and Regions: Classifications for countries

  1. Inspecting different approaches to classify countries by international organizations (World Bank and United Nations Statistics Division)
  2. Understanding the country classification usedby my data sources (WID and WHR.
  3. Deciding which classification system I am going to use.
  4. Modifying the country classification of WID and WHR where necessary so that the country classification conforms to the internationally recognized and approved systems.

I aim to compare different aspects of Austria with those of other countries. I want, for instance, to know how well Austria is doing compared to other European countries, the other member states of the European Union, or other OECD countries. It is, therefore, vital to have different categorization systems.

A.1 Country groupings in WHR and WID

There are already different classification systems in place: International organizations (e.g., World Bank, United Nations) have developed them with several grouping variants.

On the other hand the WID database also uses different categorizations scheme and some of the WHR years have also incorporated regional groupings (2013, 2015, 2020, and 2021). For an interpretation of my analysis in relation to theirs it is important to know if we are using the same classifications or if not, where the differences are.

As I am listing several classification variants it pays the effort to develop a function for the repetitive task.

A.1.1 Function class_scheme()

R Code A.1 : Function class_scheme() for showing classification scheme

Listing / Output A.1: Function class_scheme() for showing results of a classification system
Code
class_scheme <- function(df, sel1, sel2) {
    ## df = dataframe to show
    ## sel1 = name of the first column (country names) to select
    ## sel2 = name of the column with the regional indicator
  df |> 
        dplyr::select(!!sel1, !!sel2) |> 
        dplyr::nest_by(!!sel2) |> 
        dplyr::mutate(data = as.vector(data)) |>
        dplyr::mutate(data = stringr::str_c(data, collapse = "; ")) |>
        dplyr::mutate(data = paste(data, ";")) |> 
        dplyr::mutate(N = lengths(gregexpr(";", data))) |> 
        dplyr::rename(Country = data) |> 
        dplyr::arrange(!!sel2) |> 
        DT::datatable(class = 'cell-border compact stripe', 
            options = list(
              pageLength = 25,
              lengthMenu = c(5, 10, 15, 20, 25, 50)
              )
        )
}

df_whr <-  base::readRDS("data/whr/raw/whr_raw_2021.rds")
df_wid <-  base::readRDS("data/chap80/country_codes.rds")
df_wid <-  df_wid |> 
  dplyr::mutate(dplyr::across(tidyselect::where(
    base::is.factor), base::as.character))

Using {dplyr} programming code in functions needs some special consideration. I have learned the details from “Bang Bang – How to program with dplyr” (Berroth 2019).

A.1.2 Grouping Schemata

As our function is now in place, we can display the different grouping schemes.

Code Collection A.1 : Grouping schemata of WHR and WID datasets

R Code A.2 : Classification of the WHR data

Code
(
    whr_class <- class_scheme(
            df = df_whr,
            sel1 = rlang::quo(`Country name`),
            sel2 = rlang::quo(`Regional indicator`)
            )
)

The datasets for 2013, 2015, 2020 and 2021 use all the same classification scheme with 149 countries in 10 regions.

149 are by far not all countries of the world. Their number is about 195 with some insecurities about Holy See (Vatican), the State of Palestine, Taiwan and Kosovo. (Compare: How Many Countries Are There In The World?) The reason for this lower number is simple: For only those 149 countries are subjective well-being data in one of the study years available.

R Code A.3 : Classifications of the WID data with column “region1”

Code
(
    wid1_class <- class_scheme(
            df = df_wid,
            sel1 = rlang::quo(shortname),
            sel2 = rlang::quo(region1)
            )
)

region1 is a classification scheme with 216 countries in 5 regions.

216 is more than the 195 countries mentioned in How Many Countries Are There In The World?. I believe the reason for this larger amount of “countries” is that it includes also 29 economies with populations of more than 30,000 as explained in the World Bank classification with 218 countries mentioned in Section A.2.3.1.2. There is still a small difference of two countries (218 - 216 = 2).

R Code A.4 : Classifications of the WID data with column “region2”

Code
(
    wid2_class <- class_scheme(
            df = df_wid,
            sel1 = rlang::quo(shortname),
            sel2 = rlang::quo(region2)
            )
)

region2 is a classification scheme with 216 countries in 18 regions.

R Code A.5 : Classifications of the WID data with column “region3”

Code
(
    wid3_class <- class_scheme(
            df = df_wid,
            sel1 = rlang::quo(shortname),
            sel2 = rlang::quo(region3)
            )
)

region3 is a classification scheme with 216 countries in 2 regions.

R Code A.6 : Classifications of the WID data with column “region4”

Code
(
    wid4_class <- class_scheme(
            df = df_wid,
            sel1 = rlang::quo(shortname),
            sel2 = rlang::quo(region4)
            )
)

region4 is a classification scheme with 216 countries in 10 regions.

R Code A.7 : Classifications of the WID data with column “region5”

Code
(
    wid5_class <- class_scheme(
            df = df_wid,
            sel1 = rlang::quo(shortname),
            sel2 = rlang::quo(region5)
            )
)

region5 is a classification scheme with 216 countries in 8 regions.

R Code A.8 : Classifications of the WID data with column “region6”

Code
## replace NAs of region4 with values of region5
df_wid6 <- df_wid |> 
  dplyr::mutate(region6 = 
         base::ifelse(is.na(region4), region5, region4)
         )

(
    wid_class6 <- class_scheme(
            df = df_wid6,
            sel1 = rlang::quo(shortname),
            sel2 = rlang::quo(region6)
            )
)

region6 is a classification scheme with 216 countries in 17 regions.

A.1.3 Summary

XY = STILL TO WRITE!

A.2 Official classifications

In a first step I want to find out where these classifications systems come from and compare them to the country groupings in the WID and WHR data. I will look into the two official classifications schemes of World Bank and United Nations and apply the following procedure:

Procedure A.1 : Understand structure and content of the official classifications schemata

  1. Create a directory for storing the different country classification files (see Section A.2.1).
  2. Download classification files and store them for faster access as R objects with rds format (see Section A.2.2.1 and Section A.2.2.2).
  3. Inspect the data classification files of World Bank (Section A.2.3.1) and of the United Nations (Section A.2.3.2) in detail.

A.2.1 Create data directories

R Code A.9 : Create folders for country classification files

Code
pb_create_folder(base::paste0(here::here(), "/data/"))
pb_create_folder(base::paste0(here::here(), "/data/country-class"))
(For this R code chunk is no output available)

A.2.2 Download classification files

A.2.2.1 World Bank

The World Bank Classification can be downloaded from How does the World Bank classify countries?. At the bottom of the page you can see the line “Download an Excel file of historical classifications by income.”, providing a link with word “Download”. The downloaded file CLASS.xlsx does not contain a historical classification by income but the general classification system of the last available year (2023).

Yes, there is another Excel file OGHIST.xslx with the historical cutoffs for incomes and lending categories, dating from 1987 to 2023. But the download link for this file is located at another web page: World Bank Country and Lending Groups. On this page you will also find the updates for the cutoffs for countries GNI income per capita which is important for the lending eligibility of countries. World Bank country classifications by income level for 2024-2025 has the current updated values and changes over the last year.

The file CLASS.xlsx am interested here consists of three sheets:

  1. “List of Economoies”
  2. “compositions” and
  3. “Notes”

I am going to download the first two Excel sheets.

R Code A.10 : Numbered R Code Title

Run this code chunk manually if the file still needs to be downloaded.
Code
## download wb-class file ##############
downloader::download(
    url = "https://datacatalogfiles.worldbank.org/ddh-published/0037712/DR0090755/CLASS.xlsx",
    destfile = base::paste0(here::here(), 
            "/data/country-class/wb-class.xlsx")
)

## create R object ############
wb_class_economies <- 
  readxl::read_xlsx(
    base::paste0(here::here(), 
    "/data/country-class/wb-class.xlsx"),
           sheet = "List of economies") 


wb_class_compositions <- 
  readxl::read_xlsx(
    base::paste0(here::here(), 
    "/data/country-class/wb-class.xlsx"),
           sheet = "compositions")

## save as .rds files ###############
pb_save_data_file(
  "country-class", 
  wb_class_economies, 
  "wb_class_economies.rds"
)


pb_save_data_file(
  "country-class", 
  wb_class_compositions, 
  "wb_class_compositions.rds"
)
(For this R code chunk is no output available)

A.2.2.2 UNSD-M49

Another more detailed classification system expressively developed for statistical purposes is developed by the United Nations Statistics Division UNSD using the M49 methodology.

The result is called Standard country or area codes for statistical use (M49) and can be downloaded manually in different languages and formats (Copy into the clipboard, Excel or CSV) from the Overview page. On this page is no URL for an R script available, because triggering one of the buttons copies or downloads the data with the help of Javascript. So I had to download the file manually or to find another location where I could download it programmatically. I stored it as unsd-class.csv under the “data/country-class” folder.

I found with the OMNIKA DataStore an external source for the UNSD-M49 country classification. The OMNIKA DataStore is an open-access data science resource for researchers, authors, and technologists. OMNIKA Foundation is a 501c3 nonprofit whose mission is to digitize, organize, and make freely available important contents. For security reason I checked the two files with base::all.equal() to determine if those two files are identical. Yes, they are!

R Code A.11 : Numbered R Code Title

Run this code chunk manually if the file still needs to be downloaded.
Code
## download unsd-m49 file ############
url <- "https://github.com/omnika-datastore/unsd-m49-standard-area-codes/raw/refs/heads/main/2022-09-24__CSV_UNSD_M49.csv"

downloader::download(
    url = url,
    destfile = base::paste0(here::here(), 
            "/data/country-class/unsd-class.csv")
)


## create R object ###############
unsd_class <- 
  readr::read_delim(
    file = base::paste0(here::here(), 
    "/data/country-class/unsd-class.csv"),
    delim = ";"
  )


## save as .rds file ################
pb_save_data_file(
  "country-class", 
  unsd_class, 
  "unsd_class.rds")
(For this R code chunk is no output available)

A.2.3 Inspect classification files

To get an detailed understanding of the data structures I will provide the following outputs:

  1. A summary statistics with skimr::skim() followed by inspection of the first data with dplyr::glimpse().
  2. Several detailed outputs of the classifications categories (regions) and their elements (countries) in different code chunks (tabs).

A.2.3.1 World Bank

Code Collection A.2 : Inspect the structure of the World Bank classification

R Code A.12 : Inspect sheet List of Economies of the World Bank classification file

Code
wb_class_economies <- base::readRDS("data/country-class/wb_class_economies.rds")
glue::glue("******************* Using skimr::skim() ***************************")
skimr::skim(wb_class_economies)
glue::glue("")
glue::glue("****************** Using dplyr::glimpse() *************************")
dplyr::glimpse(wb_class_economies)
#> ******************* Using skimr::skim() ***************************
Data summary
Name wb_class_economies
Number of rows 267
Number of columns 5
_______________________
Column type frequency:
character 5
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Economy 1 1.00 4 50 0 266 0
Code 1 1.00 3 3 0 266 0
Region 49 0.82 10 26 0 7 0
Income group 50 0.81 10 19 0 4 0
Lending category 122 0.54 3 5 0 3 0 
#> 
#> ****************** Using dplyr::glimpse() *************************
#> Rows: 267
#> Columns: 5
#> $ Economy            <chr> "Afghanistan", "Albania", "Algeria", "American Samo…
#> $ Code               <chr> "AFG", "ALB", "DZA", "ASM", "AND", "AGO", "ATG", "A…
#> $ Region             <chr> "South Asia", "Europe & Central Asia", "Middle East…
#> $ `Income group`     <chr> "Low income", "Upper middle income", "Upper middle …
#> $ `Lending category` <chr> "IDA", "IBRD", "IBRD", NA, NA, "IBRD", "IBRD", "IBR…

R Code A.13 : Inspect sheet compositions of the World Bank classification file

Code
wb_class_compositions <- base::readRDS("data/country-class/wb_class_compositions.rds")
glue::glue("******************* Using skimr::skim() ***************************")
skimr::skim(wb_class_compositions)
glue::glue("")
glue::glue("****************** Using dplyr::glimpse() *************************")
dplyr::glimpse(wb_class_compositions)
#> ******************* Using skimr::skim() ***************************
Data summary
Name wb_class_compositions
Number of rows 2084
Number of columns 4
_______________________
Column type frequency:
character 4
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
WB_Group_Code 0 1 3 3 0 48 0
WB_Group_Name 0 1 5 50 0 48 0
WB_Country_Code 0 1 3 3 0 218 0
WB_Country_Name 0 1 4 30 0 218 0 
#> 
#> ****************** Using dplyr::glimpse() *************************
#> Rows: 2,084
#> Columns: 4
#> $ WB_Group_Code   <chr> "AFE", "AFE", "AFE", "AFE", "AFE", "AFE", "AFE", "AFE"…
#> $ WB_Group_Name   <chr> "Africa Eastern and Southern", "Africa Eastern and Sou…
#> $ WB_Country_Code <chr> "AGO", "BWA", "BDI", "COM", "COD", "ERI", "SWZ", "ETH"…
#> $ WB_Country_Name <chr> "Angola", "Botswana", "Burundi", "Comoros", "Congo, De…

R Code A.14 : Pre-defined standard categorization

Code
df_wb_standard <-  base::readRDS("data/country-class/wb_class_economies.rds") |> 
    dplyr::slice(1:218)



(
    wb_class_standard <- class_scheme(
            df = df_wb_standard,
            sel1 = rlang::quo(`Economy`),
            sel2 = rlang::quo(`Region`)
            )
)

Region is a coarse classification scheme with only 7 regions formed by 218 countries.

R Code A.15 : All provided groups, regional, economical and political

Code
df_wb_all <-  base::readRDS("data/country-class/wb_class_compositions.rds")


(
    wb_class_all <- class_scheme(
            df = df_wb_all,
            sel1 = rlang::quo(`WB_Country_Name`),
            sel2 = rlang::quo(`WB_Group_Name`)
            )
)

WB_Group_Name in the “compositions” file contains all available groups. They are not restricted to regional groups because they are formed by economical and political criteria as well. There is no 1:1 match, because almost all countries belong to two or more groups. There are 48 groups with a total of 2084 elements.

R Code A.16 : Groups formed by regional criteria (without the redundant World region)

Code
str_reg <- c("AFE", "AFW", "ARB", "CSS", "CEB",
             "EAS", "ECS", "LCN", "MEA", "NAC",
             "OSS", "PSS", "SST", "SAS", "SSF")

df_wb_reg <-  base::readRDS("data/country-class/wb_class_compositions.rds") |> 
  dplyr::filter(WB_Group_Code %in% str_reg)

(
    wb_class_reg <- class_scheme(
            df = df_wb_reg,
            sel1 = rlang::quo(`WB_Country_Name`),
            sel2 = rlang::quo(`WB_Group_Name`)
            )
)

Browsing through the composition data I have defined 15 WB_GROUP_CODEs as regional codes. These regional classification criteria results per definition to 15 regions containing 379 countries.

R Code A.17 : Groups formed by regional criteria (without the redundant World region)

Code
str_reg2 <- c("AFE", "AFW", "ARB", "CEB",
             "EAS", "ECS", "LCN", "MEA", "NAC",
             "SAS", "SSF")

df_wb_reg2 <-  base::readRDS("data/country-class/wb_class_compositions.rds") |> 
  dplyr::filter(WB_Group_Code %in% str_reg2)

(
    wb_class_reg2 <- class_scheme(
            df = df_wb_reg2,
            sel1 = rlang::quo(`WB_Country_Name`),
            sel2 = rlang::quo(`WB_Group_Name`)
            )
)

Browsing through the composition data I have declassified all small states for an alternative regional group. These regional classification criteria are smaller and results to 11 regions containing 299 countries.

A.2.3.1.1 Description of the four tabs
  1. WB economies displays the “List of Economies” and has five columns:
    • Economy with the country names (2-219) and regional names (221-268)
    • Code with the ISO alpha3 codes for countries (2-219) and for the regional names (221-268)
    • Region with seven different regional names:
      • East Asia and Pacific,
      • Europe and Central Asia,
      • Latin America & the Caribbean,
      • Middle East and North Africa,
      • North America
      • South Asia and
      • Sub-Saharan Africa
    • Income group with four groups: Low income, Lower middle income, Higher middle income, and High income.
    • Lending category with three groups: IBRD, Blend, and IDA.
  2. WB compositions has four columns: WB_Group_Code, WB_Group_Name, WB_Country_Code, WB_Country_Name. The 2084 rows are combinations of the regional and income group with their ISO alpha 3 codes and country names.
  3. WB Standard shows the World Bank seven standard regional groups with their countries. The 218 countries involved in the taxonomy of the World Bank consists of all member countries of the World Bank (189) and other economies with populations of more than 30,000 (29).
  4. WB All includes the seven regions from the “WB Standard” tab but much more. But it is important to note that there is no alternative regional structure comprises systematically all countries of the world — the overall category “World” obviously excluded.
    • Five of the seven regional groups of “WB Standard” are also clustered without high income countries.
    • There are six other regional subcategories: “Arab World”, “Caribbean small states”, “Central Europe and Baltics”, “Other small states”, “Pacific island small states”, “Small states”.
    • Additionally there are some political groups like European Union, OECD and
    • several economical classification like “Euro area”,
    • different combinations of the four income groups and different combinations of the three lending statuses.

More details

The cut off limits for the income groups are:

  • low income, $1,145 or less;
  • lower middle income, $1,146 to $4,515;
  • upper middle income, $4,516 to $14,005; and
  • high income, more than $14,005.

The effective operational cutoff for IDA eligibility is $1,335 or less. The three lending categories and their relation to each other are:

IDA countries are those that lack the financial ability to borrow from IBRD. IDA credits are deeply concessional—interest-free loans and grants for programs aimed at boosting economic growth and improving living conditions. IBRD loans are non-concessional. Blend countries are eligible for IDA credits because of their low per capita incomes but are also eligible for IBRD because they are financially creditworthy.

Three additional remark relating to the Notes sheet:

  1. In the Notes I found the sentence: “Geographic classifications in this table cover all income levels.” But there is a difference of one missing data value more in the Income group column compared with the Region column (50:49). The reason is that Venezuela RB is lacking an income group because it has been temporarily unclassified since July 2021 pending release of revised national accounts statistics. Venezuela, RB was classified as an upper-middle income country until FY21, has been unclassified since then due to the unavailability of data. But it is now again classified as Upper middle income (See the World Bank page about Venezuela, RB).

  2. The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics.

  3. What follows is a quote about some details of the income classifications for the 2023 file:

Set on 1 July 2022 remain in effect until 1 July 2023. Venezuela has been temporarily unclassified since July 2021 pending release of revised national accounts statistics. Argentina, which was temporarily unclassified in July 2016 pending release of revised national accounts statistics, was classified as upper middle income for FY17 as of 29 September 2016 based on alternative conversion factors. Also effective 29 September 2016, Syrian Arab Republic is reclassified from IBRD lending category to IDA-only. On 29 March 2017, new country codes were introduced to align World Bank 3-letter codes with ISO 3-letter codes: Andorra (AND), Dem. Rep. Congo (COD), Isle of Man (IMN), Kosovo (XKX), Romania (ROU), Timor-Leste (TLS), and West Bank and Gaza (PSE). It is to be noted that Venezuela, RB classified as an upper-middle income country until FY21, has been unclassified since then due to the unavailability of data.

A.2.3.1.2 Summary

The only missing data in the columns Economy and Code corresponds to the empty line #220 that separates the country codes from the regional codes. The missing data in the other columns stem from the different structure of the second part (starting with row #221) of the data, which consists only of the two columns ‘Economy’ and ‘Code’.

Essentially this means that we have in the CLASS.xslx file two different data sets: One for economies and the other one to explicate regional, economical and political grouping codes. In the Excel sheet compositions you will find an extended list of all available group names and their three letter codes combined with the country names and their three letter codes. These group names comprise different kinds of regional groups but also names and codes for different combination of country incomes and lending categories.

All these groups may be of interests for analysis of different trends. But the regional (sub)groups of the compositions sheet do not add up to the complete number of countries (218). This is in contrast to the different regional groups of the WID database because all their regional groups (region1 = 5, region2 = 18, region4 = 10, and region5 = 8 groups) includes all countries (in this case: 216).

The World Bank file CLASS.xslx classifies all World Bank member countries (189), and all other economies with populations of more than 30,000 (29) in a coarse grid of only seven regions. For operational and analytical purposes, these economies are divided among income groups according to their gross national income (GNI) per capita in 2023, calculated using the World Bank Atlas method.

A.2.3.2 United Nations

Code Collection A.3 : Inspect UNSD-M49 geoscheme classification

R Code A.18 : Inspect UNSD M49 geoscheme classification

Code
unsd_class <- base::readRDS("data/country-class/unsd_class.rds")
glue::glue("******************* Using skimr::skim() ***************************")
skimr::skim(unsd_class)
glue::glue("")
glue::glue("****************** Using dplyr::glimpse() *************************")
dplyr::glimpse(unsd_class)
#> ******************* Using skimr::skim() ***************************
Data summary
Name unsd_class
Number of rows 249
Number of columns 15
_______________________
Column type frequency:
character 15
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Global Code 0 1.00 3 3 0 1 0
Global Name 0 1.00 5 5 0 1 0
Region Code 1 1.00 3 3 0 5 0
Region Name 1 1.00 4 8 0 5 0
Sub-region Code 1 1.00 3 3 0 17 0
Sub-region Name 1 1.00 9 31 0 17 0
Intermediate Region Code 141 0.43 3 3 0 8 0
Intermediate Region Name 141 0.43 9 15 0 8 0
Country or Area 0 1.00 4 52 0 249 0
M49 Code 0 1.00 3 3 0 249 0
ISO-alpha2 Code 2 0.99 2 2 0 247 0
ISO-alpha3 Code 1 1.00 3 3 0 248 0
Least Developed Countries (LDC) 203 0.18 1 1 0 1 0
Land Locked Developing Countries (LLDC) 217 0.13 1 1 0 1 0
Small Island Developing States (SIDS) 196 0.21 1 1 0 1 0 
#> 
#> ****************** Using dplyr::glimpse() *************************
#> Rows: 249
#> Columns: 15
#> $ `Global Code`                             <chr> "001", "001", "001", "001", …
#> $ `Global Name`                             <chr> "World", "World", "World", "…
#> $ `Region Code`                             <chr> "002", "002", "002", "002", …
#> $ `Region Name`                             <chr> "Africa", "Africa", "Africa"…
#> $ `Sub-region Code`                         <chr> "015", "015", "015", "015", …
#> $ `Sub-region Name`                         <chr> "Northern Africa", "Northern…
#> $ `Intermediate Region Code`                <chr> NA, NA, NA, NA, NA, NA, NA, …
#> $ `Intermediate Region Name`                <chr> NA, NA, NA, NA, NA, NA, NA, …
#> $ `Country or Area`                         <chr> "Algeria", "Egypt", "Libya",…
#> $ `M49 Code`                                <chr> "012", "818", "434", "504", …
#> $ `ISO-alpha2 Code`                         <chr> "DZ", "EG", "LY", "MA", "SD"…
#> $ `ISO-alpha3 Code`                         <chr> "DZA", "EGY", "LBY", "MAR", …
#> $ `Least Developed Countries (LDC)`         <chr> NA, NA, NA, NA, "x", NA, NA,…
#> $ `Land Locked Developing Countries (LLDC)` <chr> NA, NA, NA, NA, NA, NA, NA, …
#> $ `Small Island Developing States (SIDS)`   <chr> NA, NA, NA, NA, NA, NA, NA, …

R Code A.19 : Clean UNSD M49 geoscheme classification

Code
## column renaming vector ########
m49_cols = c(
  region_c = "Region Code", region_n = "Region Name",
  subr_c = "Sub-region Code", subr_n = "Sub-region Name", 
  midr_c = "Intermediate Region Code", midr_n = "Intermediate Region Name",
  country = "Country or Area", m49 = "M49 Code", 
  iso2 = "ISO-alpha2 Code", iso3 = "ISO-alpha3 Code",
  ldc = "Least Developed Countries (LDC)", 
  lldc = "Land Locked Developing Countries (LLDC)", 
  sids = "Small Island Developing States (SIDS)"
  )
  
## clean data ###############################
unsd_class <- base::readRDS("data/country-class/unsd_class.rds")
unsd_class_clean <- unsd_class |> 
  dplyr::select(-(1:2)) |> 
  dplyr::rename(tidyselect::all_of(m49_cols)) |> 
  dplyr::filter(country != "Antarctica") |> 
  dplyr::mutate(iso2 = base::ifelse(country == "Namibia", "NA", iso2)) |> 
  dplyr::relocate(country, .before = region_c) |> 
  # .x = anonymous function; "x" = value in cols of unsd_class
  dplyr::mutate(dplyr::across(
    ldc:sids, ~ dplyr::if_else(.x == "x", "1", "999", "0") 
    )) |> 
  dplyr::arrange(country)

## save new tibble ##########
pb_save_data_file(
  "country-class",
  unsd_class_clean,
  "unsd_class_clean.rds"
)


## prepare skimmers ##########
my_skim <- skimr::skim_with(
  character = skimr::sfl(
    whitespace = NULL,
    min = NULL,
    max = NULL,
    empty = NULL
    )
)


## display results ##########
unsd_class <- base::readRDS("data/country-class/unsd_class.rds")
glue::glue("******************* Using skimr::skim() ***************************")
my_skim(unsd_class_clean) |> dplyr::select(-complete_rate)
glue::glue("")
glue::glue("****************** Using dplyr::glimpse() *************************")
dplyr::glimpse(unsd_class_clean)
#> ******************* Using skimr::skim() ***************************
Data summary
Name unsd_class_clean
Number of rows 248
Number of columns 13
_______________________
Column type frequency:
character 13
________________________
Group variables None

Variable type: character

skim_variable n_missing n_unique
country 0 248
region_c 0 5
region_n 0 5
subr_c 0 17
subr_n 0 17
midr_c 140 8
midr_n 140 8
m49 0 248
iso2 1 247
iso3 1 247
ldc 0 2
lldc 0 2
sids 0 2 
#> 
#> ****************** Using dplyr::glimpse() *************************
#> Rows: 248
#> Columns: 13
#> $ country  <chr> "Afghanistan", "Albania", "Algeria", "American Samoa", "Andor…
#> $ region_c <chr> "142", "150", "002", "009", "150", "002", "019", "019", "019"…
#> $ region_n <chr> "Asia", "Europe", "Africa", "Oceania", "Europe", "Africa", "A…
#> $ subr_c   <chr> "034", "039", "015", "061", "039", "202", "419", "419", "419"…
#> $ subr_n   <chr> "Southern Asia", "Southern Europe", "Northern Africa", "Polyn…
#> $ midr_c   <chr> NA, NA, NA, NA, NA, "017", "029", "029", "005", NA, "029", NA…
#> $ midr_n   <chr> NA, NA, NA, NA, NA, "Middle Africa", "Caribbean", "Caribbean"…
#> $ m49      <chr> "004", "008", "012", "016", "020", "024", "660", "028", "032"…
#> $ iso2     <chr> "AF", "AL", "DZ", "AS", "AD", "AO", "AI", "AG", "AR", "AM", "…
#> $ iso3     <chr> "AFG", "ALB", "DZA", "ASM", "AND", "AGO", "AIA", "ATG", "ARG"…
#> $ ldc      <chr> "1", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "…
#> $ lldc     <chr> "1", "0", "0", "0", "0", "0", "0", "0", "0", "1", "0", "0", "…
#> $ sids     <chr> "0", "0", "0", "1", "0", "0", "1", "1", "0", "0", "1", "0", "…

R Code A.20 : Numbered R Code Title

Code
df_unsd <-  base::readRDS("data/country-class/unsd_class_clean.rds")

(
    unsd_class1 <- class_scheme(
            df = df_unsd,
            sel1 = rlang::quo(`country`),
            sel2 = rlang::quo(`region_n`)
            )
)

R Code A.21 : Numbered R Code Title

Code
df_unsd <-  base::readRDS("data/country-class/unsd_class_clean.rds")

(
    unsd_class2 <- class_scheme(
            df = df_unsd,
            sel1 = rlang::quo(`country`),
            sel2 = rlang::quo(`subr_n`)
            )
)

R Code A.22 : Numbered R Code Title

Code
df_unsd <-  base::readRDS("data/country-class/unsd_class_clean.rds")

(
    unsd_class3 <- class_scheme(
            df = df_unsd,
            sel1 = rlang::quo(`country`),
            sel2 = rlang::quo(`midr_n`)
            )
)

R Code A.23 : Numbered R Code Title

Code
unsd_class4 <-  base::readRDS("data/country-class/unsd_class_clean.rds")

unsd_class4 <- unsd_class4 |> 
  dplyr::mutate(midr_n2 = 
         base::ifelse(is.na(midr_n), subr_n, midr_n)
         )

(
    unsd_class4 <- class_scheme(
            df = unsd_class4,
            sel1 = rlang::quo(`country`),
            sel2 = rlang::quo(`midr_n2`)
            )
)

midr_n2 is a classification scheme with 248 countries in 23 regions.

A.2.3.2.1 Descriptions of the XY tabs

The file has 15 columns as you can also see online from the Overview page.

The many missing values (NAs) for the categories LDC, LLDC and SIDS are easy explained: These three columns are coded with an ‘x’ if the country of this row belong to this category. Recoding these three columns with 1 and 0 (1 = yes, belongs to this category, 0 = no, does not belong to this category) will reduce most of their missing values.

One missing value in the regional categories (Region, Sub-Region and Intermediate Region) is related to Antarctica which is not seen by the M49 scheme as a separated region. It has therefore no regional codes and names with the exception of the overall comprising global region. But it has M49 as well ISO-alpha codes.

One of the missing values for ISO-alpha2 and ISO-alpha3 is related to Sark, which is “recognized by the United Nations Statistics Division (UNSD) as a separate territory” but was not accepted by ISO now for more than 20 years (McCarthy 2020). Recently a new application (see PDF) will change that but currently Sark is still waiting for ISO 3166 codes.

The other missing value for ISO-alpha2 codes belongs to Namibia because its abbreviation NA is interpreted by R as a missing value!

To clean the data I will

  • Remove the global codes and names because they a redundant: All rows have global code “001” (“World”).
  • Rename the columns to get shorter names.
  • Remove Antarctica because it is not seen as separate country.
  • Replace NA in the column ISO-alpha2 Code” of Namibia with the string “NA”.
  • Recode the columns LDC, LLDC and SIDS with 0 and 1.
  • Relocate the column “country” (previously “Country or Area”) to the first column because than it easier to find some relevant content
  • Sort the data alphabetically by “country”.
A.2.3.2.2 Summary

A.3 XY STOPPED HERE - WHAT FOLLOW ARE SOME (OLDER) TEXT MODULES

A.3.1 Modul: Why looking at classification systems?

Only sometimes are they used identically in the data I will look at. So I also have to understand these data-driven differences (e.g., in the WID database or the WHR data) with the official country nomenclatures.

I could have used just the approach used in my data set. But that approach poses two problems:

  1. Not always are the classification used coherently in my available datasets. So uses WHR (slightly) different country names in their yearly reports. For instance “Turkiye” in 2024 and “Turkey” in 2012, but the official name is “Türkiye”. Instead of the current 27 member states of the European Union the WID assigns the label “European Union” to 32 countries, including still United Kingdom but also British Overseas Territory (Gibraltar, Montserrat),and self-governing British Crown dependencies (Isle of Man, Jersey) that never had been member status in the EU.
  2. Sometimes the available data use their own categorization schema. Most of the data analysis in the World Inequality Report 2022 (WIR2022) condense data into eight different regions: Europe, South & South-East Asia, East Asia, North America, Sub-Saharan Africa, Russia & Central Asia, MENA, Latin America. I couldn’t find exact this classification schema in one of the internationally approved taxonomies.
  3. Some of the groupings I am interested in (e.g., OECD, or countries that applied for EU membership status are not part of the international standardized statistical groupings.

A.3.2 Modul: UNSD M49 geoscheme preliminiary rsults

After some research I found out that the most detailed and for statistical purposes most relevant classification system is M49 standard, developed and maintained by the United Nations Statistics Division (UNSD). Guided by the United Nations Geoscheme it divides all countries of the world into different types of regions:

Reference is: Wikipedia map

  • 6 Regions: 6 continents:
    • Africa,
    • Americas,
    • Antarctica,
    • Asia,
    • Europe and
    • Oceania.
  • 22 Sub-regions: (Missing three in Appendix 1. United Nations Geoscheme: Polynesia, and other differentiation of the Americas: North America, Central America, Caribbean, South America )
    • Africa:
      • Northern Africa,
      • Eastern Africa,
      • Middle Africa,
      • Southern Africa,
      • Western Africa
    • Americas:
      • Latin America and the Caribbean,
      • Northern America
    • Asia:
      • Central Asia,
      • Eastern Asia,
      • Southeastern Asia,
      • Southern Asia,
      • Western Asia
    • Europe:
      • Eastern Europe,
      • Northern Europe,
      • Southern Europe,
      • Western Europe
    • Oceania:
      • Australia and New Zealand,
      • Melanesia,
      • Micronesia
  • Sub-regions (18 in M49, missing 4: Sub-Saharan Africa = Eastern Africa, Middle Africa, Southern Africa, and Western Africa, and Latin America = South America and Caribbean is separated)
    • “Northern Africa”
    • “Sub-Saharan Africa”
    • “Latin America and the Caribbean”
    • “Northern America”
    • “Central Asia”
    • “Eastern Asia”
    • “South-eastern Asia”
    • “Southern Asia”
    • “Western Asia”
    • “Eastern Europe”
    • “Northern Europe”
    • “Southern Europe”
    • “Western Europe”
    • “Australia and New Zealand”
    • “Melanesia”
    • “Micronesia”
    • “Polynesia”
  • Intermediate Regions: (2, but 7)
    • Sub-Saharan Africa: Eastern Africa, Middle Africa, Southern Africa and Western Africa
    • Latin America and the Caribbean: Caribbean, Central America, South America
Note A.1
  1. The UN geoscheme is a statistical classification and does not imply any political or other affiliation of countries or territories.
  2. It may differ from geographical definitions used by autonomous UN specialized agencies, such as the United Nations Industrial Development Organization and UNESCO, for their own organizational convenience.
  3. Other alternative groupings include the World Bank regional classification and CIA World Factbook regions, and Internet Corporation for Assigned Names and Numbers Geographic Regions.
Important A.1

How to get the 22 sub-regions from the m49 data?

  1. Take the Intermediate Region Name if it is not NA.
  2. If the Intermediate Region Name is NA then take the Sub-region name.

R Code A.24 : Download the M49 Geoscheme of the United Nations

Run this code chunk only once manually: It downloads the M49 Geoscheme of the United Nations as a .csv file and saves it as an R .rds object.

Code
## run this code chunk only once (manually)

## get the file from an external source, 
## https://github.com/omnika-datastore/unsd-m49-standard-area-codes
## OMNIKA DataStore is an open-access data science resource 
## for researchers, authors, and technologists interested in mythology. 
## Managed by OMNIKA Foundation which is a 501c3 nonprofit 
## whose mission is to digitize, organize, 
## and make freely available all the world's mythological contents.
## at the official UN site is no URL provided
## the file is downloaded via a javascript
## I checked the two files with base::all.equal()`
## they are identical 

## define variables
url <- "https://github.com/omnika-datastore/unsd-m49-standard-area-codes/raw/refs/heads/main/2022-09-24__CSV_UNSD_M49.csv"
folder = "country-class"

## create folder for chapter if not already done
data_folder <-
        base::paste0(
            here::here(),
            paste0("/data/", folder, "/")
        )
    if (!base::file.exists(data_folder))
    {base::dir.create(data_folder)}

## get the UNSD M49 .csv file from the OMNIKA Foundation GitHub
destfile <- base::paste0(data_folder, "unsd_m49.csv")
utils::download.file(url, destfile)
tmp <- readr::read_csv2(destfile)


## save original file without changes
pb_save_data_file("country-class", tmp, "unsd_m49.rds")

A.3.3 Modul: WIR2022 data inspection & exploration

List regions of the WIR2022 and their countries

The World Inequality Report 2022 (WIR2022) describes global trends in inequality. It mostly condense data in eight different regions. See the following graph as an example:

The image shows the income shares for the bottom 50%, middle 40% and top 10% in 2021 dividing the world into eight different regions. With the exception of Europe the top 10% earn the biggest share of the income with the following order from lowest to the highest 10%: Europe, East Asia, North America, Russia & Central Asia, South & South-East Asia, Latin America, Sub-Saharan Africa, MENA (Middle East & North Africa). Example: In Latin America the top 10% captures 55% of national income compared to 36% of Europe.
Graph A.1: A typical example for a graph in the World Inequality Report 2022 (WIR2022), showing the division of the world into eight different world regions (“MENA” stands for Middle East & North Arfica).

In this appendix I want to know the countries that form each of these eight regions.

A.4 Data

My first task was to look for data where I could extract the information I am interested in. I scanned the files of the free accessible GitHub repository of the WID. I found country-codes-core.xlsx, an Excel file with the data I am looking for. It is sorted by a two letter code in the first column named appropriately code. “Core” means – in contrast to other country-codes files – that it includes a column corecountry where the number 1 functions as a marker for a core country. The file itself has many hidden rows which feature either regions smaller than a country (like Alabama for US or Bavaria for Germany) or bigger than a country (like Asia or Western Europe).

Important

country-codes-core.xlsx contains many hidden rows. It is therefore necessary to filter by corecountry == 1.

A.4.1 Download data

The following code chunk is only applied once. The file contains rows with all two letter combinations. Many of these combinations do not refer to an existing country. To eliminate these records one needs to select corecountry == 1.

The following chunk downloads the dataset from the internet and saves this Excel file untouched. I selected only rows for core-countries and converted all string columns to factor variable. I stored the result as country-codes.rds.

R Code A.25 : Download the country-codes-core file, manipulate and save it

Code
## run this code chunk only once (manually)

## define variables
url <- "https://github.com/WIDworld/wid-world/raw/master/data-input/country-codes/country-codes-core.xlsx"
chapter_folder = "chap80"

## create folder for chapter if not already done
chap_folder <-
        base::paste0(
            here::here(),
            paste0("/data/", chapter_folder, "/")
        )
    if (!base::file.exists(chap_folder))
    {base::dir.create(chap_folder)}

## get country-codes-core.xlsx
destfile <- base::paste0(chap_folder, "country-codes-core.xlsx")
utils::download.file(url, destfile)
tmp <- readxl::read_xlsx(destfile)


country_codes <- tmp |> 
    ## filter for core countries
    dplyr::filter(corecountry == 1) |> 
    ## convert all region columns to factor variables
    dplyr::mutate(dplyr::across(
        tidyselect::starts_with("region"), forcats::as_factor)
        )

## save cleaned data
pb_save_data_file("chap80", country_codes, "country_codes.rds")

(For this R code chunk is no output available)

A.4.2 Eplore Data

An inspection of the data file shows that column region5 contains the regions used in WIR2022.

R Code A.26 : Explore Data

Code
country_codes <- base::readRDS("data/chap80/country_codes.rds")

skimr::skim(country_codes)

glue::glue(" ")
glue::glue("############################################################")
glue::glue("Display number of countries for each region")
glue::glue("############################################################")
glue::glue(" ")
country_codes |> 
    dplyr::pull(region5) |> 
    forcats::fct_count()
Data summary
Name country_codes
Number of rows 216
Number of columns 10
_______________________
Column type frequency:
character 3
factor 5
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
code 0 1 2 2 0 216 0
titlename 0 1 4 32 0 216 0
shortname 0 1 3 32 0 216 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
region1 0 1.00 FALSE 5 Afr: 54, Asi: 53, Ame: 47, Eur: 46
region2 0 1.00 FALSE 18 Wes: 27, Car: 23, Wes: 20, Eas: 19
region3 184 0.15 FALSE 1 Eur: 32
region4 24 0.89 FALSE 9 Oth: 48, Oth: 38, Oth: 22, Oth: 18
region5 0 1.00 FALSE 8 Sub: 49, Eur: 46, Lat: 43, MEN: 20

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
corecountry 0 1 1.00 0.00 1 1 1 1 1 ▁▁▇▁▁
TH 0 1 0.19 0.39 0 0 0 0 1 ▇▁▁▁▂ 
#>  
#> ############################################################
#> Display number of countries for each region
#> ############################################################
#>  
#> # A tibble: 8 × 2
#>   f                           n
#>   <fct>                   <int>
#> 1 Europe                     46
#> 2 MENA                       20
#> 3 South & South-East Asia    20
#> 4 Latin America              43
#> 5 Russia & Central Asia      11
#> 6 Sub-Saharan Africa         49
#> 7 North America & Oceania    19
#> 8 East Asia                   8

A.5 List countries

Code Collection A.4 : List countries for each regions used in WIR2022

R Code A.27 : Countries of the European region used in the WIR2022

Code
wir2022_country_codes <- country_codes |> 
    dplyr::select(code, shortname, tidyselect::starts_with("region"))

wir2022_country_codes |> 
    dplyr::filter(region5 == "Europe") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 46 × 5
#>    code  shortname              region1 region2        region4             
#>    <chr> <chr>                  <fct>   <fct>          <fct>               
#>  1 AD    Andorra                Europe  Western Europe Other Western Europe
#>  2 AL    Albania                Europe  Eastern Europe Other Eastern Europe
#>  3 AT    Austria                Europe  Western Europe Other Western Europe
#>  4 BA    Bosnia and Herzegovina Europe  Eastern Europe Other Eastern Europe
#>  5 BE    Belgium                Europe  Western Europe Other Western Europe
#>  6 BG    Bulgaria               Europe  Eastern Europe Other Eastern Europe
#>  7 CH    Switzerland            Europe  Western Europe Other Western Europe
#>  8 CY    Cyprus                 Europe  Eastern Europe Other Eastern Europe
#>  9 CZ    Czech Republic         Europe  Eastern Europe Other Eastern Europe
#> 10 DE    Germany                Europe  Western Europe <NA>                
#> 11 DK    Denmark                Europe  Western Europe Other Western Europe
#> 12 EE    Estonia                Europe  Eastern Europe Other Eastern Europe
#> 13 ES    Spain                  Europe  Western Europe <NA>                
#> 14 FI    Finland                Europe  Western Europe Other Western Europe
#> 15 FR    France                 Europe  Western Europe <NA>                
#> 16 GB    United Kingdom         Europe  Western Europe <NA>                
#> 17 GG    Guernsey               Europe  Western Europe Other Western Europe
#> 18 GI    Gibraltar              Europe  Western Europe Other Western Europe
#> 19 GR    Greece                 Europe  Western Europe Other Western Europe
#> 20 HR    Croatia                Europe  Eastern Europe Other Eastern Europe
#> 21 HU    Hungary                Europe  Eastern Europe Other Eastern Europe
#> 22 IE    Ireland                Europe  Western Europe Other Western Europe
#> 23 IM    Isle of Man            Europe  Western Europe Other Western Europe
#> 24 IS    Iceland                Europe  Western Europe Other Western Europe
#> 25 IT    Italy                  Europe  Western Europe <NA>                
#> 26 JE    Jersey                 Europe  Western Europe Other Western Europe
#> 27 KS    Kosovo                 Europe  Eastern Europe Other Western Europe
#> 28 LI    Liechtenstein          Europe  Western Europe Other Western Europe
#> 29 LT    Lithuania              Europe  Eastern Europe Other Eastern Europe
#> 30 LU    Luxembourg             Europe  Western Europe Other Western Europe
#> 31 LV    Latvia                 Europe  Eastern Europe Other Eastern Europe
#> 32 MC    Monaco                 Europe  Western Europe Other Western Europe
#> 33 MD    Moldova                Europe  Eastern Europe Other Eastern Europe
#> 34 ME    Montenegro             Europe  Eastern Europe Other Eastern Europe
#> 35 MK    North Macedonia        Europe  Eastern Europe Other Eastern Europe
#> 36 MT    Malta                  Europe  Western Europe Other Western Europe
#> 37 NL    Netherlands            Europe  Western Europe Other Western Europe
#> 38 NO    Norway                 Europe  Western Europe Other Western Europe
#> 39 PL    Poland                 Europe  Eastern Europe Other Eastern Europe
#> 40 PT    Portugal               Europe  Western Europe Other Western Europe
#> 41 RO    Romania                Europe  Eastern Europe Other Eastern Europe
#> 42 RS    Serbia                 Europe  Eastern Europe Other Eastern Europe
#> 43 SE    Sweden                 Europe  Western Europe <NA>                
#> 44 SI    Slovenia               Europe  Eastern Europe Other Eastern Europe
#> 45 SK    Slovakia               Europe  Eastern Europe Other Eastern Europe
#> 46 SM    San Marino             Europe  Western Europe Other Western Europe

R Code A.28 : Countries of the European Union

Code
wir2022_country_codes <- country_codes |> 
    dplyr::select(code, shortname, tidyselect::starts_with("region"))

wir2022_country_codes |> 
    dplyr::filter(region3 == "European Union") |> 
    dplyr::select(-region5, -region3) |> 
    dplyr::arrange(shortname) |> 
    print(n = 50)
#> # A tibble: 32 × 5
#>    code  shortname      region1  region2        region4             
#>    <chr> <chr>          <fct>    <fct>          <fct>               
#>  1 AT    Austria        Europe   Western Europe Other Western Europe
#>  2 BE    Belgium        Europe   Western Europe Other Western Europe
#>  3 BG    Bulgaria       Europe   Eastern Europe Other Eastern Europe
#>  4 HR    Croatia        Europe   Eastern Europe Other Eastern Europe
#>  5 CY    Cyprus         Europe   Eastern Europe Other Eastern Europe
#>  6 CZ    Czech Republic Europe   Eastern Europe Other Eastern Europe
#>  7 DK    Denmark        Europe   Western Europe Other Western Europe
#>  8 EE    Estonia        Europe   Eastern Europe Other Eastern Europe
#>  9 FI    Finland        Europe   Western Europe Other Western Europe
#> 10 FR    France         Europe   Western Europe <NA>                
#> 11 DE    Germany        Europe   Western Europe <NA>                
#> 12 GI    Gibraltar      Europe   Western Europe Other Western Europe
#> 13 GR    Greece         Europe   Western Europe Other Western Europe
#> 14 HU    Hungary        Europe   Eastern Europe Other Eastern Europe
#> 15 IE    Ireland        Europe   Western Europe Other Western Europe
#> 16 IM    Isle of Man    Europe   Western Europe Other Western Europe
#> 17 IT    Italy          Europe   Western Europe <NA>                
#> 18 JE    Jersey         Europe   Western Europe Other Western Europe
#> 19 LV    Latvia         Europe   Eastern Europe Other Eastern Europe
#> 20 LT    Lithuania      Europe   Eastern Europe Other Eastern Europe
#> 21 LU    Luxembourg     Europe   Western Europe Other Western Europe
#> 22 MT    Malta          Europe   Western Europe Other Western Europe
#> 23 MS    Montserrat     Americas Caribbean      Other Latin America 
#> 24 NL    Netherlands    Europe   Western Europe Other Western Europe
#> 25 PL    Poland         Europe   Eastern Europe Other Eastern Europe
#> 26 PT    Portugal       Europe   Western Europe Other Western Europe
#> 27 RO    Romania        Europe   Eastern Europe Other Eastern Europe
#> 28 SK    Slovakia       Europe   Eastern Europe Other Eastern Europe
#> 29 SI    Slovenia       Europe   Eastern Europe Other Eastern Europe
#> 30 ES    Spain          Europe   Western Europe <NA>                
#> 31 SE    Sweden         Europe   Western Europe <NA>                
#> 32 GB    United Kingdom Europe   Western Europe <NA>

R Code A.29 : Countries of the MENA region (Middle East & North Afrika)

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "MENA") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 20 × 5
#>    code  shortname            region1 region2      region4   
#>    <chr> <chr>                <fct>   <fct>        <fct>     
#>  1 AE    United Arab Emirates Asia    West Asia    Other MENA
#>  2 BH    Bahrain              Asia    West Asia    Other MENA
#>  3 DZ    Algeria              Africa  North Africa <NA>      
#>  4 EG    Egypt                Africa  North Africa <NA>      
#>  5 IL    Israel               Asia    West Asia    Other MENA
#>  6 IQ    Iraq                 Asia    West Asia    Other MENA
#>  7 IR    Iran                 Asia    South Asia   Other MENA
#>  8 JO    Jordan               Asia    West Asia    Other MENA
#>  9 KW    Kuwait               Asia    West Asia    Other MENA
#> 10 LB    Lebanon              Asia    West Asia    Other MENA
#> 11 LY    Libya                Africa  North Africa Other MENA
#> 12 MA    Morocco              Africa  North Africa Other MENA
#> 13 OM    Oman                 Asia    West Asia    Other MENA
#> 14 PS    Palestine            Asia    West Asia    Other MENA
#> 15 QA    Qatar                Asia    West Asia    Other MENA
#> 16 SA    Saudi Arabia         Asia    West Asia    Other MENA
#> 17 SY    Syrian Arab Republic Asia    West Asia    Other MENA
#> 18 TN    Tunisia              Africa  North Africa Other MENA
#> 19 TR    Turkey               Asia    West Asia    <NA>      
#> 20 YE    Yemen                Asia    West Asia    Other MENA

R Code A.30 : Countries of the South & South-East Asia region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "South & South-East Asia") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 20 × 5
#>    code  shortname         region1 region2                               region4
#>    <chr> <chr>             <fct>   <fct>                                 <fct>  
#>  1 AF    Afghanistan       Asia    South Asia                            Other …
#>  2 BD    Bangladesh        Asia    South Asia                            Other …
#>  3 BN    Brunei Darussalam Asia    South-East Asia                       Other …
#>  4 BT    Bhutan            Asia    South Asia                            Other …
#>  5 ID    Indonesia         Asia    South-East Asia                       <NA>   
#>  6 IN    India             Asia    South Asia                            <NA>   
#>  7 KH    Cambodia          Asia    South-East Asia                       Other …
#>  8 LA    Lao PDR           Asia    South-East Asia                       Other …
#>  9 LK    Sri Lanka         Asia    South Asia                            Other …
#> 10 MM    Myanmar           Asia    South-East Asia                       Other …
#> 11 MV    Maldives          Asia    South Asia                            Other …
#> 12 MY    Malaysia          Asia    South-East Asia                       Other …
#> 13 NP    Nepal             Asia    South Asia                            Other …
#> 14 PG    Papua New Guinea  Oceania Oceania (excl. Australia and New Zea… Other …
#> 15 PH    Philippines       Asia    South-East Asia                       Other …
#> 16 PK    Pakistan          Asia    South Asia                            Other …
#> 17 SG    Singapore         Asia    South-East Asia                       Other …
#> 18 TH    Thailand          Asia    South-East Asia                       Other …
#> 19 TL    Timor-Leste       Asia    South-East Asia                       Other …
#> 20 VN    Viet Nam          Asia    South-East Asia                       Other …

R Code A.31 : Countries of the Latin America region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "Latin America") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 43 × 5
#>    code  shortname                        region1  region2         region4      
#>    <chr> <chr>                            <fct>    <fct>           <fct>        
#>  1 AG    Antigua and Barbuda              Americas Caribbean       Other Latin …
#>  2 AI    Anguilla                         Americas Caribbean       Other Latin …
#>  3 AR    Argentina                        Americas South America   <NA>         
#>  4 AW    Aruba                            Americas Caribbean       Other Latin …
#>  5 BB    Barbados                         Americas Caribbean       Other Latin …
#>  6 BO    Bolivia                          Americas South America   Other Latin …
#>  7 BQ    Bonaire, Sint Eustatius and Saba Americas Caribbean       Other Latin …
#>  8 BR    Brazil                           Americas South America   <NA>         
#>  9 BS    Bahamas                          Americas Caribbean       Other Latin …
#> 10 BZ    Belize                           Americas Central America Other Latin …
#> 11 CL    Chile                            Americas South America   <NA>         
#> 12 CO    Colombia                         Americas South America   <NA>         
#> 13 CR    Costa Rica                       Americas Central America Other Latin …
#> 14 CU    Cuba                             Americas Caribbean       Other Latin …
#> 15 CW    Curacao                          Americas Caribbean       Other Latin …
#> 16 DM    Dominica                         Americas Caribbean       Other Latin …
#> 17 DO    Dominican Republic               Americas Caribbean       Other Latin …
#> 18 EC    Ecuador                          Americas South America   Other Latin …
#> 19 GD    Grenada                          Americas Caribbean       Other Latin …
#> 20 GT    Guatemala                        Americas Central America Other Latin …
#> 21 GY    Guyana                           Americas South America   Other Latin …
#> 22 HN    Honduras                         Americas Central America Other Latin …
#> 23 HT    Haiti                            Americas Caribbean       Other Latin …
#> 24 JM    Jamaica                          Americas Caribbean       Other Latin …
#> 25 KN    Saint Kitts and Nevis            Americas Caribbean       Other Latin …
#> 26 KY    Cayman Islands                   Americas Caribbean       Other Latin …
#> 27 LC    Saint Lucia                      Americas Caribbean       Other Latin …
#> 28 MS    Montserrat                       Americas Caribbean       Other Latin …
#> 29 MX    Mexico                           Americas Central America <NA>         
#> 30 NI    Nicaragua                        Americas Central America Other Latin …
#> 31 PA    Panama                           Americas Central America Other Latin …
#> 32 PE    Peru                             Americas South America   Other Latin …
#> 33 PR    Puerto Rico                      Americas Caribbean       Other Latin …
#> 34 PY    Paraguay                         Americas South America   Other Latin …
#> 35 SR    Suriname                         Americas South America   Other Latin …
#> 36 SV    El Salvador                      Americas Central America Other Latin …
#> 37 SX    Sint Maarten (Dutch part)        Americas Caribbean       Other Latin …
#> 38 TC    Turks and Caicos Islands         Americas Caribbean       Other Latin …
#> 39 TT    Trinidad and Tobago              Americas Caribbean       Other Latin …
#> 40 UY    Uruguay                          Americas South America   Other Latin …
#> 41 VC    Saint Vincent and the Grenadines Americas Caribbean       Other Latin …
#> 42 VE    Venezuela                        Americas South America   Other Latin …
#> 43 VG    Virgin Islands, British          Americas Caribbean       Other Latin …

R Code A.32 : Countries of the Russia & Central Asia region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "Russia & Central Asia") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 11 × 5
#>    code  shortname          region1 region2      region4                    
#>    <chr> <chr>              <fct>   <fct>        <fct>                      
#>  1 AM    Armenia            Asia    West Asia    Other Russia & Central Asia
#>  2 AZ    Azerbaijan         Asia    West Asia    Other Russia & Central Asia
#>  3 BY    Belarus            Asia    West Asia    Other Russia & Central Asia
#>  4 GE    Georgia            Asia    West Asia    Other Russia & Central Asia
#>  5 KG    Kyrgyzstan         Asia    Central Asia Other Russia & Central Asia
#>  6 KZ    Kazakhstan         Asia    Central Asia Other Russia & Central Asia
#>  7 RU    Russian Federation Asia    West Asia    <NA>                       
#>  8 TJ    Tajikistan         Asia    Central Asia Other Russia & Central Asia
#>  9 TM    Turkmenistan       Asia    Central Asia Other Russia & Central Asia
#> 10 UA    Ukraine            Asia    West Asia    Other Russia & Central Asia
#> 11 UZ    Uzbekistan         Asia    Central Asia Other Russia & Central Asia

R Code A.33 : Countries of the Sub-Saharan African region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "Sub-Saharan Africa") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 49 × 5
#>    code  shortname                region1 region2       region4                 
#>    <chr> <chr>                    <fct>   <fct>         <fct>                   
#>  1 AO    Angola                   Africa  Middle Africa Other Sub-Saharan Africa
#>  2 BF    Burkina Faso             Africa  West Africa   Other Sub-Saharan Africa
#>  3 BI    Burundi                  Africa  East Africa   Other Sub-Saharan Africa
#>  4 BJ    Benin                    Africa  West Africa   Other Sub-Saharan Africa
#>  5 BW    Botswana                 Africa  South Africa  Other Sub-Saharan Africa
#>  6 CD    DR Congo                 Africa  Middle Africa Other Sub-Saharan Africa
#>  7 CF    Central African Republic Africa  Middle Africa Other Sub-Saharan Africa
#>  8 CG    Congo                    Africa  Middle Africa Other Sub-Saharan Africa
#>  9 CI    Cote d'Ivoire            Africa  West Africa   Other Sub-Saharan Africa
#> 10 CM    Cameroon                 Africa  Middle Africa Other Sub-Saharan Africa
#> 11 CV    Cabo Verde               Africa  West Africa   Other Sub-Saharan Africa
#> 12 DJ    Djibouti                 Africa  East Africa   Other Sub-Saharan Africa
#> 13 ER    Eritrea                  Africa  East Africa   Other Sub-Saharan Africa
#> 14 ET    Ethiopia                 Africa  East Africa   Other Sub-Saharan Africa
#> 15 GA    Gabon                    Africa  Middle Africa Other Sub-Saharan Africa
#> 16 GH    Ghana                    Africa  West Africa   Other Sub-Saharan Africa
#> 17 GM    Gambia                   Africa  West Africa   Other Sub-Saharan Africa
#> 18 GN    Guinea                   Africa  West Africa   Other Sub-Saharan Africa
#> 19 GQ    Equatorial Guinea        Africa  Middle Africa Other Sub-Saharan Africa
#> 20 GW    Guinea-Bissau            Africa  West Africa   Other Sub-Saharan Africa
#> 21 KE    Kenya                    Africa  East Africa   Other Sub-Saharan Africa
#> 22 KM    Comoros                  Africa  East Africa   Other Sub-Saharan Africa
#> 23 LR    Liberia                  Africa  West Africa   Other Sub-Saharan Africa
#> 24 LS    Lesotho                  Africa  South Africa  Other Sub-Saharan Africa
#> 25 MG    Madagascar               Africa  East Africa   Other Sub-Saharan Africa
#> 26 ML    Mali                     Africa  West Africa   Other Sub-Saharan Africa
#> 27 MR    Mauritania               Africa  West Africa   Other Sub-Saharan Africa
#> 28 MU    Mauritius                Africa  East Africa   Other Sub-Saharan Africa
#> 29 MW    Malawi                   Africa  East Africa   Other Sub-Saharan Africa
#> 30 MZ    Mozambique               Africa  East Africa   Other Sub-Saharan Africa
#> 31 NA    Namibia                  Africa  South Africa  Other Sub-Saharan Africa
#> 32 NE    Niger                    Africa  West Africa   Other Sub-Saharan Africa
#> 33 NG    Nigeria                  Africa  West Africa   Other Sub-Saharan Africa
#> 34 RW    Rwanda                   Africa  East Africa   Other Sub-Saharan Africa
#> 35 SC    Seychelles               Africa  East Africa   Other Sub-Saharan Africa
#> 36 SD    Sudan                    Africa  North Africa  Other Sub-Saharan Africa
#> 37 SL    Sierra Leone             Africa  West Africa   Other Sub-Saharan Africa
#> 38 SN    Senegal                  Africa  West Africa   Other Sub-Saharan Africa
#> 39 SO    Somalia                  Africa  East Africa   Other Sub-Saharan Africa
#> 40 SS    South Sudan              Africa  North Africa  Other Sub-Saharan Africa
#> 41 ST    Sao Tome and Principe    Africa  Middle Africa Other Sub-Saharan Africa
#> 42 SZ    Swaziland                Africa  South Africa  Other Sub-Saharan Africa
#> 43 TD    Chad                     Africa  Middle Africa Other Sub-Saharan Africa
#> 44 TG    Togo                     Africa  West Africa   Other Sub-Saharan Africa
#> 45 TZ    Tanzania                 Africa  East Africa   Other Sub-Saharan Africa
#> 46 UG    Uganda                   Africa  East Africa   Other Sub-Saharan Africa
#> 47 ZA    South Africa             Africa  South Africa  <NA>                    
#> 48 ZM    Zambia                   Africa  East Africa   Other Sub-Saharan Africa
#> 49 ZW    Zimbabwe                 Africa  East Africa   Other Sub-Saharan Africa

R Code A.34 : Countries of the North America & Oceania region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "North America & Oceania") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 19 × 5
#>    code  shortname        region1  region2                               region4
#>    <chr> <chr>            <fct>    <fct>                                 <fct>  
#>  1 AU    Australia        Oceania  Australia and New Zealand             <NA>   
#>  2 BM    Bermuda          Americas North America                         Other …
#>  3 CA    Canada           Americas North America                         <NA>   
#>  4 FJ    Fiji             Oceania  Oceania (excl. Australia and New Zea… Other …
#>  5 FM    Micronesia       Oceania  Oceania (excl. Australia and New Zea… Other …
#>  6 GL    Greenland        Americas North America                         Other …
#>  7 KI    Kiribati         Oceania  Oceania (excl. Australia and New Zea… Other …
#>  8 MH    Marshall Islands Oceania  Oceania (excl. Australia and New Zea… Other …
#>  9 NC    New Caledonia    Oceania  Oceania (excl. Australia and New Zea… Other …
#> 10 NR    Nauru            Oceania  Oceania (excl. Australia and New Zea… Other …
#> 11 NZ    New Zealand      Oceania  Australia and New Zealand             <NA>   
#> 12 PF    French Polynesia Oceania  Oceania (excl. Australia and New Zea… Other …
#> 13 PW    Palau            Oceania  Oceania (excl. Australia and New Zea… Other …
#> 14 SB    Solomon Islands  Oceania  Oceania (excl. Australia and New Zea… Other …
#> 15 TO    Tonga            Oceania  Oceania (excl. Australia and New Zea… Other …
#> 16 TV    Tuvalu           Oceania  Oceania (excl. Australia and New Zea… Other …
#> 17 US    USA              Americas North America                         <NA>   
#> 18 VU    Vanuatu          Oceania  Oceania (excl. Australia and New Zea… Other …
#> 19 WS    Samoa            Oceania  Oceania (excl. Australia and New Zea… Other …

R Code A.35 : Countries of the East Asia region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "East Asia") |> 
    dplyr::select(-region5, -region3) |> 
    print(n = 50)
#> # A tibble: 8 × 5
#>   code  shortname   region1 region2   region4        
#>   <chr> <chr>       <fct>   <fct>     <fct>          
#> 1 CN    China       Asia    East Asia <NA>           
#> 2 HK    Hong Kong   Asia    East Asia Other East Asia
#> 3 JP    Japan       Asia    East Asia <NA>           
#> 4 KP    North Korea Asia    East Asia Other East Asia
#> 5 KR    Korea       Asia    East Asia Other East Asia
#> 6 MN    Mongolia    Asia    East Asia Other East Asia
#> 7 MO    Macao       Asia    East Asia Other East Asia
#> 8 TW    Taiwan      Asia    East Asia Other East Asia

A.6 Glossary

term definition
EU Membership Applications The following countries have end of 2024 an open application for EU membership: Albania, Bosnia and Herzegovina, Georgia, Kosovo, Moldavia, Montenegro, North Macedonia, Serbia, Turkey, Ukraine.
EUx The European Union (EU) is a supranational political and economic union of 27 member states. It comprises end of 2024: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain Sweden.
GNI Gross National Income (GNI) is a measure of a country's income, which includes all the income earned by a country's residents, businesses, and earnings from foreign sources. It is defined as the total amount of money earned by a nation's people and businesses, no matter where it was earned. GNI is an alternative to GDP as a way to measure and track a nation’s wealth, as it calculates income instead of output.
IBRD The International Bank for Reconstruction and Development (IBRD) is a global development cooperative owned by 189 member countries. As the largest development bank in the world, it supports the World Bank Group’s mission by providing loans, guarantees, risk management products, and advisory services to middle-income and creditworthy low-income countries, as well as by coordinating responses to regional and global challenges. (https://www.worldbank.org/en/who-we-are/ibrd)
IDAx The International Development Association (IDA) is the part of the World Bank that helps the world’s low-income countries. IDA's grants and low-interest loans help countries invest in their futures, improve lives, and create safer, more prosperous communities around the world. (https://ida.worldbank.org/en/what-is-ida)
M49 The United Nations publication "Standard Country or Area Codes for Statistical Use" was originally published as Series M, No. 49 and is now commonly referred to as the M49 standard. M49 is a country/areas classification system prepared by the Statistics Division of the United Nations Secretariat primarily for use in its publications and databases.
MENA The Middle East and North Africa (MENA) region is a statistical grouping used by the United Nations (UN) to categorize 20 countries in the region. The UN defines MENA as comprising: Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syria, United Arab Emirates, and Yemen (Middle East). Algeria, Libya, Morocco, and Tunisia (North Africa).
OECD Organisation for Economic Co-operation and Development (OECD) is an intergovernmental economic organization founded in 1961. End of 2024 it comprises 38 member countries: Australia, Austria, Belgium, Canada, Chile, Colombia, Costa Rica, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Türkiye, United Kingdom, United States.
RDS The abbreviation “RDS” in file endings `.rds` refers to “R Data Serialized”. It is a format used by the R programming language to serialize and store R objects, such as data frames, lists, and functions, in a compact and portable binary format.
United Nations Geoscheme The United Nations geoscheme is a system which divides 248 countries and territories in the world into six continental regions, 22 geographical subregions, and two intermediary regions. It was devised by the United Nations Statistics Division (UNSD) based on the M49 coding classification
UNSD The United Nations Statistics Division (UNSD) is committed to the advancement of the global statistical system. It compiles and disseminates global statistical information, develop standards and norms for statistical activities, and support countries' efforts to strengthen their national statistical systems.
WHR The World Happiness Reports are a partnership of Gallup, the Oxford Wellbeing Research Centre, the UN Sustainable Development Solutions Network, and the WHR’s Editorial Board. The report is produced under the editorial control of the WHR Editorial Board. The Reports reflects a worldwide demand for more attention to happiness and well-being as criteria for government policy. It reviews the state of happiness in the world today and shows how the science of happiness explains personal and national variations in happiness. (https://worldhappiness.report/about/)
WID The 'World Inequality Database' (WID) aims to provide open and convenient access to the most extensive available database on the historical evolution of the world distribution of income and wealth, both within countries and between countries. [WID.WORLD](https://wid.world/wid-world/)

Session Info

Session Info

Code
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.4.2 (2024-10-31)
#>  os       macOS Sequoia 15.1.1
#>  system   x86_64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Europe/Vienna
#>  date     2024-11-26
#>  pandoc   3.5 @ /usr/local/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version    date (UTC) lib source
#>  base64enc     0.1-3      2015-07-28 [2] CRAN (R 4.4.0)
#>  bslib         0.8.0      2024-07-29 [2] CRAN (R 4.4.0)
#>  cachem        1.1.0      2024-05-16 [2] CRAN (R 4.4.0)
#>  cli           3.6.3      2024-06-21 [2] CRAN (R 4.4.1)
#>  colorspace    2.1-1      2024-07-26 [2] CRAN (R 4.4.0)
#>  commonmark    1.9.2      2024-10-04 [2] CRAN (R 4.4.1)
#>  crayon        1.5.3      2024-06-20 [2] CRAN (R 4.4.0)
#>  crosstalk     1.2.1      2023-11-23 [2] CRAN (R 4.4.0)
#>  curl          6.0.0      2024-11-05 [2] CRAN (R 4.4.1)
#>  digest        0.6.37     2024-08-19 [2] CRAN (R 4.4.1)
#>  dplyr         1.1.4      2023-11-17 [2] CRAN (R 4.4.0)
#>  DT            0.33       2024-04-04 [2] CRAN (R 4.4.0)
#>  evaluate      1.0.1      2024-10-10 [2] CRAN (R 4.4.1)
#>  fansi         1.0.6      2023-12-08 [2] CRAN (R 4.4.0)
#>  fastmap       1.2.0      2024-05-15 [2] CRAN (R 4.4.0)
#>  forcats       1.0.0      2023-01-29 [2] CRAN (R 4.4.0)
#>  generics      0.1.3      2022-07-05 [2] CRAN (R 4.4.0)
#>  ggplot2       3.5.1      2024-04-23 [2] CRAN (R 4.4.0)
#>  glossary    * 1.0.0.9003 2024-08-05 [2] Github (debruine/glossary@05e4a61)
#>  glue          1.8.0      2024-09-30 [2] CRAN (R 4.4.1)
#>  gtable        0.3.6      2024-10-25 [2] CRAN (R 4.4.1)
#>  here          1.0.1      2020-12-13 [2] CRAN (R 4.4.0)
#>  htmltools     0.5.8.1    2024-04-04 [2] CRAN (R 4.4.0)
#>  htmlwidgets   1.6.4      2023-12-06 [2] CRAN (R 4.4.0)
#>  jquerylib     0.1.4      2021-04-26 [2] CRAN (R 4.4.0)
#>  jsonlite      1.8.9      2024-09-20 [2] CRAN (R 4.4.1)
#>  kableExtra    1.4.0      2024-01-24 [2] CRAN (R 4.4.0)
#>  knitr         1.49       2024-11-08 [2] CRAN (R 4.4.1)
#>  lifecycle     1.0.4      2023-11-07 [2] CRAN (R 4.4.0)
#>  magrittr      2.0.3      2022-03-30 [2] CRAN (R 4.4.0)
#>  markdown      1.13       2024-06-04 [2] CRAN (R 4.4.0)
#>  munsell       0.5.1      2024-04-01 [2] CRAN (R 4.4.0)
#>  pillar        1.9.0      2023-03-22 [2] CRAN (R 4.4.0)
#>  pkgconfig     2.0.3      2019-09-22 [2] CRAN (R 4.4.0)
#>  purrr         1.0.2      2023-08-10 [2] CRAN (R 4.4.0)
#>  R6            2.5.1      2021-08-19 [2] CRAN (R 4.4.0)
#>  repr          1.1.7      2024-03-22 [2] CRAN (R 4.4.0)
#>  rlang         1.1.4      2024-06-04 [2] CRAN (R 4.4.0)
#>  rmarkdown     2.29       2024-11-04 [2] CRAN (R 4.4.1)
#>  rprojroot     2.0.4      2023-11-05 [2] CRAN (R 4.4.0)
#>  rstudioapi    0.17.1     2024-10-22 [2] CRAN (R 4.4.1)
#>  rversions     2.1.2      2022-08-31 [2] CRAN (R 4.4.0)
#>  sass          0.4.9      2024-03-15 [2] CRAN (R 4.4.0)
#>  scales        1.3.0      2023-11-28 [2] CRAN (R 4.4.0)
#>  sessioninfo   1.2.2      2021-12-06 [2] CRAN (R 4.4.0)
#>  skimr         2.1.5      2022-12-23 [2] CRAN (R 4.4.0)
#>  stringi       1.8.4      2024-05-06 [2] CRAN (R 4.4.0)
#>  stringr       1.5.1      2023-11-14 [2] CRAN (R 4.4.0)
#>  svglite       2.1.3      2023-12-08 [2] CRAN (R 4.4.0)
#>  systemfonts   1.1.0      2024-05-15 [2] CRAN (R 4.4.0)
#>  tibble        3.2.1      2023-03-20 [2] CRAN (R 4.4.0)
#>  tidyr         1.3.1      2024-01-24 [2] CRAN (R 4.4.0)
#>  tidyselect    1.2.1      2024-03-11 [2] CRAN (R 4.4.0)
#>  utf8          1.2.4      2023-10-22 [2] CRAN (R 4.4.0)
#>  vctrs         0.6.5      2023-12-01 [2] CRAN (R 4.4.0)
#>  viridisLite   0.4.2      2023-05-02 [2] CRAN (R 4.4.0)
#>  withr         3.0.2      2024-10-28 [2] CRAN (R 4.4.1)
#>  xfun          0.49       2024-10-31 [2] CRAN (R 4.4.1)
#>  xml2          1.3.6      2023-12-04 [2] CRAN (R 4.4.0)
#>  yaml          2.3.10     2024-07-26 [2] CRAN (R 4.4.0)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.4-x86_64/library
#>  [2] /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

References

Berroth, Markus. 2019. “Bang Bang - How to Program with Dplyr.” https://www.statworx.com/en/content-hub/blog/bang-bang-how-to-program-with-dplyr/.
McCarthy, Kieren. 2020. “After 20-Year Battle, Channel Island Sark Finally Earns the Right to Exist on the Internet with Its Own Top-Level Domain.” https://www.theregister.com/2020/03/23/sark_cctld_iso/.