Downloads & API Links

# Install readr if needed: install.packages("readr")
library(readr)

BASE <- "https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/"

# Master country table (one row per country/area)
countries <- read_csv(paste0(BASE, "countries_master.csv"))

# Long-format group memberships
memberships <- read_csv(paste0(BASE, "country_group_membership.csv"))

# Full denormalized library (country + group in one table)
library_df <- read_csv(paste0(BASE, "country_classification_library.csv"))

# Example: filter to LDC countries
ldc <- countries[countries$is_ldc == TRUE, ]

# Example: find all World Bank income-level memberships
income_groups <- memberships[memberships$source == "world_bank" &
                              memberships$group_type == "income_level", ]

# pip install pandas requests
import pandas as pd

BASE = "https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/"

# Master country table
countries = pd.read_csv(BASE + "countries_master.csv")

# Long-format group memberships
memberships = pd.read_csv(BASE + "country_group_membership.csv")

# Full denormalized library
library_df = pd.read_csv(BASE + "country_classification_library.csv")

# Example: LDC countries
ldc = countries[countries["is_ldc"] == True]

# Example: World Bank FCS list
fcs = countries[countries["wb_fcs_status"] == True][["iso3","country_name_en","wb_fcs_category"]]

# Example: merge memberships back to country names
df = memberships.merge(countries[["iso3","country_name_en"]], on="iso3")

--- Excel (Get Data from Web) ---

1. Open Excel → Data tab → Get Data → From Web
2. Paste this URL:
   https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/countries_master.csv

3. Click OK → in Power Query Editor, select the table and click "Close & Load"

4. To refresh later: Data tab → Refresh All

--- Power Query formula (paste in Advanced Editor) ---
let
    Source = Csv.Document(
        Web.Contents("https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/countries_master.csv"),
        [Delimiter=",", Encoding=65001, QuoteStyle=QuoteStyle.Csv]
    ),
    PromotedHeaders = Table.PromoteHeaders(Source, [PromoteAllScalars=true])
in
    PromotedHeaders

--- Power BI Desktop ---

1. Home → Get Data → Web
2. Paste URL:
   https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/countries_master.csv

3. Power Query will detect it as a CSV table automatically.
4. Promote first row as headers if not already done.

--- M formula (Power Query Advanced Editor) ---
let
    Source = Csv.Document(
        Web.Contents(
            "https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/countries_master.csv"
        ),
        [Delimiter=",", Encoding=65001, QuoteStyle=QuoteStyle.Csv]
    ),
    #"Promoted Headers" = Table.PromoteHeaders(Source, [PromoteAllScalars=true]),
    #"Changed Types"    = Table.TransformColumnTypes(#"Promoted Headers",
        {{"is_ldc","Logical"},{"is_lldc","Logical"},{"is_sids","Logical"},
         {"wb_fcs_status","Logical"},{"oecd_dac_eligible","Logical"}})
in
    #"Changed Types"

-- Tip: load country_group_membership.csv as a second query,
-- then create a relationship on iso3 to build a cross-filter.

--- Tableau Desktop ---

Option A — Web Data Connector (Tableau 2020.4+):
  Connect → Web Data Connector
  Paste: https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/countries_master.csv

Option B — Text File (download first):
  1. Download countries_master.csv from this page
  2. Connect → Text File → select the downloaded file

Option C — Google Sheets bridge:
  1. In Google Sheets: File → Import → upload the CSV
  2. Publish the sheet (File → Share → Publish to web → CSV)
  3. Connect Tableau to that published CSV URL

-- After connecting, you can JOIN country_group_membership.csv
-- on iso3 to add classification groups to your map/chart.

* SPSS syntax — download the CSV file first, then import.

GET DATA
  /TYPE=TXT
  /FILE='C:\path\to\countries_master.csv'
  /ENCODING='UTF8'
  /DELIMITERS=","
  /QUALIFIER='"'
  /ARRANGEMENT=DELIMITED
  /FIRSTCASE=2
  /VARIABLES=
    iso3 A3
    iso2 A2
    m49 A3
    country_name_en A80
    region_name_en A60
    wb_income_name A40
    is_ldc A5
    is_lldc A5
    is_sids A5
    wb_fcs_status A5
    oecd_dac_eligible A5.
CACHE.
EXECUTE.

* Note: download the latest CSV from the Downloads page
* before running this syntax, or use a macro to fetch via URL.

* Stata 16+ can import directly from a URL

local base "https://raw.githubusercontent.com/MafiAtUN/country-classification-commons/main/data/latest/"

* Master country table
import delimited "`base'countries_master.csv", ///
    varnames(1) encoding("UTF-8") clear

* Group memberships (long format)
import delimited "`base'country_group_membership.csv", ///
    varnames(1) encoding("UTF-8") clear

* Example: keep only LDC countries
keep if is_ldc == "True"

* Example: merge memberships with master on iso3
import delimited "`base'countries_master.csv",        varnames(1) clear
save countries_master.dta, replace

import delimited "`base'country_group_membership.csv", varnames(1) clear
merge m:1 iso3 using countries_master.dta