Description Usage Format Source Examples
United Nations table of locations, including regions, for statistical purposes as available in 2019.
1 2 |
A data frame with one observations per country or region. It contains the following variables:
name
Name of country or region (following ISO 3166 official short names in English - see
https://www.iso.org/obp/ui/#search/code/ and United Nations Multilingual Terminology Database - see https://unterm.un.org/unterm).
country_code
Numerical Location Code (3-digit codes following ISO 3166-1 numeric standard) - see http://en.wikipedia.org/wiki/ISO_3166-1_numeric.
reg_code
Code of the regions.
reg_name
Name of the regions.
area_code
Area code.
area_name
Area names, such as Africa
, Asia
, Europe
Latin America and the Caribbean
, Northern America
, Oceania
, World
.
location_type
Code giving the type of the observation: 0=World, 2=Major Area, 3=Region, 4=Country/Area, 5=Development group, 12=Special groupings. Other numbers are allowed and they can be used for aggregation, see below.
agcode_1500000
, agcode_1501000
, agcode_1502000
, agcode_1503000
, agcode_1517000
, agcode_1518000
, agcode_1524000
, agcode_1636000
, agcode_1637000
, agcode_1829000
, agcode_1830000
, agcode_1832000
, agcode_1833000
, agcode_1835000
, agcode_901000
, agcode_902000
, agcode_917000
, agcode_918000
, agcode_921000
, agcode_927000
, agcode_934000
, agcode_941000
, agcode_947000
, agcode_948000
, tree_level
Optional columns that can be used for aggregations. To aggregate a region with country_code
=x, get the value of its location_type
, say y. Then look for the column agcode_y
and locate all records with agcode_y
=x that have location_type
=4, see Example below.
Data provided by the United Nations Population Division.
The designations employed in this dataset do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
1 2 3 4 5 6 | data(UNlocations)
# Find high income countries in Africa (based on World Bank groups)
grouprec <- subset(UNlocations, name == "High-income countries")
# grouprec$location_type is 1503000, thus look for column agcode_1503000
subset(UNlocations, agcode_1503000 == grouprec$country_code &
location_type == 4 & area_name == "Africa")
|
name country_code reg_code reg_name area_code area_name
37 Seychelles 690 910 Eastern Africa 903 Africa
location_type tree_level agcode_1500000 agcode_1501000 agcode_1502000
37 4 5 -1 -1 -1
agcode_1503000 agcode_1517000 agcode_1518000 agcode_1636000 agcode_1637000
37 1503 -1 -1 -1 1637
agcode_1829000 agcode_1830000 agcode_1832000 agcode_1833000 agcode_1835000
37 -1 -1 -1 -1 -1
agcode_901000 agcode_902000 agcode_917000 agcode_918000 agcode_921000
37 -1 902 -1 -1 -1
agcode_927000 agcode_934000 agcode_941000 agcode_947000 agcode_948000
37 -1 934 -1 947 948
agcode_1524000
37 -1
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