| Title: | World Population Prospects 2019 |
|---|---|
| Description: | Provides data from the United Nation's World Population Prospects 2019. |
| Authors: | United Nations Population Division |
| Maintainer: | Hana Sevcikova <[email protected]> |
| License: | file LICENSE |
| Version: | 1.1-1 |
| Built: | 2024-12-23 06:21:47 UTC |
| Source: | CRAN |
Data from the United Nations World Population Prospects 2019, released on June 17, 2019.
| Package: | wpp2019 |
| Version: | 1.1-1 |
| Date: | 2020-1-30 |
| License: | CC-BY-3.0-IGO |
| URL: | http://population.un.org/wpp |
The package contains the following datasets:
tfr, tfr_supplemental, tfrprojMed, tfrproj80u, tfrproj80l, tfrproj95u, tfrproj95l, tfrprojHigh, tfrprojLow: estimates and projections of total fertility rate, including the projected 80% and 95% probability bounds, as well as low and high half child variants.
e0F, e0M, e0X_supplemental, e0Xproj, e0Xproj80u, e0Xproj80l, e0Xproj95u, e0Xproj95l: sex-specific estimates and projections of life expectancy with X=“F” and “M”, including the projected 80% and 95% probability bounds.
pop, popproj, popproj80u, popproj80l, popproj95u, popproj95l, popprojHigh, popprojLow: historical estimates of total population counts, as well as the median, probability bounds and the high and low variants of population projections.
popFT, popMT, popFTproj, popMTproj: historical estimates and projection medians for sex-specific total population.
popF, popM, popXprojMed, popXprojHigh, popXprojLow: age- and sex-specific population estimates and projections with X=“F” and “M”, including the high and low variants.
migration: total net migration
sexRatio: sex ratio at birth as a ratio of female to male
percentASFR: distribution of age-specific fertility rates
UNlocations: location dataset
The package wppExplorer offers a shiny user interface to explore these datasets, as well as functions for convenient extraction of information from the data, see function wpp.indicator() in wppExplorer, or https://rstudio.stat.washington.edu/shiny/wppExplorer/inst/explore/.
These datasets are based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
World Population Prospects: The 2019 Revision. http://population.un.org/wpp.
Datasets containing the United Nations time series of the life expectancy (e0) for all countries of the world as available in 2019.
data(e0F) data(e0M) data(e0F_supplemental) data(e0M_supplemental) data(e0Fproj) data(e0Mproj) data(e0Fproj80l) data(e0Fproj80u) data(e0Mproj80l) data(e0Mproj80u) data(e0Fproj95l) data(e0Fproj95u) data(e0Mproj95l) data(e0Mproj95u)data(e0F) data(e0M) data(e0F_supplemental) data(e0M_supplemental) data(e0Fproj) data(e0Mproj) data(e0Fproj80l) data(e0Fproj80u) data(e0Mproj80l) data(e0Mproj80u) data(e0Fproj95l) data(e0Fproj95u) data(e0Mproj95l) data(e0Mproj95u)
The datasets contain one record per country or region. They contain the following variables:
country_codeNumerical Location Code (3-digit codes following ISO 3166-1 numeric standard) - see http://en.wikipedia.org/wiki/ISO_3166-1_numeric.
nameName 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).
1950-1955, 1955-1960, ...Life expectancy in various five-year time intervals (i.e., from 1 July in year t to 1 July in year t+5 such as the period 1950-1955 refers to the period 1950.5-1955.5 and the mid of the period is 1953.0). The e0*proj datasets start at 2020-2025. The e0*_supplemental datasets start at 1750-1755. Missing data have NA values.
Datasets e0F and e0F_supplemental contain estimates for female historical e0; e0M and e0M_supplemental contain estimates for male historical e0. The *_supplemental datasets contain a subset of countries for which data prior 1950 are available. Datasets e0Mproj and e0Fproj contain projections of male and female e0, respectively. Datasets *80l, *95l are the lower bounds of 80 and 95% probability intervals, *80u, *95u are the corresponding upper bounds.
The historical datasets (e0F_supplemental and e0M_supplemental for female and male, respectively) for 29 countries or areas cover the period 1750-1950 (including 20 countries with data since at least 1900) and are based on series for 5-year periods from the following sources: (1) University of California at Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). (2012). Human Mortality Database Available at https://www.mortality.org. Data downloaded on 9 Jan. 2012; (2) University of California at Berkeley (USA), Max Planck Institute for Demographic Research (Germany), and Institut National d'Etudes Demographiques (France). Human Life-Table Database (2011). Available at https://www.lifetable.de. Data downloaded on 29 Dec. 2011; (3) Statistics Finland (2006). Statistical Yearbook of Finland 2006; (4) Hungarian Central Statistical Office (2006). Hungary Demographic Yearbook 2005; (5) Japan Ministry of Internal Affairs and Communication (2012). Historical Statistics of Japan. Available at: www.stat.go.jp/english/data/chouki; (6) Andreev E.M. et al. (1998). Demographic History of Russia 1927-1959. Informatika, Moscow.
These datasets are based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
The pre-1950 datasets were collected by Patrick Gerland.
World Population Prospects: The 2019 Revision. http://population.un.org/wpp
data(e0M) head(e0M) data(e0Fproj) str(e0Fproj)data(e0M) head(e0M) data(e0Fproj) str(e0Fproj)
Estimates and projections of total net migration.
data(migration)data(migration)
Data frame with one row per country. It contains the following variables:
country_codeNumerical Location Code (3-digit codes following ISO 3166-1 numeric standard) - see http://en.wikipedia.org/wiki/ISO_3166-1_numeric.
nameCountry name.
1950-1955, 1955-1960, ...Net migration (in thousand) for the specific five-year time period (i.e., from 1 July in year t to 1 July in year t+5 such as the period 1950-1955 refers to the period 1950.5-1955.5 and the mid of the period is 1953.0).
These datasets are based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
World Population Prospects: The 2019 Revision. http://population.un.org/wpp.
data(migration) str(migration)data(migration) str(migration)
Age-specific data on mortality rates for male (mxM) and female (mxF).
data(mxM) data(mxF)data(mxM) data(mxF)
Data frames with one row per country and age group. They contain the following variables:
country_codeNumerical Location Code (3-digit codes following ISO 3166-1 numeric standard) - see http://en.wikipedia.org/wiki/ISO_3166-1_numeric.
nameCountry name.
ageA character string representing an age interval (given by the starting age of the interval).
1950-1955, 1955-1960, ...mx for the given five-year time period (i.e., from 1 July in year t to 1 July in year t+5 such as the period 1950-1955 refers to the period 1950.5-1955.5 and the mid of the period is 1953.0).
These datasets are based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
World Population Prospects: The 2019 Revision. http://population.un.org/wpp
data(mxF) head(mxF)data(mxF) head(mxF)
Datasets giving the percentage of fertility rates over ages 15-50.
data(percentASFR)data(percentASFR)
A data frame with one row per country and age group. For each country there are seven age groups. It contains columns country_code, name, age and one column per five-year time interval (i.e., from 1 July in year t to 1 July in year t+5 such as the period 1950-1955 refers to the period 1950.5-1955.5 and the mid of the period is 1953.0).
This dataset is based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
World Population Prospects: The 2019 Revision. http://population.un.org/wpp.
data(percentASFR) str(percentASFR)data(percentASFR) str(percentASFR)
Datasets with historical population estimates and projections.
data(pop) data(popMT) data(popFT) data(popM) data(popF) data(popproj) data(popproj80l) data(popproj80u) data(popproj95l) data(popproj95u) data(popprojHigh) data(popprojLow) data(popMTproj) data(popFTproj) data(popMprojMed) data(popFprojMed) data(popMprojHigh) data(popFprojHigh) data(popMprojLow) data(popFprojLow)data(pop) data(popMT) data(popFT) data(popM) data(popF) data(popproj) data(popproj80l) data(popproj80u) data(popproj95l) data(popproj95u) data(popprojHigh) data(popprojLow) data(popMTproj) data(popFTproj) data(popMprojMed) data(popFprojMed) data(popMprojHigh) data(popFprojHigh) data(popMprojLow) data(popFprojLow)
Datasets that start with popM or popF and do not have “T” in their names, are age-specific and are organized as
data frames with one row per country and age group. For each country there are 21 age groups. It contains the following variables:
country_codeNumerical Location Code (3-digit codes following ISO 3166-1 numeric standard) - see http://en.wikipedia.org/wiki/ISO_3166-1_numeric.
nameCountry name.
ageA character string representing an age interval. For each country there are 21 values: “0-4”, “5-9”, “10-14”, “15-19”, “20-24”, “25-29”, “30-34”, “35-39”, “40-44”, “45-49”, “50-54”, “55-59”, “60-64”, “65-69”, “70-74”, “75-79”, “80-84”, “85-89”, “90-94”, “95-99”, and “100+” in that order.
1950, 1955, ...Population estimate or projection (in thousand) for the given time.
The remaining datasets, i.e. those that do not have “M” or “F”, or have “T” in their names, contain one row per country.
Dataset pop provides estimates of historical total population counts.
Datasets popMT and popFT provide estimates of total counts of male and female population, respectively.
Datasets popM (popF) contain age-specific estimates of the historical population counts for male (female).
Dataset popproj provides median projection of total population counts, i.e. aggregated over sex and age. Datasets popproj80l, popproj80u, popproj95l, and popproj95u are the lower (l) and upper (u) bounds of the 80 and 95% probability intervals of the total population. Datasets popprojHigh and popprojLow contain the upper and lower variant of total population defined as +- 1/2 child.
Datasets popMTproj and popFTproj provide median projection of total counts of male and female population, respectively.
Datasets popXprojMed, popXprojHigh and popXprojLow contain median, high and low variants of age-specific projections, respectively, with X=M for male and X=F for female.
All values are in thousands.
These datasets are based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
World Population Prospects: The 2019 Revision. http://population.un.org/wpp.
data(popM) str(popM)data(popM) str(popM)
Estimates and projections of the sex ratio at birth derived as the number of male divided by the number of female.
data(sexRatio)data(sexRatio)
A data frame with one record per country. It contains columns country_code, name, and one column per five-year time interval (i.e., from 1 July in year t to 1 July in year t+5 such as the period 1950-1955 refers to the period 1950.5-1955.5 and the mid of the period is 1953.0).
This dataset is based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
World Population Prospects: The 2019 Revision. http://population.un.org/wpp.
data(sexRatio) str(sexRatio)data(sexRatio) str(sexRatio)
Datasets containing the United Nations time series of the total fertility rate (TFR) for all countries of the world as available in 2019.
data(tfr) data(tfr_supplemental) data(tfrprojMed) data(tfrproj80l) data(tfrproj80u) data(tfrproj95l) data(tfrproj95u) data(tfrprojHigh) data(tfrprojLow)data(tfr) data(tfr_supplemental) data(tfrprojMed) data(tfrproj80l) data(tfrproj80u) data(tfrproj95l) data(tfrproj95u) data(tfrprojHigh) data(tfrprojLow)
The datasets contain one record per country or region. It contains the following variables:
country_codeNumerical Location Code (3-digit codes following ISO 3166-1 numeric standard) - see http://en.wikipedia.org/wiki/ISO_3166-1_numeric.
nameName 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).
1950-1955, 1955-1960, ...TFR in various five-year time intervals (i.e., from 1 July in year t to 1 July in year t+5 such as the period 1950-1955 refers to the period 1950.5-1955.5 and the mid of the period is 1953.0). The tfrproj* datasets start at 2020-2025. The tfr_supplemental datasets start at 1740-1745. Missing data have NA values.
Dataset tfr contains estimates of the historical TFR starting at 1950; tfr_supplemental contains a subset of countries for which data prior 1950 are available. Datasets tfrprojMed contain the median projections. Datasets tfrproj80l, tfrproj80u, tfrproj95l, and tfrproj95u are the lower (l) and upper (u) bounds of the 80 and 95% probability intervals, respectively.
Datasets tfrprojHigh and tfrprojLow contain high and low variants, respectively, defined as +-1/2 child.
The historical dataset tfr_supplemental (for 103 countries or areas) covers the period 1740-1950 (including 24 countries with data before 1850), and is based on series for five-year periods from the following sources: (1) Max Planck Institute for Demographic Research (Germany) and Vienna Institute of Demography (Austria). (2012). Human Fertility Database (HFD). Available at https://www.humanfertility.org. Data downloaded on 13 May 2012; (2) Festy, P. (1979). La fecondite des pays occidentaux de 1870 a 1970. Paris: Presses universitaires de France; (3) Chesnais, J.C. (1992). The demographic transition: stages, patterns, and economic implications: a longitudinal study of sixty-seven countries covering the period 1720-1984. Oxford ; New York: Clarendon Press; (4) Bhat, P.N.M. (1989). "Mortality and fertility in India, 1881-1961: a reassessment." pp. 73-118 in India's historical demography: studies in famine, disease and society, edited by T. Dyson. London and Riverdale, Md: Curzon and Riverdale Co.; (5) Hofsten, E.A.G.v. and H. Lundstrom. (1976). Swedish population history: Main trends from 1750 to 1970. Stockholm: Statistiska centralbyran: LiberForlag; (6) Ajus, F. and M. Lindgren. (2012). Gapminder fertility dataset, 2010 (including documentation for Children per Woman (Total Fertility Rate) for countries and territories, Version 2. The Gapminder Foundation. Sweden, Stockholm. http://www.gapminder.org/data/documentation/gd008/. Data downloaded on 8 April 2012.
These datasets are based on estimates and projections of United Nations, Department of Economic and Social Affairs, Population Division (2019).
The pre-1950 dataset was collected by Patrick Gerland.
World Population Prospects: The 2019 Revision. http://population.un.org/wpp.
data(tfr) head(tfr) data(tfrprojMed) str(tfrprojMed)data(tfr) head(tfr) data(tfrprojMed) str(tfrprojMed)
United Nations table of locations, including regions, for statistical purposes as available in 2019.
data(UNlocations)data(UNlocations)
A data frame with one observations per country or region. It contains the following variables:
nameName 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_codeNumerical Location Code (3-digit codes following ISO 3166-1 numeric standard) - see http://en.wikipedia.org/wiki/ISO_3166-1_numeric.
reg_codeCode of the regions.
reg_nameName of the regions.
area_codeArea code.
area_nameArea names, such as Africa, Asia, Europe Latin America and the Caribbean, Northern America, Oceania, World.
location_typeCode 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=, get the value of its location_type, say . Then look for the column agcode_y and locate all records with agcode_y= 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.
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")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")