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Downloads data from the Wittgenstein Centre Human Capital Data Explorer. Requires a working internet connection.

Usage

get_wcde(
  indicator = "pop",
  scenario = 2,
  country_code = NULL,
  country_name = NULL,
  pop_age = c("total", "all"),
  pop_sex = c("total", "both", "all"),
  pop_edu = c("total", "four", "six", "eight"),
  include_scenario_names = FALSE,
  server = c("iiasa", "github", "1&1", "search-available", "iiasa-local"),
  version = c("wcde-v3", "wcde-v2", "wcde-v1")
)

Arguments

indicator

One character string based on the indicator column in the wic_indicators data frame, representing the variable to be downloaded.

scenario

Vector of length one or more with numbers corresponding the scenarios. See details for more information. Defaults to 2 for the SSP2 Medium scenario.

country_code

Vector of length one or more of country numeric codes based on ISO 3 digit numeric values.

country_name

Vector of length one or more of country names. The corresponding country code will be guessed using the countrycodes package.

pop_age

Character string for population age groups if indicator is set to pop. Defaults to no age groups total, but can be set to all.

pop_sex

Character string for population sexes if indicatoris set to pop. Defaults to no sex total, but can be set to both or all.

pop_edu

Character string for population educational attainment if indicator is set to pop. Defaults to total, but can be set to four, six or eight.

include_scenario_names

Logical vector of length one to indicate if to include additional columns for scenario names and short names. FALSE by default.

server

Character string for server to download from. Defaults to iiasa, but can use github or 1&1 if IIASA server is down. Can check availability by setting to search-available.

version

Character string for version of projections to obtain. Defaults to wcde-v3, but can use wcde-v2 or wcde-v1. Scenario and indicator availability vary between versions.

Value

A tibble with the data selected.

Details

If no country_name or country_code is provided data for all countries and regions are downloaded. A full list of available countries and regions can be found in the wic_locations data frame.

indicator must be set to a value in the first column in the table below of available demographic indicators:

indicatorIndicator Description
popPopulation Size (000's)
bpopPopulation Size by Broad Age (000's)
epopPopulation Size by Education (000's)
propEducational Attainment Distribution
bpropEducational Attainment Distribution by Broad Age
growthAverage Annual Growth Rate
nirateAverage Annual Rate of Natural Increase
sexratioSex Ratio
magePopulation Median Age
tdrTotal Dependency Ratio
ydrYouth Dependency Ratio
odrOld-age Dependency Ratio
ryl15Age When Remaining Life Expectancy is Below 15 years
pryl15Proportion of Population with a Remaining Life Expectancy below 15 Years
mysMean Years of Schooling by Age
bmysMean Years of Schooling by Broad Age
ggapmys15Gender Gap in Mean Years Schooling (15+)
ggapmys25Gender Gap in Mean Years Schooling (25+)
ggapedu15Gender Gap in Educational Attainment (15+)
ggapedu25Gender Gap in Educational Attainment (25+)
tfrTotal Fertility Rate
etfrTotal Fertility Rate by Education
asfrAge-Specific Fertility Rate
easfrAge-Specific Fertility Rate by Education
cbrCrude Birth Rate
macbMean Age at Childbearing
emacbMean Age at Childbearing by Education
e0Life Expectancy at Birth
cdrCrude Death Rate
assrAge-Specific Survival Ratio
eassrAge-Specific Survival Ratio by Education
netNet Migration
neteduNet Migration Flows by Education
emiEmigration Flows
immImmigration Flows

See wic_indicators data frame for more details.

scenario must be set to one or values in the first column table below of the available future scenarios:

scenariodescriptionversion
1Rapid Development (SSP1)V1, V2, V3
2Medium (SSP2)V1, V2, V3
3Stalled Development (SSP3)V1, V2, V3
4Inequality (SSP4)V1, V3
5Conventional Development (SSP5)V1, V3
20Medium - Constant Enrollment Rate (SSP2-CER)V1
21Medium - Fast Track Education (SSP2-FT)V1
22Medium - Zero Migration (SSP2-ZM)V2, V3
23Medium - Double Migration (SSP2-DM)V2, V3

See wic_scenarios data frame for more details.

Examples

# \donttest{
# SSP2 tfr for Austria and Bulgaria
get_wcde(indicator = "tfr", country_code = c(40, 100))
#> # A tibble: 32 × 5
#>    scenario name     country_code period      tfr
#>       <dbl> <chr>           <dbl> <chr>     <dbl>
#>  1        2 Austria            40 2020-2025  1.45
#>  2        2 Bulgaria          100 2020-2025  1.57
#>  3        2 Austria            40 2025-2030  1.48
#>  4        2 Bulgaria          100 2025-2030  1.56
#>  5        2 Austria            40 2030-2035  1.51
#>  6        2 Bulgaria          100 2030-2035  1.59
#>  7        2 Austria            40 2035-2040  1.54
#>  8        2 Bulgaria          100 2035-2040  1.60
#>  9        2 Austria            40 2040-2045  1.57
#> 10        2 Bulgaria          100 2040-2045  1.59
#> # … with 22 more rows

# SSP1 and SSP2 life expectancy for Vietnam and United Kingdom (guessing the country codes)
get_wcde(scenario = c(1, 2), indicator = "e0", country_name = c("Vietnam", "UK"))
#> # A tibble: 128 × 6
#>    scenario name                                      count…¹ sex   period    e0
#>       <dbl> <chr>                                       <dbl> <chr> <chr>  <dbl>
#>  1        1 Viet Nam                                      704 Male  2020-…  71.2
#>  2        1 United Kingdom of Great Britain and Nort…     826 Male  2020-…  80.4
#>  3        1 Viet Nam                                      704 Fema… 2020-…  80.3
#>  4        1 United Kingdom of Great Britain and Nort…     826 Fema… 2020-…  84.3
#>  5        1 Viet Nam                                      704 Male  2025-…  73.2
#>  6        1 United Kingdom of Great Britain and Nort…     826 Male  2025-…  82.5
#>  7        1 Viet Nam                                      704 Fema… 2025-…  82.0
#>  8        1 United Kingdom of Great Britain and Nort…     826 Fema… 2025-…  86.2
#>  9        1 Viet Nam                                      704 Male  2030-…  75.1
#> 10        1 United Kingdom of Great Britain and Nort…     826 Male  2030-…  84.0
#> # … with 118 more rows, and abbreviated variable name ¹​country_code

# SSP1 and SSP3 population by education for all countries
get_wcde(scenario = c(1, 3), indicator = "tfr")
#> # A tibble: 7,296 × 5
#>    scenario name                     country_code period      tfr
#>       <dbl> <chr>                           <dbl> <chr>     <dbl>
#>  1        1 Bulgaria                          100 2020-2025  1.53
#>  2        1 Myanmar                           104 2020-2025  1.94
#>  3        1 Burundi                           108 2020-2025  4.55
#>  4        1 Belarus                           112 2020-2025  1.32
#>  5        1 Cambodia                          116 2020-2025  2.13
#>  6        1 Algeria                            12 2020-2025  2.76
#>  7        1 Cameroon                          120 2020-2025  4.01
#>  8        1 Canada                            124 2020-2025  1.37
#>  9        1 Cape Verde                        132 2020-2025  1.80
#> 10        1 Central African Republic          140 2020-2025  5.15
#> # … with 7,286 more rows

# population totals (aggregated over age, sex and education)
get_wcde(indicator = "pop", country_name = "Austria")
#> # A tibble: 17 × 5
#>    scenario name    country_code  year   pop
#>       <dbl> <chr>          <dbl> <dbl> <dbl>
#>  1        2 Austria           40  2020 8901.
#>  2        2 Austria           40  2025 9045.
#>  3        2 Austria           40  2030 9106.
#>  4        2 Austria           40  2035 9152.
#>  5        2 Austria           40  2040 9190.
#>  6        2 Austria           40  2045 9230.
#>  7        2 Austria           40  2050 9266.
#>  8        2 Austria           40  2055 9281.
#>  9        2 Austria           40  2060 9277.
#> 10        2 Austria           40  2065 9257.
#> 11        2 Austria           40  2070 9234.
#> 12        2 Austria           40  2075 9204.
#> 13        2 Austria           40  2080 9152.
#> 14        2 Austria           40  2085 9073.
#> 15        2 Austria           40  2090 8969.
#> 16        2 Austria           40  2095 8850.
#> 17        2 Austria           40  2100 8721.

# population totals by education group
get_wcde(indicator = "pop", country_name = "Austria", pop_edu = "four")
#> # A tibble: 85 × 6
#>    scenario name    country_code  year education           pop
#>       <dbl> <fct>          <dbl> <dbl> <fct>             <dbl>
#>  1        2 Austria           40  2020 Under 15       1283.   
#>  2        2 Austria           40  2020 No Education      0.763
#>  3        2 Austria           40  2020 Primary         431.   
#>  4        2 Austria           40  2020 Secondary      5052.   
#>  5        2 Austria           40  2020 Post Secondary 2134.   
#>  6        2 Austria           40  2025 Under 15       1295.   
#>  7        2 Austria           40  2025 No Education      3.19 
#>  8        2 Austria           40  2025 Primary         343.   
#>  9        2 Austria           40  2025 Secondary      5056.   
#> 10        2 Austria           40  2025 Post Secondary 2347.   
#> # … with 75 more rows

# population totals by age-sex group
get_wcde(indicator = "pop", country_name = "Austria", pop_age = "all", pop_sex = "both")
#> # A tibble: 714 × 7
#>    scenario name    country_code age    sex    year   pop
#>       <dbl> <chr>          <dbl> <chr>  <chr> <dbl> <dbl>
#>  1        2 Austria           40 0--4   Male   2020  224.
#>  2        2 Austria           40 5--9   Male   2020  219.
#>  3        2 Austria           40 10--14 Male   2020  216.
#>  4        2 Austria           40 15--19 Male   2020  226.
#>  5        2 Austria           40 20--24 Male   2020  266.
#>  6        2 Austria           40 25--29 Male   2020  308.
#>  7        2 Austria           40 30--34 Male   2020  309.
#>  8        2 Austria           40 35--39 Male   2020  307.
#>  9        2 Austria           40 40--44 Male   2020  283.
#> 10        2 Austria           40 45--49 Male   2020  309.
#> # … with 704 more rows
# }