# Overview of the wcde package

#### Guy J. Abel, Samir K.C., Michaela Potancokova, Claudia Reiter, Andrea Tamburini and Dilek Yildiz

The `wcde` package allows for R users to easily download data from the Wittgenstein Centre for Demography and Human Capital Data Explorer as well as containing a number of helpful functions for working with education specific demographic data.

# Installation

You can install the released version of `wcde` from CRAN with:

    install.packages("wcde")

Install the developmental version with:

    library(devtools)
    install_github("guyabel/wcde", ref = "main")

# Getting data into R

The `get_wcde()` function can be used to download data from the Wittgenstein Centre Human Capital Data Explorer. It requires three user inputs

- `indicator`: a short code for the indicator of interest
- `scenario`: a number referring to a SSP narrative, by default 2 is used (for SSP2)
- `country_code` (or `country_name`): corresponding to the country of interest

    library(wcde)
    # download education specific tfr data
    get_wcde(indicator = "etfr",
             country_name = c("Brazil", "Albania"))
    #> # A tibble: 192 × 6
    #>    scenario name    country_code education          period     etfr
    #>       <dbl> <chr>          <dbl> <chr>              <chr>     <dbl>
    #>  1        2 Brazil            76 No Education       2020-2025  2.24
    #>  2        2 Albania            8 No Education       2020-2025  2.31
    #>  3        2 Brazil            76 Incomplete Primary 2020-2025  2.24
    #>  4        2 Albania            8 Incomplete Primary 2020-2025  2.51
    #>  5        2 Brazil            76 Primary            2020-2025  2.24
    #>  6        2 Albania            8 Primary            2020-2025  2.17
    #>  7        2 Brazil            76 Lower Secondary    2020-2025  1.78
    #>  8        2 Albania            8 Lower Secondary    2020-2025  1.88
    #>  9        2 Brazil            76 Upper Secondary    2020-2025  1.35
    #> 10        2 Albania            8 Upper Secondary    2020-2025  1.61
    #> # ℹ 182 more rows

    # download education specific survivorship rates
    get_wcde(indicator = "eassr",
             country_name = c("Niger", "Korea"))
    #> # A tibble: 8,448 × 8
    #>    scenario name              country_code age    sex   education   period eassr
    #>       <dbl> <chr>                    <dbl> <chr>  <chr> <chr>       <chr>  <dbl>
    #>  1        2 Niger                      562 15--19 Male  No Educati… 2020-… 0.986
    #>  2        2 Republic of Korea          410 15--19 Male  No Educati… 2020-… 0.999
    #>  3        2 Niger                      562 15--19 Male  Incomplete… 2020-… 0.988
    #>  4        2 Republic of Korea          410 15--19 Male  Incomplete… 2020-… 0.999
    #>  5        2 Niger                      562 15--19 Male  Primary     2020-… 0.989
    #>  6        2 Republic of Korea          410 15--19 Male  Primary     2020-… 0.999
    #>  7        2 Niger                      562 15--19 Male  Lower Seco… 2020-… 0.992
    #>  8        2 Republic of Korea          410 15--19 Male  Lower Seco… 2020-… 0.999
    #>  9        2 Niger                      562 15--19 Male  Upper Seco… 2020-… 0.992
    #> 10        2 Republic of Korea          410 15--19 Male  Upper Seco… 2020-… 0.999
    #> # ℹ 8,438 more rows

## Indicator codes

The indicator input must match the short code from the indicator table. The `find_indicator()` function can be used to look up short codes (given in the first column) from the `wic_indicators` data frame:

    find_indicator(x = "tfr")
    #> # A tibble: 2 × 6
    #>   indicator description          `wcde-v3` `wcde-v2` `wcde-v1` definition_latest
    #>   <chr>     <chr>                <chr>     <chr>     <chr>     <chr>
    #> 1 etfr      Total Fertility Rat… projecti… projecti… projecti… The average numb…
    #> 2 tfr       Total Fertility Rate past-ava… past-ava… past-ava… The average numb…

## Temporal coverage

By default, `get_wdce()` returns data for all years or available periods or years. The `filter()` function in dplyr can be used to filter data for specific years or periods, for example:

    library(tidyverse)
    get_wcde(indicator = "e0",
             country_name = c("Japan", "Australia")) %>%
      filter(period == "2015-2020")
    #> # A tibble: 4 × 6
    #>   scenario name      country_code sex    period       e0
    #>      <dbl> <chr>            <dbl> <chr>  <chr>     <dbl>
    #> 1        2 Japan              392 Male   2015-2020  81.1
    #> 2        2 Australia           36 Male   2015-2020  81.1
    #> 3        2 Japan              392 Female 2015-2020  87.3
    #> 4        2 Australia           36 Female 2015-2020  85.1

    get_wcde(indicator = "sexratio",
             country_name = c("China", "South Korea")) %>%
      filter(year == 2020)
    #> # A tibble: 44 × 6
    #>    scenario name              country_code age     year sexratio
    #>       <dbl> <chr>                    <dbl> <chr>  <dbl>    <dbl>
    #>  1        2 China                      156 All     2020     1.05
    #>  2        2 Republic of Korea          410 All     2020     1
    #>  3        2 China                      156 0--4    2020     1.14
    #>  4        2 Republic of Korea          410 0--4    2020     1.05
    #>  5        2 China                      156 5--9    2020     1.16
    #>  6        2 Republic of Korea          410 5--9    2020     1.05
    #>  7        2 China                      156 10--14  2020     1.17
    #>  8        2 Republic of Korea          410 10--14  2020     1.07
    #>  9        2 China                      156 15--19  2020     1.17
    #> 10        2 Republic of Korea          410 15--19  2020     1.08
    #> # ℹ 34 more rows

Past data is only available for selected indicators. These can be viewed using the version column:

    wic_indicators %>%
      filter(`wcde-v3` == "past-available") %>%
      select(1:2)
    #> # A tibble: 28 × 2
    #>    indicator description
    #>    <chr>     <chr>
    #>  1 asfr      Age-Specific Fertility Rate
    #>  2 assr      Age-Specific Survival Ratio
    #>  3 bmys      Mean Years of Schooling by Broad Age
    #>  4 bpop      Population Size by Broad Age (000's)
    #>  5 bprop     Educational Attainment Distribution by Broad Age
    #>  6 cbr       Crude Birth Rate
    #>  7 cdr       Crude Death Rate
    #>  8 e0        Life Expectancy at Birth
    #>  9 epop      Population Size by Education (000's)
    #> 10 ggapedu15 Gender Gap in Educational Attainment (15+)
    #> # ℹ 18 more rows

The `filter()` function can also be used to filter specific indicators to specific age, sex or education groups

    get_wcde(indicator = "sexratio",
             country_name = c("China", "South Korea")) %>%
      filter(year == 2020,
             age == "All")
    #> # A tibble: 2 × 6
    #>   scenario name              country_code age    year sexratio
    #>      <dbl> <chr>                    <dbl> <chr> <dbl>    <dbl>
    #> 1        2 China                      156 All    2020     1.05
    #> 2        2 Republic of Korea          410 All    2020     1

## Country names and codes

Country names are guessed using the countrycode package.

    get_wcde(indicator = "tfr",
             country_name = c("U.A.E", "Espania", "Österreich"))
    #> # A tibble: 90 × 5
    #>    scenario name                 country_code period      tfr
    #>       <dbl> <chr>                       <dbl> <chr>     <dbl>
    #>  1        2 United Arab Emirates          784 1950-1955  6.88
    #>  2        2 Spain                         724 1950-1955  2.49
    #>  3        2 Austria                        40 1950-1955  2.08
    #>  4        2 United Arab Emirates          784 1955-1960  6.81
    #>  5        2 Spain                         724 1955-1960  2.65
    #>  6        2 Austria                        40 1955-1960  2.52
    #>  7        2 United Arab Emirates          784 1960-1965  6.65
    #>  8        2 Spain                         724 1960-1965  2.82
    #>  9        2 Austria                        40 1960-1965  2.78
    #> 10        2 United Arab Emirates          784 1965-1970  6.50
    #> # ℹ 80 more rows

The `get_wcde()` functions accepts ISO alpha numeric codes for countries via the `country_code` argument:

    get_wcde(indicator = "etfr", country_code = c(44, 100))
    #> # A tibble: 192 × 6
    #>    scenario name     country_code education          period     etfr
    #>       <dbl> <chr>           <dbl> <chr>              <chr>     <dbl>
    #>  1        2 Bahamas            44 No Education       2020-2025  2.2
    #>  2        2 Bulgaria          100 No Education       2020-2025  1.86
    #>  3        2 Bahamas            44 Incomplete Primary 2020-2025  2.2
    #>  4        2 Bulgaria          100 Incomplete Primary 2020-2025  1.86
    #>  5        2 Bahamas            44 Primary            2020-2025  2.2
    #>  6        2 Bulgaria          100 Primary            2020-2025  1.86
    #>  7        2 Bahamas            44 Lower Secondary    2020-2025  1.74
    #>  8        2 Bulgaria          100 Lower Secondary    2020-2025  1.86
    #>  9        2 Bahamas            44 Upper Secondary    2020-2025  1.46
    #> 10        2 Bulgaria          100 Upper Secondary    2020-2025  1.51
    #> # ℹ 182 more rows

A full list of available countries and region aggregates, and their codes, can be found in the `wic_locations` data frame.

    wic_locations
    #> # A tibble: 232 × 8
    #>    name               isono continent region dim   `wcde-v3` `wcde-v2` `wcde-v1`
    #>    <chr>              <dbl> <chr>     <chr>  <chr> <lgl>     <lgl>     <lgl>
    #>  1 World                900 <NA>      <NA>   area  TRUE      TRUE      TRUE
    #>  2 Africa               903 <NA>      <NA>   area  TRUE      TRUE      TRUE
    #>  3 Asia                 935 <NA>      <NA>   area  TRUE      TRUE      TRUE
    #>  4 Europe               908 <NA>      <NA>   area  TRUE      TRUE      TRUE
    #>  5 Latin America and…   904 <NA>      <NA>   area  TRUE      TRUE      TRUE
    #>  6 Northern America     905 <NA>      <NA>   area  TRUE      TRUE      TRUE
    #>  7 Oceania              909 <NA>      <NA>   area  TRUE      TRUE      TRUE
    #>  8 Afghanistan            4 Asia      South… coun… TRUE      TRUE      TRUE
    #>  9 Albania                8 Europe    South… coun… TRUE      TRUE      TRUE
    #> 10 Algeria               12 Africa    North… coun… TRUE      TRUE      TRUE
    #> # ℹ 222 more rows

## Scenarios

By default `get_wcde()` returns data for Medium (SSP2) scenario. Results for different SSP scenarios can be returned by passing a different (or multiple) scenario values to the `scenario` argument in `get_data()`.

    get_wcde(indicator = "growth",
             country_name = c("India", "China"),
             scenario = c(1:3, 22, 23)) %>%
      filter(period == "2095-2100")
    #> # A tibble: 10 × 5
    #>    scenario name  country_code period    growth
    #>       <dbl> <chr>        <dbl> <chr>      <dbl>
    #>  1        1 India          356 2095-2100   -1
    #>  2        1 China          156 2095-2100   -1.1
    #>  3        2 India          356 2095-2100   -0.5
    #>  4        2 China          156 2095-2100   -1
    #>  5        3 India          356 2095-2100    0.2
    #>  6        3 China          156 2095-2100   -0.4
    #>  7       22 India          356 2095-2100   -0.5
    #>  8       22 China          156 2095-2100   -1
    #>  9       23 India          356 2095-2100   -0.5
    #> 10       23 China          156 2095-2100   -1

Set `include_scenario_names = TRUE` to include a columns with the full names of the scenarios

    get_wcde(indicator = "tfr",
             country_name = c("Kenya", "Nigeria", "Algeria"),
             scenario = 1:3,
             include_scenario_names = TRUE) %>%
      filter(period == "2045-2050")
    #> # A tibble: 9 × 7
    #>   scenario scenario_name            scenario_abb name  country_code period   tfr
    #>      <dbl> <chr>                    <chr>        <chr>        <dbl> <chr>  <dbl>
    #> 1        1 Rapid Development (SSP1) SSP1         Kenya          404 2045-…  1.64
    #> 2        1 Rapid Development (SSP1) SSP1         Nige…          566 2045-…  2.62
    #> 3        1 Rapid Development (SSP1) SSP1         Alge…           12 2045-…  1.53
    #> 4        2 Medium (SSP2)            SSP2         Kenya          404 2045-…  2.34
    #> 5        2 Medium (SSP2)            SSP2         Nige…          566 2045-…  3.76
    #> 6        2 Medium (SSP2)            SSP2         Alge…           12 2045-…  2.06
    #> 7        3 Stalled Development (SS… SSP3         Kenya          404 2045-…  3.04
    #> 8        3 Stalled Development (SS… SSP3         Nige…          566 2045-…  4.87
    #> 9        3 Stalled Development (SS… SSP3         Alge…           12 2045-…  2.68

Additional details of the pathways for each scenario numeric code can be found in the `wic_scenarios` object. Further background and links to the corresponding literature are provided in the Data Explorer

    wic_scenarios
    #> # A tibble: 9 × 6
    #>   scenario_name              scenario scenario_abb `wcde-v3` `wcde-v2` `wcde-v1`
    #>   <chr>                         <dbl> <chr>        <lgl>     <lgl>     <lgl>
    #> 1 Rapid Development (SSP1)          1 SSP1         TRUE      TRUE      TRUE
    #> 2 Medium (SSP2)                     2 SSP2         TRUE      TRUE      TRUE
    #> 3 Stalled Development (SSP3)        3 SSP3         TRUE      TRUE      TRUE
    #> 4 Inequality (SSP4)                 4 SSP4         TRUE      FALSE     TRUE
    #> 5 Conventional Development …        5 SSP5         TRUE      FALSE     TRUE
    #> 6 Medium - Zero Migration (…       22 SSP2-ZM      TRUE      TRUE      FALSE
    #> 7 Medium - Double Migration…       23 SSP2-DM      TRUE      TRUE      FALSE
    #> 8 Medium - Constant Enrolme…       20 SSP2-CER     FALSE     FALSE     TRUE
    #> 9 Medium - Fast Track Educa…       21 SSP2-FT      FALSE     FALSE     TRUE

## All countries data

Data for all countries can be obtained by not setting `country_name` or `country_code`

    get_wcde(indicator = "mage")
    #> # A tibble: 6,858 × 5
    #>    scenario name                     country_code  year  mage
    #>       <dbl> <chr>                           <dbl> <dbl> <dbl>
    #>  1        2 Bulgaria                          100  1950  26.9
    #>  2        2 Myanmar                           104  1950  21.8
    #>  3        2 Burundi                           108  1950  19.5
    #>  4        2 Belarus                           112  1950  26.6
    #>  5        2 Cambodia                          116  1950  18.6
    #>  6        2 Algeria                            12  1950  19.5
    #>  7        2 Cameroon                          120  1950  20.4
    #>  8        2 Canada                            124  1950  27.7
    #>  9        2 Cape Verde                        132  1950  17.9
    #> 10        2 Central African Republic          140  1950  22.4
    #> # ℹ 6,848 more rows

## Multiple indicators

The `get_wdce()` function needs to be called multiple times to download multiple indicators. This can be done using the `map()` function in `purrr`

    mi <- tibble(ind = c("odr", "nirate", "ggapedu25")) %>%
      mutate(d = map(.x = ind, .f = ~get_wcde(indicator = .x)))
    mi
    #> # A tibble: 3 × 2
    #>   ind       d
    #>   <chr>     <list>
    #> 1 odr       <tibble [6,858 × 5]>
    #> 2 nirate    <tibble [6,630 × 5]>
    #> 3 ggapedu25 <tibble [52,776 × 6]>

    mi %>%
      filter(ind == "odr") %>%
      select(-ind) %>%
      unnest(cols = d)
    #> # A tibble: 6,858 × 5
    #>    scenario name                     country_code  year   odr
    #>       <dbl> <chr>                           <dbl> <dbl> <dbl>
    #>  1        2 Bulgaria                          100  1950  0.1
    #>  2        2 Myanmar                           104  1950  0.05
    #>  3        2 Burundi                           108  1950  0.06
    #>  4        2 Belarus                           112  1950  0.13
    #>  5        2 Cambodia                          116  1950  0.05
    #>  6        2 Algeria                            12  1950  0.06
    #>  7        2 Cameroon                          120  1950  0.06
    #>  8        2 Canada                            124  1950  0.12
    #>  9        2 Cape Verde                        132  1950  0.07
    #> 10        2 Central African Republic          140  1950  0.09
    #> # ℹ 6,848 more rows

    mi %>%
      filter(ind == "nirate") %>%
      select(-ind) %>%
      unnest(cols = d)
    #> # A tibble: 6,630 × 5
    #>    scenario name                     country_code period    nirate
    #>       <dbl> <chr>                           <dbl> <chr>      <dbl>
    #>  1        2 Bulgaria                          100 1950-1955   11
    #>  2        2 Myanmar                           104 1950-1955   19.8
    #>  3        2 Burundi                           108 1950-1955   27.9
    #>  4        2 Belarus                           112 1950-1955    9.9
    #>  5        2 Cambodia                          116 1950-1955   25.2
    #>  6        2 Algeria                            12 1950-1955   29.1
    #>  7        2 Cameroon                          120 1950-1955   16.2
    #>  8        2 Canada                            124 1950-1955   20
    #>  9        2 Cape Verde                        132 1950-1955   27.4
    #> 10        2 Central African Republic          140 1950-1955   15.1
    #> # ℹ 6,620 more rows

    mi %>%
      filter(ind == "ggapedu25") %>%
      select(-ind) %>%
      unnest(cols = d)
    #> # A tibble: 52,776 × 6
    #>    scenario name                     country_code  year education    ggapedu25
    #>       <dbl> <chr>                           <dbl> <dbl> <chr>            <dbl>
    #>  1        2 Bulgaria                          100  1950 No Education     -17.8
    #>  2        2 Myanmar                           104  1950 No Education     -19.3
    #>  3        2 Burundi                           108  1950 No Education     -22.9
    #>  4        2 Belarus                           112  1950 No Education     -19.8
    #>  5        2 Cambodia                          116  1950 No Education     -27.3
    #>  6        2 Algeria                            12  1950 No Education      -1.2
    #>  7        2 Cameroon                          120  1950 No Education     -19
    #>  8        2 Canada                            124  1950 No Education       0
    #>  9        2 Cape Verde                        132  1950 No Education     -23.7
    #> 10        2 Central African Republic          140  1950 No Education     -20.2
    #> # ℹ 52,766 more rows

## Previous versions

Previous versions of projections from the Wittgenstein Centre for Demography are available using the `version` argument in `get_wdce()`. Set `version` to `"wcde-v1"` or `"wcde-v2"` or `"wcde-v3"` (the default since 2024).

    get_wcde(indicator = "etfr",
             country_name = c("Brazil", "Albania"),
             version = "wcde-v2")
    #> # A tibble: 204 × 6
    #>    scenario name    country_code education          period     etfr
    #>       <dbl> <chr>          <dbl> <chr>              <chr>     <dbl>
    #>  1        2 Brazil            76 No Education       2015-2020  2.47
    #>  2        2 Albania            8 No Education       2015-2020  1.88
    #>  3        2 Brazil            76 Incomplete Primary 2015-2020  2.47
    #>  4        2 Albania            8 Incomplete Primary 2015-2020  1.88
    #>  5        2 Brazil            76 Primary            2015-2020  2.47
    #>  6        2 Albania            8 Primary            2015-2020  1.88
    #>  7        2 Brazil            76 Lower Secondary    2015-2020  1.89
    #>  8        2 Albania            8 Lower Secondary    2015-2020  1.9
    #>  9        2 Brazil            76 Upper Secondary    2015-2020  1.37
    #> 10        2 Albania            8 Upper Secondary    2015-2020  1.57
    #> # ℹ 194 more rows

Note, not all indicators and scenarios are available in all versions - see the the `wic_indicators` and `wic_scenarios` objects for further details or see above.

## Server

If you have trouble with connecting to the IIASA server you can try alternative hosts using the `server` option in `get_wcde()`, which can be set to `"iiasa"` (default) `"github"` and `"1&1"`.

    get_wcde(indicator = "etfr",
             country_name = c("Brazil", "Albania"),
             version = "wcde-v2", server = "github")
    #> # A tibble: 204 × 6
    #>    scenario name    country_code education          period     etfr
    #>       <dbl> <chr>          <dbl> <chr>              <chr>     <dbl>
    #>  1        2 Brazil            76 No Education       2015-2020  2.47
    #>  2        2 Albania            8 No Education       2015-2020  1.88
    #>  3        2 Brazil            76 Incomplete Primary 2015-2020  2.47
    #>  4        2 Albania            8 Incomplete Primary 2015-2020  1.88
    #>  5        2 Brazil            76 Primary            2015-2020  2.47
    #>  6        2 Albania            8 Primary            2015-2020  1.88
    #>  7        2 Brazil            76 Lower Secondary    2015-2020  1.89
    #>  8        2 Albania            8 Lower Secondary    2015-2020  1.9
    #>  9        2 Brazil            76 Upper Secondary    2015-2020  1.37
    #> 10        2 Albania            8 Upper Secondary    2015-2020  1.57
    #> # ℹ 194 more rows

You may also set `server = "search-available"` to search through the three possible data location to download the data wherever it is available.

# Working with population data

Population data for a range of age-sex-educational attainment combinations can be obtained by setting `indicator = "pop"` in `get_wcde()` and specifying a `pop_age`, `pop_sex` and `pop_edu` arguments. By default each of the three population breakdown arguments are set to “total”

    get_wcde(indicator = "pop", country_name = "India")
    #> # A tibble: 31 × 5
    #>    scenario name  country_code  year     pop
    #>       <dbl> <chr>        <dbl> <dbl>   <dbl>
    #>  1        2 India          356  1950 353104.
    #>  2        2 India          356  1955 394095
    #>  3        2 India          356  1960 440828.
    #>  4        2 India          356  1965 494677.
    #>  5        2 India          356  1970 551306.
    #>  6        2 India          356  1975 616603.
    #>  7        2 India          356  1980 688875.
    #>  8        2 India          356  1985 771520.
    #>  9        2 India          356  1990 861205.
    #> 10        2 India          356  1995 954786.
    #> # ℹ 21 more rows

The `pop_age` argument can be set to `all` to get population data broken down in five-year age groups. The `pop_sex` argument can be set to `both` to get population data broken down into female and male groups. The `pop_edu` argument can be set to `four`, `six` or `eight` to get population data broken down into education categorizations with different levels of detail.

    get_wcde(indicator = "pop", country_code = 900, pop_edu = "four")
    #> # A tibble: 155 × 6
    #>    scenario name  country_code  year education          pop
    #>       <dbl> <fct>        <dbl> <dbl> <fct>            <dbl>
    #>  1        2 World          900  1950 Under 15       857662.
    #>  2        2 World          900  1950 No Education   779147.
    #>  3        2 World          900  1950 Primary        495037.
    #>  4        2 World          900  1950 Secondary      320938.
    #>  5        2 World          900  1950 Post Secondary  24890.
    #>  6        2 World          900  1955 Under 15       971774.
    #>  7        2 World          900  1955 No Education   779160.
    #>  8        2 World          900  1955 Primary        548060
    #>  9        2 World          900  1955 Secondary      384565.
    #> 10        2 World          900  1955 Post Secondary  35093.
    #> # ℹ 145 more rows

The population breakdown arguments can be used in combination to provide further breakdowns, for example sex and education specific population totals

    get_wcde(indicator = "pop", country_code = 900, pop_edu = "six", pop_sex = "both")
    #> # A tibble: 434 × 7
    #>    scenario name  country_code  year sex    education              pop
    #>       <dbl> <fct>        <dbl> <dbl> <fct>  <fct>                <dbl>
    #>  1        2 World          900  1950 Male   Under 15           438532.
    #>  2        2 World          900  1950 Male   No Education       333362
    #>  3        2 World          900  1950 Male   Incomplete Primary 109944.
    #>  4        2 World          900  1950 Male   Primary            167428.
    #>  5        2 World          900  1950 Male   Lower Secondary    102241.
    #>  6        2 World          900  1950 Male   Upper Secondary     66705.
    #>  7        2 World          900  1950 Male   Post Secondary      16416.
    #>  8        2 World          900  1950 Female Under 15           419131.
    #>  9        2 World          900  1950 Female No Education       445785.
    #> 10        2 World          900  1950 Female Incomplete Primary  72736.
    #> # ℹ 424 more rows

The full age-sex-education specific data can also be obtained by setting `indicator = "epop"` in `get_wcde()`.

# Population pyramids

Create population pyramids by setting male population values to negative equivalent to allow for divergent columns from the y axis.

    w <- get_wcde(indicator = "pop", country_code = 900,
                  pop_age = "all", pop_sex = "both", pop_edu = "four",
                  version = "wcde-v3")
    w
    #> # A tibble: 4,650 × 8
    #>    scenario name  country_code  year age    sex    education          pop
    #>       <dbl> <fct>        <dbl> <dbl> <fct>  <fct>  <fct>            <dbl>
    #>  1        2 World          900  1950 0--4   Male   Under 15       170451.
    #>  2        2 World          900  1950 0--4   Female Under 15       163206.
    #>  3        2 World          900  1950 5--9   Male   Under 15       136460.
    #>  4        2 World          900  1950 5--9   Female Under 15       130304.
    #>  5        2 World          900  1950 10--14 Male   Under 15       131621.
    #>  6        2 World          900  1950 10--14 Female Under 15       125620
    #>  7        2 World          900  1950 15--19 Male   No Education    33724.
    #>  8        2 World          900  1950 15--19 Male   Primary         49895
    #>  9        2 World          900  1950 15--19 Male   Secondary       35984.
    #> 10        2 World          900  1950 15--19 Male   Post Secondary    312.
    #> # ℹ 4,640 more rows

    w <- w %>%
      mutate(pop_pm = ifelse(test = sex == "Male", yes = -pop, no = pop),
             pop_pm = pop_pm/1e3)
    w
    #> # A tibble: 4,650 × 9
    #>    scenario name  country_code  year age    sex    education        pop   pop_pm
    #>       <dbl> <fct>        <dbl> <dbl> <fct>  <fct>  <fct>          <dbl>    <dbl>
    #>  1        2 World          900  1950 0--4   Male   Under 15      1.70e5 -170.
    #>  2        2 World          900  1950 0--4   Female Under 15      1.63e5  163.
    #>  3        2 World          900  1950 5--9   Male   Under 15      1.36e5 -136.
    #>  4        2 World          900  1950 5--9   Female Under 15      1.30e5  130.
    #>  5        2 World          900  1950 10--14 Male   Under 15      1.32e5 -132.
    #>  6        2 World          900  1950 10--14 Female Under 15      1.26e5  126.
    #>  7        2 World          900  1950 15--19 Male   No Education  3.37e4  -33.7
    #>  8        2 World          900  1950 15--19 Male   Primary       4.99e4  -49.9
    #>  9        2 World          900  1950 15--19 Male   Secondary     3.60e4  -36.0
    #> 10        2 World          900  1950 15--19 Male   Post Seconda… 3.12e2   -0.312
    #> # ℹ 4,640 more rows

## Standard plot

Use standard ggplot code to create population pyramid with

- `scale_x_symmetric()` from the `lemon` package to allow for equal male and female x-axis
- fill colours set to the `wic_col4` object in the wcde package which contains the names of the colours used in the Wittgenstein Centre Human Capital Data Explorer Data Explorer.

Note `wic_col6` and `wic_col8` objects also exist for equivalent plots of population data objects with corresponding numbers of categories of education.

    library(lemon)

    w %>%
      filter(year == 2020) %>%
      ggplot(mapping = aes(x = pop_pm, y = age, fill = fct_rev(education))) +
      geom_col() +
      geom_vline(xintercept = 0, colour = "black") +
      scale_x_symmetric(labels = abs) +
      scale_fill_manual(values = wic_col4, name = "Education") +
      labs(x = "Population (millions)", y = "Age") +
      theme_bw()

## Sex label position

Add male and female labels on the x-axis by

- Creating a facet plot with the strips on the bottom with transparent backgrounds and no space between.
- Set the x axis to have zero expansion beyond the values in the data allowing the two sides of the pyramids to meet.
- Add a `geom_blank()` to allow for equal x-axis and additional space at the end of largest columns.

    w <- w %>%
      mutate(pop_max = ifelse(sex == "Male", -max(pop/1e3), max(pop/1e3)))

    w %>%
      filter(year == 2020) %>%
      ggplot(mapping = aes(x = pop_pm, y = age, fill = fct_rev(education))) +
      geom_col() +
      geom_vline(xintercept = 0, colour = "black") +
      scale_x_continuous(labels = abs, expand = c(0, 0)) +
      scale_fill_manual(values = wic_col4, name = "Education") +
      labs(x = "Population (millions)", y = "Age") +
      facet_wrap(facets = "sex", scales = "free_x", strip.position = "bottom") +
      geom_blank(mapping = aes(x = pop_max * 1.1)) +
      theme(panel.spacing.x = unit(0, "pt"),
            strip.placement = "outside",
            strip.background = element_rect(fill = "transparent"),
            strip.text.x = element_text(margin = margin( b = 0, t = 0)))

## Animate

Animate the pyramid through the past data and projection periods using the `transition_time()` function in the `gganimate` package

    library(gganimate)

    ggplot(data = w,
           mapping = aes(x = pop_pm, y = age, fill = fct_rev(education))) +
      geom_col() +
      geom_vline(xintercept = 0, colour = "black") +
      scale_x_continuous(labels = abs, expand = c(0, 0)) +
      scale_fill_manual(values = wic_col4, name = "Education") +
      facet_wrap(facets = "sex", scales = "free_x", strip.position = "bottom") +
      geom_blank(mapping = aes(x = pop_max * 1.1)) +
      theme(panel.spacing.x = unit(0, "pt"),
            strip.placement = "outside",
            strip.background = element_rect(fill = "transparent"),
            strip.text.x = element_text(margin = margin(b = 0, t = 0))) +
      transition_time(time = year) +
      labs(x = "Population (millions)", y = "Age",
           title = 'SSP2 World Population {round(frame_time)}')