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 interestscenario: a number referring to a SSP narrative, by default 2 is used (for SSP2)country_code(orcountry_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 rowsIndicator 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 rowsPast 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 rowsThe 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 1Country 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 rowsThe 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 rowsA 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 rowsScenarios
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 -1Set 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-… 2.68Additional 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 TRUEAll 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 rowsMultiple 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 rowsPrevious 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 rowsNote, 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 rowsYou 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 rowsThe 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 rowsThe 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 rowsThe 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 rowsStandard plot
Use standard ggplot code to create population pyramid with
scale_x_symmetric()from thelemonpackage to allow for equal male and female x-axis- fill colours set to the
wic_col4object 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)}')