Overview of the wcde package
Guy J. Abel, Samir K.C., Michaela Potancokova, Claudia Reiter, Andrea Tamburini and Dilek Yildiz
Source:vignettes/wcde.Rmd
wcde.Rmd
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(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: 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
#> # … with 194 more rows
# download education specific survivorship rates
get_wcde(indicator = "eassr",
country_name = c("Niger", "Korea"))
#> # A tibble: 8,976 × 8
#> scenario name country_code age sex education period eassr
#> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 2 Niger 562 Newborn Male No Educat… 2015-… 91.6
#> 2 2 Republic of Korea 410 Newborn Male No Educat… 2015-… 99.4
#> 3 2 Niger 562 Newborn Male Incomplet… 2015-… 92
#> 4 2 Republic of Korea 410 Newborn Male Incomplet… 2015-… 99.5
#> 5 2 Niger 562 Newborn Male Primary 2015-… 92.5
#> 6 2 Republic of Korea 410 Newborn Male Primary 2015-… 99.5
#> 7 2 Niger 562 Newborn Male Lower Sec… 2015-… 93.4
#> 8 2 Republic of Korea 410 Newborn Male Lower Sec… 2015-… 99.6
#> 9 2 Niger 562 Newborn Male Upper Sec… 2015-… 95.2
#> 10 2 Republic of Korea 410 Newborn Male Upper Sec… 2015-… 99.7
#> # … with 8,966 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 × 3
#> indicator description definition
#> <chr> <chr> <chr>
#> 1 tfr Total Fertility Rate "The average number of children b…
#> 2 etfr Total Fertility Rate by Education "The average number of children b…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 80.7
#> 2 2 Australia 36 Male 2015-2020 81.3
#> 3 2 Japan 392 Female 2015-2020 87.2
#> 4 2 Australia 36 Female 2015-2020 85
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 1.06
#> 2 2 Republic of Korea 410 All 2020 1
#> 3 2 China 156 0--4 2020 1.15
#> 4 2 Republic of Korea 410 0--4 2020 1.07
#> 5 2 China 156 5--9 2020 1.16
#> 6 2 Republic of Korea 410 5--9 2020 1.07
#> 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 1.16
#> 10 2 Republic of Korea 410 15--19 2020 1.1
#> # … with 34 more rowsPast data is only available for selected indicators. These can be
viewed using the past indicator column:
wic_indicators %>%
filter(past) %>%
select(1:2)
#> # A tibble: 28 × 2
#> indicator description
#> <chr> <chr>
#> 1 pop Population Size (000's)
#> 2 bpop Population Size by Broad Age (000's)
#> 3 epop Population Size by Education (000's)
#> 4 prop Educational Attainment Distribution
#> 5 bprop Educational Attainment Distribution by Broad Age
#> 6 growth Average Annual Growth Rate
#> 7 nirate Average Annual Rate of Natural Increase
#> 8 sexratio Sex Ratio
#> 9 mage Population Median Age
#> 10 tdr Total Dependency Ratio
#> # … with 18 more rowsThe filter() function can also be used to filter
specific indicators to specific age, sex or education groups
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 6.97
#> 2 2 Spain 724 1950-1955 2.53
#> 3 2 Austria 40 1950-1955 2.1
#> 4 2 United Arab Emirates 784 1955-1960 6.97
#> 5 2 Spain 724 1955-1960 2.7
#> 6 2 Austria 40 1955-1960 2.57
#> 7 2 United Arab Emirates 784 1960-1965 6.87
#> 8 2 Spain 724 1960-1965 2.81
#> 9 2 Austria 40 1960-1965 2.78
#> 10 2 United Arab Emirates 784 1965-1970 6.77
#> # … with 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: 204 × 6
#> scenario name country_code education period etfr
#> <dbl> <chr> <dbl> <chr> <chr> <dbl>
#> 1 2 Bahamas 44 No Education 2015-2020 2.71
#> 2 2 Bulgaria 100 No Education 2015-2020 1.72
#> 3 2 Bahamas 44 Incomplete Primary 2015-2020 2.71
#> 4 2 Bulgaria 100 Incomplete Primary 2015-2020 1.72
#> 5 2 Bahamas 44 Primary 2015-2020 2.71
#> 6 2 Bulgaria 100 Primary 2015-2020 1.72
#> 7 2 Bahamas 44 Lower Secondary 2015-2020 2.09
#> 8 2 Bulgaria 100 Lower Secondary 2015-2020 1.73
#> 9 2 Bahamas 44 Upper Secondary 2015-2020 1.76
#> 10 2 Bulgaria 100 Upper Secondary 2015-2020 1.44
#> # … with 194 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: 230 × 5
#> name isono continent region dim
#> <chr> <dbl> <chr> <chr> <chr>
#> 1 World 900 NA NA area
#> 2 Africa 903 NA NA area
#> 3 Asia 935 NA NA area
#> 4 Europe 908 NA NA area
#> 5 Latin America and the Caribbean 904 NA NA area
#> 6 Northern America 905 NA NA area
#> 7 Oceania 909 NA NA area
#> 8 Afghanistan 4 Asia South-Central Asia country
#> 9 Albania 8 Europe Southern Europe country
#> 10 Algeria 12 Africa Northern Africa country
#> # … with 220 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, 21, 22)) %>%
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 -0.7
#> 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 -0.2
#> 7 21 India 356 2095-2100 -0.5
#> 8 21 China 156 2095-2100 -0.9
#> 9 22 India 356 2095-2100 -0.5
#> 10 22 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-… 1.62
#> 2 1 Rapid Development (SSP1) SSP1 Nige… 566 2045-… 2.29
#> 3 1 Rapid Development (SSP1) SSP1 Alge… 12 2045-… 1.53
#> 4 2 Medium (SSP2) SSP2 Kenya 404 2045-… 2.36
#> 5 2 Medium (SSP2) SSP2 Nige… 566 2045-… 3.37
#> 6 2 Medium (SSP2) SSP2 Alge… 12 2045-… 1.77
#> 7 3 Stalled Development (SS… SSP3 Kenya 404 2045-… 3.33
#> 8 3 Stalled Development (SS… SSP3 Nige… 566 2045-… 4.65
#> 9 3 Stalled Development (SS… SSP3 Alge… 12 2045-… 2.41Additional 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: 5 × 3
#> scenario_name scenario scenario_abb
#> <chr> <dbl> <chr>
#> 1 Rapid Development (SSP1) 1 SSP1
#> 2 Medium (SSP2) 2 SSP2
#> 3 Stalled Development (SSP3) 3 SSP3
#> 4 Medium - Zero Migration (SSP2 - ZM) 21 SSP2ZM
#> 5 Medium - Double Migration (SSP2 - DM) 22 SSP2DMAll countries data
Data for all countries can be obtained by not setting
country_name or country_code
get_wcde(indicator = "mage")
#> # A tibble: 7,099 × 5
#> scenario name country_code year mage
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 2 Bulgaria 100 1950 27.3
#> 2 2 Myanmar 104 1950 22.8
#> 3 2 Burundi 108 1950 19.5
#> 4 2 Belarus 112 1950 27.2
#> 5 2 Cambodia 116 1950 18.7
#> 6 2 Algeria 12 1950 19.4
#> 7 2 Cameroon 120 1950 20.8
#> 8 2 Canada 124 1950 27.7
#> 9 2 Cape Verde 132 1950 23
#> 10 2 Central African Republic 140 1950 22.5
#> # … with 7,089 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 [7,099 × 5]>
#> 2 nirate <tibble [6,870 × 5]>
#> 3 ggapedu25 <tibble [41,346 × 6]>
mi %>%
filter(ind == "odr") %>%
select(-ind) %>%
unnest(cols = d)
#> # A tibble: 7,099 × 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 0.13
#> 10 2 Central African Republic 140 1950 0.09
#> # … with 7,089 more rows
mi %>%
filter(ind == "nirate") %>%
select(-ind) %>%
unnest(cols = d)
#> # A tibble: 6,870 × 5
#> scenario name country_code period nirate
#> <dbl> <chr> <dbl> <chr> <dbl>
#> 1 2 Bulgaria 100 1950-1955 11.1
#> 2 2 Myanmar 104 1950-1955 19.1
#> 3 2 Burundi 108 1950-1955 24.1
#> 4 2 Belarus 112 1950-1955 10.1
#> 5 2 Cambodia 116 1950-1955 25.9
#> 6 2 Algeria 12 1950-1955 27.1
#> 7 2 Cameroon 120 1950-1955 17.6
#> 8 2 Canada 124 1950-1955 18.9
#> 9 2 Cape Verde 132 1950-1955 26.9
#> 10 2 Central African Republic 140 1950-1955 10.7
#> # … with 6,860 more rows
mi %>%
filter(ind == "ggapedu25") %>%
select(-ind) %>%
unnest(cols = d)
#> # A tibble: 41,346 × 6
#> scenario name country_code year education ggapedu25
#> <dbl> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 2 Bulgaria 100 1950 No Education -20
#> 2 2 Myanmar 104 1950 No Education -13
#> 3 2 Burundi 108 1950 No Education -6
#> 4 2 Belarus 112 1950 No Education -10
#> 5 2 Cambodia 116 1950 No Education -21
#> 6 2 Algeria 12 1950 No Education -2
#> 7 2 Cameroon 120 1950 No Education -13
#> 8 2 Canada 124 1950 No Education -2
#> 9 2 Cape Verde 132 1950 No Education -9
#> 10 2 Central African Republic 140 1950 No Education -1
#> # … with 41,336 more rowsWorking 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 376325.
#> 2 2 India 356 1955 409276.
#> 3 2 India 356 1960 449604.
#> 4 2 India 356 1965 497830.
#> 5 2 India 356 1970 553787.
#> 6 2 India 356 1975 621525.
#> 7 2 India 356 1980 697040.
#> 8 2 India 356 1985 781904.
#> 9 2 India 356 1990 870422.
#> 10 2 India 356 1995 960733.
#> # … with 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 868844.
#> 2 2 World 900 1950 No Education 763612.
#> 3 2 World 900 1950 Primary 549510.
#> 4 2 World 900 1950 Secondary 329182.
#> 5 2 World 900 1950 Post Secondary 30143.
#> 6 2 World 900 1955 Under 15 984764.
#> 7 2 World 900 1955 No Education 762022.
#> 8 2 World 900 1955 Primary 600299.
#> 9 2 World 900 1955 Secondary 392261.
#> 10 2 World 900 1955 Post Secondary 38199.
#> # … with 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 443968.
#> 2 2 World 900 1950 Male No Education 317636.
#> 3 2 World 900 1950 Male Incomplete Primary 116692.
#> 4 2 World 900 1950 Male Primary 194902
#> 5 2 World 900 1950 Male Lower Secondary 104160
#> 6 2 World 900 1950 Male Upper Secondary 69384.
#> 7 2 World 900 1950 Male Post Secondary 21102.
#> 8 2 World 900 1950 Female Under 15 424877.
#> 9 2 World 900 1950 Female No Education 445976.
#> 10 2 World 900 1950 Female Incomplete Primary 81231.
#> # … with 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")
w
#> # A tibble: 6,510 × 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 172362.
#> 2 2 World 900 1950 0--4 Male No Education 0
#> 3 2 World 900 1950 0--4 Male Primary 0
#> 4 2 World 900 1950 0--4 Male Secondary 0
#> 5 2 World 900 1950 0--4 Male Post Secondary 0
#> 6 2 World 900 1950 0--4 Female Under 15 166026.
#> 7 2 World 900 1950 0--4 Female No Education 0
#> 8 2 World 900 1950 0--4 Female Primary 0
#> 9 2 World 900 1950 0--4 Female Secondary 0
#> 10 2 World 900 1950 0--4 Female Post Secondary 0
#> # … with 6,500 more rows
w <- w %>%
mutate(pop_pm = ifelse(test = sex == "Male", yes = -pop, no = pop),
pop_pm = pop_pm/1e3)
w
#> # A tibble: 6,510 × 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 172362. -172.
#> 2 2 World 900 1950 0--4 Male No Education 0 0
#> 3 2 World 900 1950 0--4 Male Primary 0 0
#> 4 2 World 900 1950 0--4 Male Secondary 0 0
#> 5 2 World 900 1950 0--4 Male Post Secondary 0 0
#> 6 2 World 900 1950 0--4 Female Under 15 166026. 166.
#> 7 2 World 900 1950 0--4 Female No Education 0 0
#> 8 2 World 900 1950 0--4 Female Primary 0 0
#> 9 2 World 900 1950 0--4 Female Secondary 0 0
#> 10 2 World 900 1950 0--4 Female Post Secondary 0 0
#> # … with 6,500 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)}')