The wcde package allows for R users to easily download data from the Wittgenstein Centre 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 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: 204 x 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 x 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 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 x 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 x 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 x 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 rows

Past 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 x 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 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 x 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

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 x 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 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: 204 x 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 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: 230 x 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 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, 21, 22)) %>%
  filter(period == "2095-2100")
#> # A tibble: 10 x 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   -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 x 7
#>   scenario scenario_name         scenario_abb name    country_code period    tfr
#>      <dbl> <chr>                 <chr>        <chr>          <dbl> <chr>   <dbl>
#> 1        1 Rapid Development (S~ SSP1         Kenya            404 2045-2~  1.62
#> 2        1 Rapid Development (S~ SSP1         Nigeria          566 2045-2~  2.29
#> 3        1 Rapid Development (S~ SSP1         Algeria           12 2045-2~  1.53
#> 4        2 Medium (SSP2)         SSP2         Kenya            404 2045-2~  2.36
#> 5        2 Medium (SSP2)         SSP2         Nigeria          566 2045-2~  3.37
#> 6        2 Medium (SSP2)         SSP2         Algeria           12 2045-2~  1.77
#> 7        3 Stalled Development ~ SSP3         Kenya            404 2045-2~  3.33
#> 8        3 Stalled Development ~ SSP3         Nigeria          566 2045-2~  4.65
#> 9        3 Stalled Development ~ SSP3         Algeria           12 2045-2~  2.41

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: 5 x 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 SSP2DM

All 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 x 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 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 x 2
#>   ind       d                    
#>   <chr>     <list>               
#> 1 odr       <tibble [7,099 x 5]> 
#> 2 nirate    <tibble [6,870 x 5]> 
#> 3 ggapedu25 <tibble [41,346 x 6]>

mi %>%
  filter(ind == "odr") %>%
  select(-ind) %>%
  unnest(cols = d)
#> # A tibble: 7,099 x 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 x 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 x 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 rows

Working with population data

The education population data in the data explorer, obtained by setting indicator = "epop" in get_wcde(), provide results by up to three different education categorizations (4, 6 and 8 education groups).

d <- get_wcde(indicator = "epop", country_code = 900)
d
#> # A tibble: 16,368 x 8
#>    scenario name  country_code age   sex   education           year     epop
#>       <dbl> <chr>        <dbl> <chr> <chr> <chr>              <dbl>    <dbl>
#>  1        2 World          900 All   Both  Total               1950 2541292.
#>  2        2 World          900 All   Both  Under 15            1950  868844.
#>  3        2 World          900 All   Both  No Education        1950  763612.
#>  4        2 World          900 All   Both  Incomplete Primary  1950  197922.
#>  5        2 World          900 All   Both  Primary             1950  351588.
#>  6        2 World          900 All   Both  Lower Secondary     1950  209181.
#>  7        2 World          900 All   Both  Upper Secondary     1950  120001.
#>  8        2 World          900 All   Both  Post Secondary      1950   30143.
#>  9        2 World          900 0--4  Both  Total               1950  338387.
#> 10        2 World          900 0--4  Both  Under 15            1950  338387.
#> # ... with 16,358 more rows

As the data frame contains multiple groupings, the edu_group_sum() function can be used to provide education specific population data. Users can specify the education groupings by setting the n argument to 4, 6 or 8.

d %>% 
  edu_group_sum(n = 4) %>%
  filter(year == 2020)
#> # A tibble: 210 x 8
#>    scenario name  country_code  year age   sex    education         epop
#>       <dbl> <fct>        <dbl> <dbl> <fct> <fct>  <fct>            <dbl>
#>  1        2 World          900  2020 0--4  Male   Under 15       340742.
#>  2        2 World          900  2020 0--4  Male   No Education        0 
#>  3        2 World          900  2020 0--4  Male   Primary             0 
#>  4        2 World          900  2020 0--4  Male   Secondary           0 
#>  5        2 World          900  2020 0--4  Male   Post Secondary      0 
#>  6        2 World          900  2020 0--4  Female Under 15       319616.
#>  7        2 World          900  2020 0--4  Female No Education        0 
#>  8        2 World          900  2020 0--4  Female Primary             0 
#>  9        2 World          900  2020 0--4  Female Secondary           0 
#> 10        2 World          900  2020 0--4  Female Post Secondary      0 
#> # ... with 200 more rows

d %>% 
  edu_group_sum(n = 6) %>%
  filter(year == 2020,
         age == "30--34")
#> # A tibble: 14 x 8
#>    scenario name  country_code  year age    sex    education            epop
#>       <dbl> <fct>        <dbl> <dbl> <fct>  <fct>  <fct>               <dbl>
#>  1        2 World          900  2020 30--34 Male   Under 15               0 
#>  2        2 World          900  2020 30--34 Male   No Education       23820.
#>  3        2 World          900  2020 30--34 Male   Incomplete Primary 14488.
#>  4        2 World          900  2020 30--34 Male   Primary            35713.
#>  5        2 World          900  2020 30--34 Male   Lower Secondary    69758.
#>  6        2 World          900  2020 30--34 Male   Upper Secondary    96988.
#>  7        2 World          900  2020 30--34 Male   Post Secondary     68842.
#>  8        2 World          900  2020 30--34 Female Under 15               0 
#>  9        2 World          900  2020 30--34 Female No Education       35410.
#> 10        2 World          900  2020 30--34 Female Incomplete Primary 14645.
#> 11        2 World          900  2020 30--34 Female Primary            33563.
#> 12        2 World          900  2020 30--34 Female Lower Secondary    60923.
#> 13        2 World          900  2020 30--34 Female Upper Secondary    83646.
#> 14        2 World          900  2020 30--34 Female Post Secondary     69475.

Population pyramids

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

w <- d %>% 
  edu_group_sum(n = 4) %>%
  mutate(pop = ifelse(test = sex == "Male", yes = -epop, no = epop),
         pop = pop/1e3) 
w
#> # A tibble: 6,510 x 9
#>    scenario name  country_code  year age   sex    education         epop   pop
#>       <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 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, 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), max(pop)))

w %>%
  filter(year == 2020) %>%
  ggplot(mapping = aes(x = pop, 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, 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)}')