context('Indicators')
set.year <- function(wpp.year) {
if(!get.wpp.year()==wpp.year)
set.wpp.year(wpp.year)
}
test_that('fertility age profile has the right dimension', {
prof <- wppExplorerBayesMig:::get.age.profile.fert(2000, c('FR', 'AF'))
expect_true(all(dim(prof) == c(2*7, 4))) # 2 countries by 7 age groups
prof <- wppExplorerBayesMig:::get.age.profile.fert(2020, 'GE')
expect_true(all(dim(prof) == c(7, 4)))
expect_true(all(prof$charcode == 'GE'))
})
test_that('age-specific fertility rate has the right value', {
ind <- wpp.by.year(wpp.by.country(wpp.indicator('fertage', age="20-24"), 'FR'), 2015)$value
data(tfr, package="wpp2015")
data(percentASFR, package="wpp2015")
tfrFR <- subset(tfr, country_code==250)["2010-2015"]
asfrFR <- subset(percentASFR, country_code==250 & age=="20-24")["2010-2015"]
expect_true(ind == tfrFR*asfrFR/100)
})
test_that('mortality values for high ages come out correctly', {
set.year(2015)
mx <- wpp.by.year(wpp.by.country(wpp.indicator('mortagesex', sex="M", age="100+"), 'FR'), 2015)$value
expect_true(mx > 0.51)
mx <- wpp.by.year(wpp.by.country(wpp.indicator('mortagesex', sex="M", age="100+"), 'FI'), 2015)$value
expect_true(mx > 0.5 & mx < 0.51)
mx2 <- wpp.by.year(wpp.by.country(wpp.indicator('mortagesex', sex="M", age="100"), 'FI'), 2015)$value
expect_true(all.equal(mx, mx2))
mx <- wpp.by.year(wpp.by.country(wpp.indicator('mortagesex', sex="F", age="110"), 'FI'), 2015)$value
expect_true(mx > 1.2)
})
test_that('migration rate has the right value', {
ind <- wpp.by.year(wpp.by.country(wpp.indicator('migrate'), 'AE'), 2005)$value
data(pop, package="wpp2015BayesMig")
data(migration, package="wpp2015BayesMig")
popUA <- sum(subset(pop, country_code==784)[c("2000", "2005")])/2
migUA <- subset(migration, country_code == 784)["2000-2005"]
expect_true(ind == migUA*200/popUA)
})
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