tests/testthat/test-basepop.R
In timriffe/DemoTools: Standardize, Evaluate, and Adjust Demographic Data
# (1) RefYear
refDate <- 1986.21
# (2) Reported population by 5-year age groups and sex in the base year (Include unknowns).
pop_male_counts <- c(`0` = 11684,`1` = 46738,`5` = 55639,`10` = 37514,
`15` = 29398, `20` = 27187,`25` = 27770,`30` = 20920,
`35` = 16973,`40` = 14999, `45` = 11330,`50` = 10415,
`55` = 6164,`60` = 7330,`65` = 3882, `70` = 3882,
`75` = 1840,`80` = 4200
)
pop_female_counts <- c(`0` = 11673,`1` = 46693,`5` = 55812,`10` = 35268,
`15` = 33672, `20` = 31352,`25` = 33038,`30` = 24029,
`35` = 16120,`40` = 14679, `45` = 8831,`50` = 9289,
`55` = 4172,`60` = 6174,`65` = 2715, `70` = 3344,
`75` = 1455, `80` = 4143)
# (4) Sex ratio at birth (m/f)
sex_ratio <- 1.0300
# (6) The male and female nLx functions for ages under 1 year, 1 to 4 years, and 5 to 9
# years, pertaining to an earlier and later date
nLxDatesIn <- c(1977.31, 1986.50)
nLxMale <- matrix(c(87732,
304435,
361064,
88451,
310605,
370362
),
nrow = 3, ncol = 2)
# includes age 10 patch
nLxFemale <- matrix(c(89842,314521,372681,666666,353053,340650,326588,311481,
295396,278646,261260,241395,217419,90478,320755,
382531,666666,364776, 353538,340687, 326701, 311573,
295501, 278494, 258748,234587),
nrow = 13,
ncol = 2)
rownames(nLxFemale) <- c(0,1,seq(5,55,by=5))
# (7) A set of age-specific fertility rates pertaining to an earlier and later
# date
AsfrMat <- matrix(c(0.2000,0.3000,0.3000, 0.2500, 0.2000,
0.1500, 0.0500,0.1500,0.2000,0.2750,
0.2250, 0.1750, 0.1250, 0.0500),
nrow = 7,
ncol = 2)
rownames(AsfrMat) <- seq(15,45,by=5)
AsfrDatesIn <- c(1977.81, 1985.71)
smoothed_females <- smooth_age_5(Value = pop_female_counts,
Age = as.numeric(names(pop_female_counts)),
method = "Arriaga",
OAG = TRUE,
young.tail = "Original")
smoothed_females <- c(pop_female_counts[1:2], smoothed_females[-1])
smoothed_males <- smooth_age_5(Value = pop_male_counts,
Age = as.numeric(names(pop_male_counts)),
method = "Arriaga",
OAG = TRUE,
young.tail = "Original")
smoothed_males <- c(pop_male_counts[1:2], smoothed_males[-1])
## This is the only number that messes up the whole calculation.
## smooth_age_5 returns the same result as the PASS excel sheet
## except for the age groups 10-15 and 15-19. Here we only use
## age group 15-19. If we plug in manually the correct value,
## we get all results match exactly, otherwise there are
## some differences.
smoothed_females[4] <- 34721
# smoothed_males will be off in the same cell, but inconsequential for children
test_that("basepop_five - bpa matches the expected result from PASS", {
bpa <-
basepop_five(
refDate = 1986.21,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
method = "linear",
radix = 100000
)
# matches BASEPOP1altA.xlsx
#expect_true(all(round(bpa$Males_adjusted[1:3], 0) == c(13559, 47444, 54397)))
expect_true(all(round(bpa$Males_adjusted[1:3], 0) == c(13406, 47184, 54397)))
#expect_true(all(round(bpa_female[1:3], 0) == c(13467, 47576, 54554)))
expect_true(all(round(bpa$Females_adjusted[1:3], 0) == c(13315, 47315, 54554)))
})
test_that("basepop_five - bpe matches the expected result from PASS", {
bpe <-
basepop_five(
refDate = refDate,
Males_five = pop_male_counts,
Females_five = pop_female_counts,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
method = "linear",
radix = 100000
)
expect_true(all(round(bpe$Males_adjusted[1:3], 0) == c(13679, 47967, 55721)))
expect_true(all(round(bpe$Females_adjusted[1:3], 0) == c(13587, 48101, 55882)))
})
# test_that("basepop_single fails if provided five year age groups", {
#
# female_single <-
# c(
# `0` = 11673,
# `1` = 11474,
# `2` = 11670,
# `3` = 11934,
# `4` = 11614,
# `5` = 10603,
# `6` = 11144,
# `7` = 11179,
# `8` = 11269,
# `9` = 11617,
# `10` = 6772,
# `11` = 6948,
# `12` = 7030,
# `13` = 7211,
# `14` = 7306,
# `15` = 6531,
# `16` = 6443,
# `17` = 6535,
# `18` = 6951,
# `19` = 7213,
# `20` = 6096,
# `21` = 6234,
# `22` = 6327,
# `23` = 6410,
# `24` = 6285,
# `25` = 6464,
# `26` = 6492,
# `27` = 6549,
# `28` = 6739,
# `29` = 6795,
# `30` = 5013,
# `31` = 4888,
# `32` = 4735,
# `33` = 4747,
# `34` = 4646,
# `35` = 3040,
# `36` = 3068,
# `37` = 3107,
# `38` = 3246,
# `39` = 3658,
# `40` = 2650,
# `41` = 2788,
# `42` = 2977,
# `43` = 3108,
# `44` = 3156,
# `45` = 1756,
# `46` = 1784,
# `47` = 1802,
# `48` = 1764,
# `49` = 1724,
# `50` = 1982,
# `51` = 1935,
# `52` = 1846,
# `53` = 1795,
# `54` = 1731,
# `55` = 863,
# `56` = 850,
# `57` = 825,
# `58` = 819,
# `59` = 816,
# `60` = 1348,
# `61` = 1342,
# `62` = 1246,
# `63` = 1138,
# `64` = 1101,
# `65` = 391,
# `66` = 520,
# `67` = 585,
# `68` = 560,
# `69` = 659,
# `70` = 670,
# `71` = 750,
# `72` = 686,
# `73` = 634,
# `74` = 604,
# `75` = 353,
# `76` = 340,
# `77` = 270,
# `78` = 246,
# `79` = 247,
# `80` = 4143
# )
#
# # Correction for males
# # To test that males are checked for single ages, we first
# # correctly define the female pop counts as single ages
# expect_error(
# basepop_single(
# refDate = refDate,
# Males_single = pop_male_counts,
# Females_single = female_single,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "is_single(as.numeric(names(Males_single))) is not TRUE",
# fixed = TRUE
# )
#
# # check that pop male counts are named
# expect_error(
# basepop_single(
# refDate = refDate,
# Males_single = setNames(pop_male_counts, NULL),
# Females_single = female_single,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "!is.null(names(Males_single)) is not TRUE",
# fixed = TRUE
# )
#
# # Check female counts are single ages
# expect_error(
# basepop_single(
# refDate = refDate,
# Females_single = pop_female_counts,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "is_single(as.numeric(names(Females_single))) is not TRUE",
# fixed = TRUE
# )
#
# # Check that pop female counts are named
# expect_error(
# basepop_single(
# refDate = refDate,
# Females_single = setNames(pop_female_counts, NULL),
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "!is.null(names(Females_single)) is not TRUE",
# fixed = TRUE
# )
#
# # Works as well for smoothing
# expect_error(
# basepop_single(
# refDate = refDate,
# Males_single = pop_male_counts,
# Females_single = pop_female_counts,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "is_single(as.numeric(names(Females_single))) is not TRUE",
# fixed = TRUE
# )
# })
pop_male_counts <-
c(`0` = 11684,`1` = 11473,`2` = 11647,`3` = 11939,`4` = 11680,
`5` = 10600,`6` = 11100,`7` = 11157,`8` = 11238,`9` = 11544,
`10` = 7216,`11` = 7407,`12` = 7461,`13` = 7656,`14` = 7774,
`15` = 5709,`16` = 5629,`17` = 5745,`18` = 6056,`19` = 6259,
`20` = 5303,`21` = 5423,`22` = 5497,`23` = 5547,`24` = 5417,
`25` = 5441,`26` = 5466,`27` = 5500,`28` = 5668,`29` = 5694,
`30` = 4365,`31` = 4252,`32` = 4122,`33` = 4142,`34` = 4039,
`35` = 3210,`36` = 3222,`37` = 3258,`38` = 3413,`39` = 3871,
`40` = 2684,`41` = 2844,`42` = 3052,`43` = 3182,`44` = 3237,
`45` = 2263,`46` = 2298,`47` = 2318,`48` = 2257,`49` = 2194,
`50` = 2231,`51` = 2172,`52` = 2072,`53` = 2008,`54` = 1932,
`55` = 1301,`56` = 1262,`57` = 1213,`58` = 1197,`59` = 1191,
`60` = 1601,`61` = 1593,`62` = 1490,`63` = 1348,`64` = 1299,
`65` = 568,`66` = 745,`67` = 843,`68` = 801,`69` = 925,
`70` = 806,`71` = 883,`72` = 796,`73` = 725,`74` = 672,
`75` = 470,`76` = 441,`77` = 340,`78` = 300,`79` = 289,
`80` = 4200
)
pop_female_counts <-
c(`0` = 11673,`1` = 11474,`2` = 11670,`3` = 11934,`4` = 11614,
`5` = 10603,`6` = 11144,`7` = 11179,`8` = 11269,`9` = 11617,
`10` = 6772,`11` = 6948,`12` = 7030,`13` = 7211,`14` = 7306,
`15` = 6531,`16` = 6443,`17` = 6535,`18` = 6951,`19` = 7213,
`20` = 6096,`21` = 6234,`22` = 6327,`23` = 6410,`24` = 6285,
`25` = 6464,`26` = 6492,`27` = 6549,`28` = 6739,`29` = 6795,
`30` = 5013,`31` = 4888,`32` = 4735,`33` = 4747,`34` = 4646,
`35` = 3040,`36` = 3068,`37` = 3107,`38` = 3246,`39` = 3658,
`40` = 2650,`41` = 2788,`42` = 2977,`43` = 3108,`44` = 3156,
`45` = 1756,`46` = 1784,`47` = 1802,`48` = 1764,`49` = 1724,
`50` = 1982,`51` = 1935,`52` = 1846,`53` = 1795,`54` = 1731,
`55` = 863,`56` = 850,`57` = 825,`58` = 819,`59` = 816,
`60` = 1348,`61` = 1342,`62` = 1246,`63` = 1138,`64` = 1101,
`65` = 391,`66` = 520,`67` = 585,`68` = 560,`69` = 659,
`70` = 670,`71` = 750,`72` = 686,`73` = 634,`74` = 604,
`75` = 353,`76` = 340,`77` = 270,`78` = 246,`79` = 247,
`80` = 4143
)
smoothed_females <- smooth_age_5(Value = pop_female_counts,
Age = as.numeric(names(pop_female_counts)),
method = "Arriaga",
OAG = TRUE,
young.tail = "Original")
# test_that("basepop_single does calculation for males when providing Males_single", {
#
# # Since the default is for females, I saved the correct calculations for males
# # and just check that the current implementation matches it
# res <-
# basepop_single(
# refDate = refDate,
# Males_single = pop_male_counts,
# Females_single = pop_female_counts,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear",
# radix = 100000
# )
#
# correct_res_males <-
# c(
# `0` = 13315,
# `1` = 11614,
# `2` = 11791,
# `3` = 12086,
# `4` = 11824,
# `5` = 10393,
# `6` = 10883,
# `7` = 10939,
# `8` = 11019,
# `9` = 11319
# )
#
# expect_equivalent(round(res[1:10], 0), correct_res_males)
# })
refDate <- 1986
location <- "Spain"
res <- fertestr::FetchPopWpp2019(location, refDate, ages = 0:100, sex = "female")
pop_female_counts <- single2abridged(setNames(res$pop, res$ages))
res <- fertestr::FetchPopWpp2019(location, refDate, ages = 0:100, sex = "male")
pop_male_counts <- single2abridged(setNames(res$pop, res$ages))
# Download asfr matrix to test that it can download the nLx only
invisible(
capture.output(
nLxFemale <-
downloadnLx(
NULL,
location,
gender = "female",
c(1978, 1985.5)
)
)
)
# Download asfr matrix to test that it can download the nLx only
invisible(capture.output(AsfrMat <- downloadAsfr(NULL, location, c(1978, 1985.5))))
test_that("basepop_five can download data for nLx", {
# Female
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
AsfrMat = AsfrMat
)
)
# Test that the output print doesn't print its
# downloading the Asfr
expect_false(any(grepl("^Downloading Asfr", output)))
# Test that it's downloading nLx for the two years and female
expect_true(sum(grepl("^Downloading nLx", output)) == 2)
})
test_that("basepop_five can download data for asfr", {
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Test that the output print doesn't print it's
# downloading the nLx
expect_false(any(grepl("^Downloading nLx", output)))
# For males
# There's no need to test this separately between gender because
# the Asfr matrix is downloaded regardless of gender. However,
# since the code is becoming convoluted, I'm testing just in case
# output <-
# capture.output(
# basepop_five(
# location = location,
# refDate = refDate,
# nLxFemale = nLxFemale,
# Females_five = pop_female_counts,
# Males_five = pop_male_counts,
# female = TRUE
# )
# )
#
# expect_false(any(grepl("^Downloading nLx", output)))
})
test_that("basepop_five infers radix if not provided", {
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
expect_true(sum(grepl("^Setting radix", output)) == 1)
})
test_that("basepop raises error when downloads needed but no location is specified", {
expect_error(
basepop_five(
refDate = refDate,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
verbose = FALSE
),
"You need to provide a location to download the data for nLx"
)
expect_error(
basepop_five(
refDate = refDate,
AsfrMat = AsfrMat,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
radix = 1,
verbose = FALSE
),
"You need to provide a location to download the data for nLx"
)
expect_error(
basepop_five(
refDate = refDate,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
radix = 1,
verbose = FALSE
),
"You need to provide a location to download the data for Asfrmat"
)
# If provided all correct arguments, it download the data
# successfully
expect_success({
res <-
basepop_five(
location = "Spain",
refDate = refDate,
AsfrMat = AsfrMat,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
radix = 1,
verbose = FALSE
)
expect_type(res$Females_adjusted, "double")
})
})
test_that("basepop_five can download from dates provided", {
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxDatesIn = c(1978, 1986.5),
AsfrDatesIn = c(1978, 1985.5),
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Test that the output print doesn't print its
# calculating the datesin for both nLx and Asfr.
expect_false(any(grepl("^Assuming the two", output)))
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxDatesIn = c(1978, 1986.5),
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Test that it calculates the dates for the asfr
expect_true(sum(grepl("^Assuming the two", output)) == 1)
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Tests that it calculates the dates for nLx and asfr
expect_true(sum(grepl("^Assuming the two", output)) == 2)
})
test_that("basepop works with up to year 1955", {
# This is where the test actually happens since
# internally we subtract 7.5 from the refDate to download
# the nLx and Asfr data. So the minimum year will be 1955.
res <-
basepop_five(
location = "Spain",
refDate= 1962.5,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
method = "linear",
radix = 100000
)
expect_true("1955" %in% colnames(res$Asfr))
expect_true("1955" %in% colnames(res$nLxm))
expect_true("1955" %in% colnames(res$nLxf))
})
test_that("basepop works well with SRBDatesIn", {
# Most of the tests for SRBDatesIn are actually done when
# testing downloadSRB. These tests just make sure that
# basepop can handle NULL/Non-NULL SRBDatesIn dates. Everything
# else is forward to downloadSRB.
# Works when SRBDatesIn is NULL, meaning that it convert them
# to refDate - c(0.5, 2.5, 7.5)
expect_success({
res <-
basepop_five(
location = "Spain",
refDate= 1962.5,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
SRBDatesIn = NULL,
method = "linear",
radix = 100000
)
expect_type(res, "list")
})
# Works when SRBDatesIn is an actual date.
expect_success({
res <-
basepop_five(
location = "Spain",
refDate= 1962.5,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
SRBDatesIn = 1960,
method = "linear",
radix = 100000
)
expect_type(res, "list")
})
})
# IW: no need capping at 1955
# test_that("basepop caps nLxDatesIn to 1955 when provided a date below that", {
#
# tmp_nlx <- c(1954, 1960)
# expect_output(
# tmp <-
# basepop_five(
# location = "Spain",
# refDate = 1960,
# Males_five = smoothed_males,
# Females_five = smoothed_females,
# SRB = sex_ratio,
# nLxDatesIn = tmp_nlx,
# AsfrDatesIn = c(1955, 1960),
# method = "linear",
# radix = 100000
# ),
# regexp = "nLxDate\\(s\\) 1954 is/are below 1955\\. Capping at 1955",
# all = FALSE
# )
#
# tmp_asfr <- c(1954, 1960)
# expect_output(
# tmp <-
# basepop_five(
# location = "Spain",
# refDate = 1960,
# Males_five = smoothed_males,
# Females_five = smoothed_females,
# SRB = sex_ratio,
# nLxDatesIn = c(1955, 1960),
# AsfrDatesIn = tmp_asfr,
# method = "linear",
# radix = 100000
# ),
# regexp = "AsfrDate\\(s\\) 1954 is/are below 1955\\. Capping at 1955",
# all = FALSE
# )
#
# })
test_that("basepop fails when it implies an extrapolation of > 5 years", {
## For nLxDatesIn ##
## By setting refDate to 1974, the difference between 1974 - 7.5 and the
## minimum of nLxDatesIn is greater than five.
expect_error(
basepop_five(
refDate = 1974,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
radix = 100000
),
regexp = "nLxDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
## By setting refDate to 1995, the difference between 1995 - 0.5 and the
## maximum of nLxDatesIn is greater than five.
expect_error(
basepop_five(
refDate = 1995,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
radix = 100000
),
regexp = "nLxDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
## For AsfrDatesIn
## Here we just provide AsfrDatesIn which we are much higher than refDate - 7.5
expect_error(
basepop_five(
refDate = 1986,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = c(1925, 1930),
radix = 100000
),
regexp = "AsfrDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
## Here we just provide AsfrDatesIn which we are much higher than refDate - 0.5
expect_error(
basepop_five(
refDate = 1986,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = c(2020, 2025),
radix = 100000
),
regexp = "AsfrDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
})
srb_checker <- function(x, ordered_name = FALSE) {
expect_length(x, 3)
expect_named(x)
expect_type(x, "double")
# Check the names of x are returned ordered
# This only makes sense when we download the data from WPP because
# otherwise we respect the order provided by the user.
if (ordered_name) {
expect_true(all(names(x) == as.character(sort(as.numeric(names(x))))))
}
}
test_that("downloadSRB works as expected", {
# Should return same estimate three times
srb_checker(downloadSRB(c(1.05), DatesOut = c(1999, 1920, 1930)))
# Should return three values with the last being 1.05
srb_checker(downloadSRB(c(1.05, 1.07), DatesOut = c(1999, 1920, 1930)))
# Should return the same thing
srb_checker(downloadSRB(c(1.05, 1.07, 1.08), DatesOut = c(1999, 1920, 1930)))
# Should error
expect_error(
downloadSRB(1:4),
regexp = "SRB can only accept three dates at maximum",
fixed = TRUE
)
# Should return three SRBs
srb_checker(downloadSRB(SRB = NULL, location = "Spain", DatesOut = 1955:1957))
# Assumes SRB
outp <- capture_output_lines(
downloadSRB(SRB = NULL,
location = "Whatever",
DatesOut = 1955:1957)
)
expect_true(all(
c("Whatever not available in DemoToolsData::WPP2019_births",
"Assuming SRB to be 1.047 ") %in% outp) )
# Should fail because of number of years
expect_error(
downloadSRB(SRB = NULL, location = "Whatever", DatesOut = 1955:1958),
regexp = "SRB can only accept three dates at maximum",
fixed = TRUE
)
# Should impute the first two years with the last
srb_checker(
downloadSRB(SRB = NULL, location = "Germany", DatesOut = 1948:1950),
ordered_name = TRUE
)
# Should impute all values
srb_checker(
downloadSRB(SRB = NULL, location = "Germany", DatesOut = 1947:1949),
ordered_name = TRUE
)
})
# Interpolation between pivot wpp years, also into the period 1950-1955
tfr_pj <- data.frame(
year = c(1950.0,1950.5,1951.5,1952.5,1953.0,
1953.5,1954.5,1955.0,1955.5,
1956.5,1957.5,1958.0,1958.5,
1959.5,1960.0,1960.5,1961.5,
1962.5,1963.0,1963.5,1964.5,
1965.0,1965.5,1966.5,1967.5,
1968.0,1968.5,1969.5,1970.0,
1970.5,1971.5,1972.5,1973.0),
tfr = c(7.2986,7.3290,7.3898,7.4506,7.4810,7.5114,7.5722,7.6026,7.6330,
7.6938,7.7546,7.7850,7.8130,7.8690,7.8970,7.9250,7.9810,8.0370,
8.0650,8.0695,8.0785,8.0830,8.0875,8.0965,8.1055,8.1100,8.0980,
8.0740,8.0620,8.0500,8.0260,8.0020,7.9900))
test_that("Replicate Peter Johnson´s excel for extrapolatebeyond 1955",{
expect_equal(tfr_pj$tfr,
downloadAsfr(Asfrmat = NULL, location = "Kenya",
AsfrDatesIn = tfr_pj$year) %>% colSums() %>%
as.numeric() * 5
)
})
test_that("Receive a message if asked dates are not in 1950-2025 interval",{
expect_output(downloadAsfr(Asfrmat = NULL, location = "Kenya",
AsfrDatesIn = 1900),
regexp = "Careful, extrapolating beyond range 1950-2025")
expect_output(downloadnLx(nLx = NULL, location = "Kenya",
nLxDatesIn = 1900, gender="both"),
regexp = "Careful, extrapolating beyond range 1950-2025")
})
timriffe/DemoTools documentation built on Oct. 14, 2024, 12:53 p.m.
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# (1) RefYear
refDate <- 1986.21
# (2) Reported population by 5-year age groups and sex in the base year (Include unknowns).
pop_male_counts <- c(`0` = 11684,`1` = 46738,`5` = 55639,`10` = 37514,
`15` = 29398, `20` = 27187,`25` = 27770,`30` = 20920,
`35` = 16973,`40` = 14999, `45` = 11330,`50` = 10415,
`55` = 6164,`60` = 7330,`65` = 3882, `70` = 3882,
`75` = 1840,`80` = 4200
)
pop_female_counts <- c(`0` = 11673,`1` = 46693,`5` = 55812,`10` = 35268,
`15` = 33672, `20` = 31352,`25` = 33038,`30` = 24029,
`35` = 16120,`40` = 14679, `45` = 8831,`50` = 9289,
`55` = 4172,`60` = 6174,`65` = 2715, `70` = 3344,
`75` = 1455, `80` = 4143)
# (4) Sex ratio at birth (m/f)
sex_ratio <- 1.0300
# (6) The male and female nLx functions for ages under 1 year, 1 to 4 years, and 5 to 9
# years, pertaining to an earlier and later date
nLxDatesIn <- c(1977.31, 1986.50)
nLxMale <- matrix(c(87732,
304435,
361064,
88451,
310605,
370362
),
nrow = 3, ncol = 2)
# includes age 10 patch
nLxFemale <- matrix(c(89842,314521,372681,666666,353053,340650,326588,311481,
295396,278646,261260,241395,217419,90478,320755,
382531,666666,364776, 353538,340687, 326701, 311573,
295501, 278494, 258748,234587),
nrow = 13,
ncol = 2)
rownames(nLxFemale) <- c(0,1,seq(5,55,by=5))
# (7) A set of age-specific fertility rates pertaining to an earlier and later
# date
AsfrMat <- matrix(c(0.2000,0.3000,0.3000, 0.2500, 0.2000,
0.1500, 0.0500,0.1500,0.2000,0.2750,
0.2250, 0.1750, 0.1250, 0.0500),
nrow = 7,
ncol = 2)
rownames(AsfrMat) <- seq(15,45,by=5)
AsfrDatesIn <- c(1977.81, 1985.71)
smoothed_females <- smooth_age_5(Value = pop_female_counts,
Age = as.numeric(names(pop_female_counts)),
method = "Arriaga",
OAG = TRUE,
young.tail = "Original")
smoothed_females <- c(pop_female_counts[1:2], smoothed_females[-1])
smoothed_males <- smooth_age_5(Value = pop_male_counts,
Age = as.numeric(names(pop_male_counts)),
method = "Arriaga",
OAG = TRUE,
young.tail = "Original")
smoothed_males <- c(pop_male_counts[1:2], smoothed_males[-1])
## This is the only number that messes up the whole calculation.
## smooth_age_5 returns the same result as the PASS excel sheet
## except for the age groups 10-15 and 15-19. Here we only use
## age group 15-19. If we plug in manually the correct value,
## we get all results match exactly, otherwise there are
## some differences.
smoothed_females[4] <- 34721
# smoothed_males will be off in the same cell, but inconsequential for children
test_that("basepop_five - bpa matches the expected result from PASS", {
bpa <-
basepop_five(
refDate = 1986.21,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
method = "linear",
radix = 100000
)
# matches BASEPOP1altA.xlsx
#expect_true(all(round(bpa$Males_adjusted[1:3], 0) == c(13559, 47444, 54397)))
expect_true(all(round(bpa$Males_adjusted[1:3], 0) == c(13406, 47184, 54397)))
#expect_true(all(round(bpa_female[1:3], 0) == c(13467, 47576, 54554)))
expect_true(all(round(bpa$Females_adjusted[1:3], 0) == c(13315, 47315, 54554)))
})
test_that("basepop_five - bpe matches the expected result from PASS", {
bpe <-
basepop_five(
refDate = refDate,
Males_five = pop_male_counts,
Females_five = pop_female_counts,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
method = "linear",
radix = 100000
)
expect_true(all(round(bpe$Males_adjusted[1:3], 0) == c(13679, 47967, 55721)))
expect_true(all(round(bpe$Females_adjusted[1:3], 0) == c(13587, 48101, 55882)))
})
# test_that("basepop_single fails if provided five year age groups", {
#
# female_single <-
# c(
# `0` = 11673,
# `1` = 11474,
# `2` = 11670,
# `3` = 11934,
# `4` = 11614,
# `5` = 10603,
# `6` = 11144,
# `7` = 11179,
# `8` = 11269,
# `9` = 11617,
# `10` = 6772,
# `11` = 6948,
# `12` = 7030,
# `13` = 7211,
# `14` = 7306,
# `15` = 6531,
# `16` = 6443,
# `17` = 6535,
# `18` = 6951,
# `19` = 7213,
# `20` = 6096,
# `21` = 6234,
# `22` = 6327,
# `23` = 6410,
# `24` = 6285,
# `25` = 6464,
# `26` = 6492,
# `27` = 6549,
# `28` = 6739,
# `29` = 6795,
# `30` = 5013,
# `31` = 4888,
# `32` = 4735,
# `33` = 4747,
# `34` = 4646,
# `35` = 3040,
# `36` = 3068,
# `37` = 3107,
# `38` = 3246,
# `39` = 3658,
# `40` = 2650,
# `41` = 2788,
# `42` = 2977,
# `43` = 3108,
# `44` = 3156,
# `45` = 1756,
# `46` = 1784,
# `47` = 1802,
# `48` = 1764,
# `49` = 1724,
# `50` = 1982,
# `51` = 1935,
# `52` = 1846,
# `53` = 1795,
# `54` = 1731,
# `55` = 863,
# `56` = 850,
# `57` = 825,
# `58` = 819,
# `59` = 816,
# `60` = 1348,
# `61` = 1342,
# `62` = 1246,
# `63` = 1138,
# `64` = 1101,
# `65` = 391,
# `66` = 520,
# `67` = 585,
# `68` = 560,
# `69` = 659,
# `70` = 670,
# `71` = 750,
# `72` = 686,
# `73` = 634,
# `74` = 604,
# `75` = 353,
# `76` = 340,
# `77` = 270,
# `78` = 246,
# `79` = 247,
# `80` = 4143
# )
#
# # Correction for males
# # To test that males are checked for single ages, we first
# # correctly define the female pop counts as single ages
# expect_error(
# basepop_single(
# refDate = refDate,
# Males_single = pop_male_counts,
# Females_single = female_single,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "is_single(as.numeric(names(Males_single))) is not TRUE",
# fixed = TRUE
# )
#
# # check that pop male counts are named
# expect_error(
# basepop_single(
# refDate = refDate,
# Males_single = setNames(pop_male_counts, NULL),
# Females_single = female_single,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "!is.null(names(Males_single)) is not TRUE",
# fixed = TRUE
# )
#
# # Check female counts are single ages
# expect_error(
# basepop_single(
# refDate = refDate,
# Females_single = pop_female_counts,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "is_single(as.numeric(names(Females_single))) is not TRUE",
# fixed = TRUE
# )
#
# # Check that pop female counts are named
# expect_error(
# basepop_single(
# refDate = refDate,
# Females_single = setNames(pop_female_counts, NULL),
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "!is.null(names(Females_single)) is not TRUE",
# fixed = TRUE
# )
#
# # Works as well for smoothing
# expect_error(
# basepop_single(
# refDate = refDate,
# Males_single = pop_male_counts,
# Females_single = pop_female_counts,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear"
# ),
# "is_single(as.numeric(names(Females_single))) is not TRUE",
# fixed = TRUE
# )
# })
pop_male_counts <-
c(`0` = 11684,`1` = 11473,`2` = 11647,`3` = 11939,`4` = 11680,
`5` = 10600,`6` = 11100,`7` = 11157,`8` = 11238,`9` = 11544,
`10` = 7216,`11` = 7407,`12` = 7461,`13` = 7656,`14` = 7774,
`15` = 5709,`16` = 5629,`17` = 5745,`18` = 6056,`19` = 6259,
`20` = 5303,`21` = 5423,`22` = 5497,`23` = 5547,`24` = 5417,
`25` = 5441,`26` = 5466,`27` = 5500,`28` = 5668,`29` = 5694,
`30` = 4365,`31` = 4252,`32` = 4122,`33` = 4142,`34` = 4039,
`35` = 3210,`36` = 3222,`37` = 3258,`38` = 3413,`39` = 3871,
`40` = 2684,`41` = 2844,`42` = 3052,`43` = 3182,`44` = 3237,
`45` = 2263,`46` = 2298,`47` = 2318,`48` = 2257,`49` = 2194,
`50` = 2231,`51` = 2172,`52` = 2072,`53` = 2008,`54` = 1932,
`55` = 1301,`56` = 1262,`57` = 1213,`58` = 1197,`59` = 1191,
`60` = 1601,`61` = 1593,`62` = 1490,`63` = 1348,`64` = 1299,
`65` = 568,`66` = 745,`67` = 843,`68` = 801,`69` = 925,
`70` = 806,`71` = 883,`72` = 796,`73` = 725,`74` = 672,
`75` = 470,`76` = 441,`77` = 340,`78` = 300,`79` = 289,
`80` = 4200
)
pop_female_counts <-
c(`0` = 11673,`1` = 11474,`2` = 11670,`3` = 11934,`4` = 11614,
`5` = 10603,`6` = 11144,`7` = 11179,`8` = 11269,`9` = 11617,
`10` = 6772,`11` = 6948,`12` = 7030,`13` = 7211,`14` = 7306,
`15` = 6531,`16` = 6443,`17` = 6535,`18` = 6951,`19` = 7213,
`20` = 6096,`21` = 6234,`22` = 6327,`23` = 6410,`24` = 6285,
`25` = 6464,`26` = 6492,`27` = 6549,`28` = 6739,`29` = 6795,
`30` = 5013,`31` = 4888,`32` = 4735,`33` = 4747,`34` = 4646,
`35` = 3040,`36` = 3068,`37` = 3107,`38` = 3246,`39` = 3658,
`40` = 2650,`41` = 2788,`42` = 2977,`43` = 3108,`44` = 3156,
`45` = 1756,`46` = 1784,`47` = 1802,`48` = 1764,`49` = 1724,
`50` = 1982,`51` = 1935,`52` = 1846,`53` = 1795,`54` = 1731,
`55` = 863,`56` = 850,`57` = 825,`58` = 819,`59` = 816,
`60` = 1348,`61` = 1342,`62` = 1246,`63` = 1138,`64` = 1101,
`65` = 391,`66` = 520,`67` = 585,`68` = 560,`69` = 659,
`70` = 670,`71` = 750,`72` = 686,`73` = 634,`74` = 604,
`75` = 353,`76` = 340,`77` = 270,`78` = 246,`79` = 247,
`80` = 4143
)
smoothed_females <- smooth_age_5(Value = pop_female_counts,
Age = as.numeric(names(pop_female_counts)),
method = "Arriaga",
OAG = TRUE,
young.tail = "Original")
# test_that("basepop_single does calculation for males when providing Males_single", {
#
# # Since the default is for females, I saved the correct calculations for males
# # and just check that the current implementation matches it
# res <-
# basepop_single(
# refDate = refDate,
# Males_single = pop_male_counts,
# Females_single = pop_female_counts,
# SmoothedFemales = smoothed_females,
# SRB = sex_ratio,
# nLxFemale = nLxFemale,
# nLxMale = nLxMale,
# nLxDatesIn = nLxDatesIn,
# AsfrMat = AsfrMat,
# AsfrDatesIn = AsfrDatesIn,
# method = "linear",
# radix = 100000
# )
#
# correct_res_males <-
# c(
# `0` = 13315,
# `1` = 11614,
# `2` = 11791,
# `3` = 12086,
# `4` = 11824,
# `5` = 10393,
# `6` = 10883,
# `7` = 10939,
# `8` = 11019,
# `9` = 11319
# )
#
# expect_equivalent(round(res[1:10], 0), correct_res_males)
# })
refDate <- 1986
location <- "Spain"
res <- fertestr::FetchPopWpp2019(location, refDate, ages = 0:100, sex = "female")
pop_female_counts <- single2abridged(setNames(res$pop, res$ages))
res <- fertestr::FetchPopWpp2019(location, refDate, ages = 0:100, sex = "male")
pop_male_counts <- single2abridged(setNames(res$pop, res$ages))
# Download asfr matrix to test that it can download the nLx only
invisible(
capture.output(
nLxFemale <-
downloadnLx(
NULL,
location,
gender = "female",
c(1978, 1985.5)
)
)
)
# Download asfr matrix to test that it can download the nLx only
invisible(capture.output(AsfrMat <- downloadAsfr(NULL, location, c(1978, 1985.5))))
test_that("basepop_five can download data for nLx", {
# Female
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
AsfrMat = AsfrMat
)
)
# Test that the output print doesn't print its
# downloading the Asfr
expect_false(any(grepl("^Downloading Asfr", output)))
# Test that it's downloading nLx for the two years and female
expect_true(sum(grepl("^Downloading nLx", output)) == 2)
})
test_that("basepop_five can download data for asfr", {
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Test that the output print doesn't print it's
# downloading the nLx
expect_false(any(grepl("^Downloading nLx", output)))
# For males
# There's no need to test this separately between gender because
# the Asfr matrix is downloaded regardless of gender. However,
# since the code is becoming convoluted, I'm testing just in case
# output <-
# capture.output(
# basepop_five(
# location = location,
# refDate = refDate,
# nLxFemale = nLxFemale,
# Females_five = pop_female_counts,
# Males_five = pop_male_counts,
# female = TRUE
# )
# )
#
# expect_false(any(grepl("^Downloading nLx", output)))
})
test_that("basepop_five infers radix if not provided", {
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
expect_true(sum(grepl("^Setting radix", output)) == 1)
})
test_that("basepop raises error when downloads needed but no location is specified", {
expect_error(
basepop_five(
refDate = refDate,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
verbose = FALSE
),
"You need to provide a location to download the data for nLx"
)
expect_error(
basepop_five(
refDate = refDate,
AsfrMat = AsfrMat,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
radix = 1,
verbose = FALSE
),
"You need to provide a location to download the data for nLx"
)
expect_error(
basepop_five(
refDate = refDate,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
radix = 1,
verbose = FALSE
),
"You need to provide a location to download the data for Asfrmat"
)
# If provided all correct arguments, it download the data
# successfully
expect_success({
res <-
basepop_five(
location = "Spain",
refDate = refDate,
AsfrMat = AsfrMat,
Females_five = pop_female_counts,
Males_five = pop_male_counts,
radix = 1,
verbose = FALSE
)
expect_type(res$Females_adjusted, "double")
})
})
test_that("basepop_five can download from dates provided", {
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxDatesIn = c(1978, 1986.5),
AsfrDatesIn = c(1978, 1985.5),
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Test that the output print doesn't print its
# calculating the datesin for both nLx and Asfr.
expect_false(any(grepl("^Assuming the two", output)))
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
nLxDatesIn = c(1978, 1986.5),
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Test that it calculates the dates for the asfr
expect_true(sum(grepl("^Assuming the two", output)) == 1)
output <-
capture.output(
basepop_five(
location = location,
refDate = refDate,
Females_five = pop_female_counts,
Males_five = pop_male_counts
)
)
# Tests that it calculates the dates for nLx and asfr
expect_true(sum(grepl("^Assuming the two", output)) == 2)
})
test_that("basepop works with up to year 1955", {
# This is where the test actually happens since
# internally we subtract 7.5 from the refDate to download
# the nLx and Asfr data. So the minimum year will be 1955.
res <-
basepop_five(
location = "Spain",
refDate= 1962.5,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
method = "linear",
radix = 100000
)
expect_true("1955" %in% colnames(res$Asfr))
expect_true("1955" %in% colnames(res$nLxm))
expect_true("1955" %in% colnames(res$nLxf))
})
test_that("basepop works well with SRBDatesIn", {
# Most of the tests for SRBDatesIn are actually done when
# testing downloadSRB. These tests just make sure that
# basepop can handle NULL/Non-NULL SRBDatesIn dates. Everything
# else is forward to downloadSRB.
# Works when SRBDatesIn is NULL, meaning that it convert them
# to refDate - c(0.5, 2.5, 7.5)
expect_success({
res <-
basepop_five(
location = "Spain",
refDate= 1962.5,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
SRBDatesIn = NULL,
method = "linear",
radix = 100000
)
expect_type(res, "list")
})
# Works when SRBDatesIn is an actual date.
expect_success({
res <-
basepop_five(
location = "Spain",
refDate= 1962.5,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
SRBDatesIn = 1960,
method = "linear",
radix = 100000
)
expect_type(res, "list")
})
})
# IW: no need capping at 1955
# test_that("basepop caps nLxDatesIn to 1955 when provided a date below that", {
#
# tmp_nlx <- c(1954, 1960)
# expect_output(
# tmp <-
# basepop_five(
# location = "Spain",
# refDate = 1960,
# Males_five = smoothed_males,
# Females_five = smoothed_females,
# SRB = sex_ratio,
# nLxDatesIn = tmp_nlx,
# AsfrDatesIn = c(1955, 1960),
# method = "linear",
# radix = 100000
# ),
# regexp = "nLxDate\\(s\\) 1954 is/are below 1955\\. Capping at 1955",
# all = FALSE
# )
#
# tmp_asfr <- c(1954, 1960)
# expect_output(
# tmp <-
# basepop_five(
# location = "Spain",
# refDate = 1960,
# Males_five = smoothed_males,
# Females_five = smoothed_females,
# SRB = sex_ratio,
# nLxDatesIn = c(1955, 1960),
# AsfrDatesIn = tmp_asfr,
# method = "linear",
# radix = 100000
# ),
# regexp = "AsfrDate\\(s\\) 1954 is/are below 1955\\. Capping at 1955",
# all = FALSE
# )
#
# })
test_that("basepop fails when it implies an extrapolation of > 5 years", {
## For nLxDatesIn ##
## By setting refDate to 1974, the difference between 1974 - 7.5 and the
## minimum of nLxDatesIn is greater than five.
expect_error(
basepop_five(
refDate = 1974,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
radix = 100000
),
regexp = "nLxDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
## By setting refDate to 1995, the difference between 1995 - 0.5 and the
## maximum of nLxDatesIn is greater than five.
expect_error(
basepop_five(
refDate = 1995,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = AsfrDatesIn,
radix = 100000
),
regexp = "nLxDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
## For AsfrDatesIn
## Here we just provide AsfrDatesIn which we are much higher than refDate - 7.5
expect_error(
basepop_five(
refDate = 1986,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = c(1925, 1930),
radix = 100000
),
regexp = "AsfrDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
## Here we just provide AsfrDatesIn which we are much higher than refDate - 0.5
expect_error(
basepop_five(
refDate = 1986,
Males_five = smoothed_males,
Females_five = smoothed_females,
SRB = sex_ratio,
nLxFemale = nLxFemale,
nLxMale = nLxMale,
nLxDatesIn = nLxDatesIn,
AsfrMat = AsfrMat,
AsfrDatesIn = c(2020, 2025),
radix = 100000
),
regexp = "AsfrDatesIn implies an extrapolation of > 5 years to achieve the needed reference dates",
fixed = TRUE
)
})
srb_checker <- function(x, ordered_name = FALSE) {
expect_length(x, 3)
expect_named(x)
expect_type(x, "double")
# Check the names of x are returned ordered
# This only makes sense when we download the data from WPP because
# otherwise we respect the order provided by the user.
if (ordered_name) {
expect_true(all(names(x) == as.character(sort(as.numeric(names(x))))))
}
}
test_that("downloadSRB works as expected", {
# Should return same estimate three times
srb_checker(downloadSRB(c(1.05), DatesOut = c(1999, 1920, 1930)))
# Should return three values with the last being 1.05
srb_checker(downloadSRB(c(1.05, 1.07), DatesOut = c(1999, 1920, 1930)))
# Should return the same thing
srb_checker(downloadSRB(c(1.05, 1.07, 1.08), DatesOut = c(1999, 1920, 1930)))
# Should error
expect_error(
downloadSRB(1:4),
regexp = "SRB can only accept three dates at maximum",
fixed = TRUE
)
# Should return three SRBs
srb_checker(downloadSRB(SRB = NULL, location = "Spain", DatesOut = 1955:1957))
# Assumes SRB
outp <- capture_output_lines(
downloadSRB(SRB = NULL,
location = "Whatever",
DatesOut = 1955:1957)
)
expect_true(all(
c("Whatever not available in DemoToolsData::WPP2019_births",
"Assuming SRB to be 1.047 ") %in% outp) )
# Should fail because of number of years
expect_error(
downloadSRB(SRB = NULL, location = "Whatever", DatesOut = 1955:1958),
regexp = "SRB can only accept three dates at maximum",
fixed = TRUE
)
# Should impute the first two years with the last
srb_checker(
downloadSRB(SRB = NULL, location = "Germany", DatesOut = 1948:1950),
ordered_name = TRUE
)
# Should impute all values
srb_checker(
downloadSRB(SRB = NULL, location = "Germany", DatesOut = 1947:1949),
ordered_name = TRUE
)
})
# Interpolation between pivot wpp years, also into the period 1950-1955
tfr_pj <- data.frame(
year = c(1950.0,1950.5,1951.5,1952.5,1953.0,
1953.5,1954.5,1955.0,1955.5,
1956.5,1957.5,1958.0,1958.5,
1959.5,1960.0,1960.5,1961.5,
1962.5,1963.0,1963.5,1964.5,
1965.0,1965.5,1966.5,1967.5,
1968.0,1968.5,1969.5,1970.0,
1970.5,1971.5,1972.5,1973.0),
tfr = c(7.2986,7.3290,7.3898,7.4506,7.4810,7.5114,7.5722,7.6026,7.6330,
7.6938,7.7546,7.7850,7.8130,7.8690,7.8970,7.9250,7.9810,8.0370,
8.0650,8.0695,8.0785,8.0830,8.0875,8.0965,8.1055,8.1100,8.0980,
8.0740,8.0620,8.0500,8.0260,8.0020,7.9900))
test_that("Replicate Peter Johnson´s excel for extrapolatebeyond 1955",{
expect_equal(tfr_pj$tfr,
downloadAsfr(Asfrmat = NULL, location = "Kenya",
AsfrDatesIn = tfr_pj$year) %>% colSums() %>%
as.numeric() * 5
)
})
test_that("Receive a message if asked dates are not in 1950-2025 interval",{
expect_output(downloadAsfr(Asfrmat = NULL, location = "Kenya",
AsfrDatesIn = 1900),
regexp = "Careful, extrapolating beyond range 1950-2025")
expect_output(downloadnLx(nLx = NULL, location = "Kenya",
nLxDatesIn = 1900, gender="both"),
regexp = "Careful, extrapolating beyond range 1950-2025")
})
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