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2.initial_population.R
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library(tidyverse)
library(here)
min_year <- 2000
# initial population ------------------------------------------------------
initial_pop <- read_csv(here("public_dataset","17100005.csv")) %>%
filter(GEO %in% c('Canada',"British Columbia")) %>%
filter(REF_DATE >= min_year) %>%
mutate(sex=str_sub(Sex,1,1),
age_remove = str_detect(`Age group`,'to|over|All|Median|Average'),
age_keep = `Age group`=="100 years and over")
initial_pop_distribution <- initial_pop %>%
filter(!age_remove | age_keep) %>%
mutate(age =as.numeric(gsub("([0-9]+).*$","\\1",`Age group`))) %>%
rename(n=VALUE) %>%
select(1,sex,age,GEO,n) %>%
rename(year=REF_DATE,
province=GEO) %>%
mutate(province = case_when(province=="Canada" ~ "CA",
province=="British Columbia" ~ "BC",
TRUE ~ "NONE")) %>%
filter(sex!="B")
# approximate
tmp <- initial_pop_distribution %>%
filter(is.na(n))
initial_pop_distribution %>%
filter(year %in% ((tmp$year %>% unique())+1)) %>%
filter(age %in% ((tmp$age %>% unique()) +1)) %>%
mutate(age=age-1) -> tmp2
tmp %>%
select(-n) %>%
left_join(tmp2 %>% select(-year),
by=c('sex','age','province')) %>%
select(n) %>%
unlist() -> tmp3
initial_pop_distribution$n[which(is.na(initial_pop_distribution$n))] <- tmp3
initial_pop_distribution %>%
filter(!is.na(n)) %>%
group_by(year,province) %>%
mutate(total_n = sum(n)) %>%
mutate(prop=n/total_n) -> initial_pop_distribution
initial_pop_distribution_both <- initial_pop_distribution %>%
group_by(year,age,province) %>%
mutate(n_both=sum(n)) %>%
mutate(prop_male = n/n_both) %>%
ungroup() %>%
filter(sex=='M') %>%
select(year,age,province,prop_male)
initial_pop_distribution_prop <- initial_pop_distribution %>%
group_by(year,age,province) %>%
summarise(n=sum(n)) %>%
ungroup() %>%
arrange(year,province)
initial_pop_distribution_birth_n <- initial_pop_distribution_prop %>%
filter(age==0) %>%
select(-age) %>%
rename(n_birth=n)
initial_pop_distribution_prop %>%
left_join(initial_pop_distribution_birth_n,by=c("year","province")) %>%
mutate(prop=n/n_birth) %>%
left_join(initial_pop_distribution_both,by=c("year","age","province"))-> initial_pop_distribution_prop_past
initial_pop <- read_csv(here("public_dataset","17100057.csv"))
colnames(initial_pop) <- gsub(' ','_',colnames(initial_pop))
pop_projection_BC_CA <- initial_pop %>%
select(REF_DATE,GEO,Projection_scenario,Sex,Age_group,VALUE) %>%
mutate(Projection_scenario = str_remove(Projection_scenario,"Projection scenario "),
Projection_scenario = str_remove(Projection_scenario, "\\:.*")) %>%
filter(GEO %in% c('Canada',"British Columbia"))
write_rds(pop_projection_BC_CA,"../src/processed_data/pop_projection_BC_CA.rds")
initial_pop_distribution <- pop_projection_BC_CA %>%
filter(REF_DATE >= max(initial_pop_distribution_prop$year)) %>%
mutate(Projection_scenario = str_remove(Projection_scenario,"Projection scenario "),
Projection_scenario = str_remove(Projection_scenario, "\\:.*")) %>%
mutate(sex=str_sub(Sex,1,1),
age_remove = str_detect(Age_group,'to|over|All|Median|Average'),
age_keep = Age_group=="100 years and over") %>%
filter(!age_remove | age_keep) %>%
mutate(age = as.numeric(gsub("([0-9]+).*$","\\1",Age_group)),
age = ifelse(is.na(age),0,age)) %>%
rename(n=VALUE,
projection_scenario = Projection_scenario ) %>%
select(1,projection_scenario,sex,age,GEO,n) %>%
rename(year=REF_DATE,
province=GEO) %>%
mutate(n=n*1000) %>%
mutate(province = case_when(province=="Canada" ~ "CA",
province=="British Columbia" ~ "BC",
TRUE ~ "NONE")) %>%
filter(sex!="B") %>%
filter(!is.na(n))
# initial_pop <- readRDS(here("public_dataset","pop_projection_BC_CA.rds")) %>%
# filter(GEO %in% c('Canada',"British Columbia")) %>%
# filter(REF_DATE >= max(initial_pop_distribution_prop$year)) %>%
# mutate(Projection_scenario = str_remove(Projection_scenario,"Projection scenario "),
# Projection_scenario = str_remove(Projection_scenario, "\\:.*")) %>%
# mutate(sex=str_sub(Sex,1,1),
# age_remove = str_detect(Age_group,'to|over|All|Median|Average'),
# age_keep = Age_group=="100 years and over")
#
# initial_pop_distribution <- initial_pop %>%
# filter(!age_remove | age_keep) %>%
# mutate(age = as.numeric(gsub("([0-9]+).*$","\\1",Age_group)),
# age = ifelse(is.na(age),0,age)) %>%
# rename(n=VALUE,
# projection_scenario = Projection_scenario ) %>%
# select(1,projection_scenario,sex,age,GEO,n) %>%
# rename(year=REF_DATE,
# province=GEO) %>%
# mutate(n=n*1000) %>%
# mutate(province = case_when(province=="Canada" ~ "CA",
# province=="British Columbia" ~ "BC",
# TRUE ~ "NONE")) %>%
# filter(sex!="B") %>%
# filter(!is.na(n))
# note that for province, projections are not available 2044 onwards
initial_pop_distribution %>%
group_by(year,province,projection_scenario) %>%
mutate(total_n = sum(n)) %>%
mutate(prop=n/total_n) -> initial_pop_distribution
initial_pop_distribution_both <- initial_pop_distribution %>%
group_by(year,age,province,projection_scenario) %>%
mutate(n_both=sum(n)) %>%
mutate(prop_male = n/n_both) %>%
ungroup() %>%
filter(sex=='M') %>%
select(year,age,province,prop_male,projection_scenario)
initial_pop_distribution_prop <- initial_pop_distribution %>%
group_by(year,age,province,projection_scenario) %>%
summarise(n=sum(n)) %>%
ungroup() %>%
arrange(year,province,projection_scenario)
initial_pop_distribution_birth_n <- initial_pop_distribution_prop %>%
filter(age==0) %>%
select(-age) %>%
rename(n_birth=n)
initial_pop_distribution_prop %>%
left_join(initial_pop_distribution_birth_n,by=c("year","province","projection_scenario")) %>%
mutate(prop=n/n_birth) %>%
left_join(initial_pop_distribution_both,by=c("year","age","province","projection_scenario"))-> initial_pop_distribution_prop_project
master_initial_pop_distribution_prop <- initial_pop_distribution_prop_past %>%
mutate(projection_scenario="past") %>%
rbind(.,initial_pop_distribution_prop_project %>%
select(year,age,province,n,n_birth,prop,prop_male,projection_scenario))
write_csv(master_initial_pop_distribution_prop,'../src/processed_data/master_initial_pop_distribution_prop.csv')