timriffe

# Idea: use wpp2022 or wpp2024 data
# 1) select year whose pop we want to adjust (2000)
# 2) get mort for 10 prior years
# 3) in cohort lines, get Lx, reverse-Sx, reverse cumprod of reverse-Sx
# 4) inflate year 2000 pop to fill Lexis space 1990-1999
# 5) calculate exposures by age and year 1990-1999, ages 15-49

# TODO: 1. merge with asfr, get births, use to get Bt series.
#       2. use same general idea to forward-survive Bt to Jan 1 2000, giving adjusted pop ages 0-9
#       3. make adjustments for mid-year census, in case given.

timriffe

# note, our citation lookup needs an openalex paper id
# but you probably only have the doi. This does the translation
# this returns a full url, where the id is the last part
get_openalex_id_from_doi <- function(doi, email) {
  base_url <- "https://api.openalex.org/works/doi:"
  doi_encoded <- URLencode(doi, reserved = TRUE)
  
  url <- paste0(base_url, doi_encoded, "?mailto=",email)
  
  res <- httr::GET(url, httr::user_agent(email))

timriffe

# See also https://gist.github.com/timriffe/f820143e939f9262b3083122e1ce49ae
# for semantic scholar. Sorry output isn't harmonized

get_citing_papers_opencite <- function(doi){
  openciteurl <- paste0( "https://opencitations.net/index/coci/api/v1/citations/",doi )
  result      <- rjson::fromJSON(file = openciteurl)
  result |> lapply(as_tibble) |> bind_rows() 
}
get_second_order_citations_opencite <- function(doi){
  citing <- get_citing_papers_opencite(doi)

timriffe

# get the semantic scholar method
library(httr)
library(jsonlite)
library(dplyr)
get_citing_papers <- function(doi, page_size = 100, max_results = 10000) {
  base_url <- paste0("https://api.semanticscholar.org/graph/v1/paper/DOI:",
                     URLencode(doi, reserved = TRUE),
                     "/citations")
  fields <- "citingPaper.paperId,citingPaper.title,citingPaper.citationCount"
  

timriffe

# download Excel from here:
# https://docs.google.com/spreadsheets/d/1zJPZ9uu6NpYruV8utJ-Cby4BGMpHw4XE/edit?usp=sharing&ouid=107044799582363262397&rtpof=true&sd=true

library(tidyverse)
library(readxl)
library(colorspace)
dat <- read_excel("Data/graficos_prev_ratio_antia_yolanda.xlsx", sheet = "table") 

p <-
  dat %>% 

timriffe

library(scholar)
library(googlesheets4)
library(tidyverse)
gs4_auth(email = "[email protected]")
authors <- read_sheet(ss = "https://docs.google.com/spreadsheets/d/18RWKaMvkUOGp_URLwRu-3jTLUz2iI4y3oN_hu8MLoRg/edit#gid=0")
authors
pubsi <- list()
for (i in 1:nrow(authors)){
  pubsi[[i]] <- get_publications(id = authors$scholar_id[i],

timriffe

library(tidyverse)
library(rvest)
library(httr)
options(timeout = 1e6)
cdc_url <- "https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm"
pg <- read_html(cdc_url)


test <- html_attr(html_nodes(pg, "a"), "href")

timriffe

library(HMDHFDplus)
library(tidyverse)
library(data.table)
# read and reshape births by year, age, order
Bxi <- readHFDweb("SWE","birthsRRbo", 
                  username = Sys.getenv("us"), 
                  password = Sys.getenv("pw"))|> 
  select(-OpenInterval, -Total) |> 
  pivot_longer(B1:B5p, names_to = "order", values_to = "Bxi") |> 

timriffe

timriffe

library(tidyverse)
library(HMDHFDplus)
fert <- readHFDweb("USA",
                   "asfrRR", 
                   username = Sys.getenv("us"), 
                   password = Sys.getenv("pw"))

# ASFR as function of rates:
# decomposition result is just the age-specific rate differences
fert |>