timriffe

# these come from here: https://www.who.int/data/data-collection-tools/who-mortality-database
icd_base_url <-"https://cdn.who.int/media/docs/default-source/world-health-data-platform/mortality-raw-data/"
icd_files    <- c("mort_country_codes.zip","morticd10_part1.zip","morticd10_part2.zip",
                  "morticd10_part3.zip","morticd10_part4.zip","morticd10_part5.zip")
for (i in 1:length(icd_files)){
  url_i   <- paste0(icd_base_url,icd_files[i])
  local_i <- file.path(icd_files[i])
  download.file(url_i, destfile = local_i, overwrite = TRUE)
}

timriffe

library(tidyverse)
library(readxl)
library(zoo)


IN <- read_excel("/home/tim/Data/grafico rp ambiental.xlsx", 
           sheet = 2,
           range = "B6:F71") %>% 
  mutate(Variable = na.locf(Variable)) %>% 

timriffe

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


p <-
  dat %>% 
  ggplot(aes(y = origen, 
             x = Valor, 

timriffe

library(tidyverse)
library(readxl)

IN <- read_excel("RAZONES DE PREVALENCIA.xlsx")[-c(1,2),-c(1,4,6,8)]

colnames(IN) <- c("Grupo","TABACO EN GENERAL_Hombres","TABACO EN GENERAL_Mujeres", "TABACO DE LIAR_Hombres","TABACO DE LIAR_Mujeres")
IN$Grupo[1] <- "General"

redux <- 
  IN %>% 

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library(eurostat)
library(tidyverse)
library(countrycode)
IN <- get_eurostat("demo_r_mwk_05")

db_eurs <-
  IN %>% 
  mutate(time = as.character(time)) %>% 
  separate(time, sep = "W", into = c("year","week"), convert = TRUE) %>% 
  dplyr::filter(year >= 2016, 

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conditions <- structure(list(idno = c(1, 2, 3, 4, 5), cvd = c(1, 1, 0, 1, 1
), depression = c(0, 1, 1, 0, 0), diabetes = c(0, 1, 0, 1, 1)), class = "data.frame", row.names = c(NA, 
                                                                                                    -5L))

data <- conditions
RS_adj_mat <- function(data, the_conditions = c("cvd","depression","diabetes")){

  stopifnot(all(the_conditions %in% colnames(data)))
  

timriffe

library(tidyverse)
library(readxl)
library(colorspace)

# convertir a formato tidy y crear coordinatas centricas
ISF_tidy <-
  read_excel("ISF_PC.xlsx", sheet = "simplified") %>% 
  pivot_longer(`2015-2011`:`1930-1926`, names_to = "YOB", values_to = "ASFR") %>% 
  filter(!is.na(ASFR)) %>% 

timriffe

library(tidyverse)

# example: uses Ohio, March 1 (dunno why),
# and population is actually 2018, because I can't find 2020 pops yet.
# slightly less of a problem because I grouped to 5-year intervals.

X <-tibble(Sex = c(rep("f",21),rep("m",21)), 
       Age = c(seq(0,100,by=5),seq(0,100,by=5)), 
       Deaths = c(0.9, 1.5, 2.5, 4, 3.4, 5.6, 14.2, 24.8, 35.1, 63.9, 
                  111.9, 213.5, 363.8, 566.4, 811.4, 1076.6, 1271.3, 1012.5, 1990.5, 

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rm(list = ls())
getwd()
load("Data/Smoothing.RData")

library(MortalitySmooth)
library(ungroup)

# ?Mort2Dsmooth

x <- c(0,1,seq(5,85,by=5))

timriffe

library(DemoTools)
calc_stable <- function(nLx, asfr, Age_nLx, Age_asfr){
  
  asfr_vec        <- rep(0,length(Age_nLx))
  names(asfr_vec) <- Age_nLx
  asfr_vec[as.character(Age_asfr)] <- asfr
  
  int <- age2int(Age_nLx)
  int[length(int)] <- int[(length(int)-1)]
  x <- Age_nLx + int / 2