label.R

rm(list = ls())
library(tidyverse)
library(readxl)
library(fs)
library(magrittr)

##
##copy about
##

# dir_delete("./meta")
# dir_copy(path = "C:\\Users\\Guy\\Dropbox\\WCDE for Guy/meta/",
#          new_path = "./meta")
# dir_delete("./md")
# dir_copy(path = "C:\\Users\\Guy\\Dropbox\\WCDE for Guy/md/",
#          new_path = "./md")
# dir_ls(path = "md/") %>%
#   as_tibble()
  
##
##Indicators
##

ind <- read_excel("./meta/indicator.xlsx")

ind0 <- ind %>%
  distinct(type1, type) %>%
  deframe() %>%
  as.list()

# note the %$% not %>% before split()
ind1 <- ind %>%
  filter(type == "phcs") %>%
  distinct(fullname, type2) %>%
  mutate(type2 = fct_rev(type2)) %$%
  split(fullname, type2)

ind2 <- ind %>%
  filter(type == "demo") %>%
  distinct(fullname, type2) %>%
  mutate(type2 = fct_inorder(type2)) %$%
  split(fullname, type2)
ind2[[2]] <- ind2[[2]][c(1:3,6,4:5,7)]
ind2[[4]] <- ind2[[4]][c(1,4,2:3)]
ind2$Migration <- c(ind2$Migration, " ")


ind3 <- ind %>%
  filter(type == "si") %>%
  distinct(fullname, type2) %>%
  mutate(type2 = fct_inorder(type2)) %$%
  split(fullname, type2)


ind4 <- c(ind1, ind2, ind3)

# limits for labels
dim_avail <- read_csv("../wcde-data/data/dim.csv") %>%
  filter(v == "wcde-v3") %>%
  filter(!is.na(label)) %>%
  distinct(i, dim, label, code) %>%
  arrange(i, dim, code) %>%
  left_join(ind[,1:2], by = c("i" = "name"))


##
##geography
##
geog <- read_excel("./meta/geography.xlsx") %>%
  as_tibble() %>%
  replace_na(list(edu8 = 0)) %>%
  mutate(isono = as.integer(isono),
         ggarea = ifelse(
           str_detect(string = ggarea, pattern = "[:digit:]"),
           str_pad(
             string = ggarea,
             width = 3,
             pad = "0"
           ),
           ggarea
         ))

an1 <- geog %>%
  filter(dim == "area") %>%
  pull(name)

rn1 <- geog %>%
  filter(dim == "region") %>%
  pull(name)

nn1 <- geog %>%
  filter(dim == "country") %>%
  pull(name)

nn2 <- geog %>%
  filter(dim == "country") %>%
  select(continent, name) %$%
  split(name, continent)

geo1 <- c(list("World", "Continent", "Region"), nn2)
names(geo1)[2:3] <- geo1[2:3]
geo1[[2]] <- an1[-1]
geo1[[3]] <- rn1

geo2 <- list("World", "Continent", "Region")
names(geo2) <- geo2
geo2[[2]] <- an1[-1]
geo2[[3]] <- geog %>%
  filter(dim == "region") %>%
  pull(name)

geo3 <- geo1
geo3[[3]] <- NULL

nn3 <- geog %>%
  filter(dim == "country",
         is185 == 1) %>%
  select(continent, name) %$%
  split(.$name, .$continent)

geo3 <- c(list("World", "Continent"), nn3)
names(geo3)[2] <- geo3[2]
geo3[[2]] <- an1[-1]

geo4 <- c(an1, rn1)[str_detect(c(an1, rn1), "Europe")]





##
## dimensions
##
dimen <- read_excel("./meta/dimension.xlsx")

sn1 <- dimen %>%
  filter(dim == "scenario") %>%
  arrange(wcde_code) %>%
  arrange(wcde_code != 2) %>%
  mutate(name = fct_inorder(name)) %>%
  select(wcde_code, name) %$%
  split(wcde_code, name)


yn1 <- seq(2020, 2100, 5)
yn2 <- as.list(yn1)[-length(yn1)]
names(yn2) <-
  paste0(yn1[-length(yn1)], "-", substr(yn1[-length(yn1)] + 5, 3, 4))

yn3 <- seq(1950, 2100, 5)
yn4 <- as.list(yn3)[-length(yn3)]
names(yn4) <-
  paste0(yn3[-length(yn3)], "-", substr(yn3[-length(yn3)] + 5, 3, 4))

age1 <- dimen %>%
  filter(dim == "age") %>%
  mutate(name = fct_inorder(name)) %$%
  split(code, name)

sex1 <- dimen %>%
  filter(dim == "sex") %>%
  mutate(name = fct_inorder(name)) %$%
  split(code, name)

edu1 <- edu2 <- dimen %>%
  filter(dim == "edu") %>%
  mutate(name = fct_inorder(name)) %$%
  split(code, name)

# these might need revising for v3... no longer 8 edu groups
edu2[4] <- edu2[5]
edu2[6] <- edu2[7]

names(edu2)[4] <- names(edu2)[5]
names(edu2)[7] <- gsub("Upper ", "", names(edu2)[7])
names(edu2)[6] <- names(edu2)[7]


edu4 <- edu1[c(1, 2, 4, 6, 7) + 1]
edu4[[3]] <- 3:4
edu4[[4]] <- 5:6
edu4[[5]] <- 7:10
names(edu4) <-
  str_remove_all(string = names(edu4), pattern = "Upper ")
edu4 <- c(edu1[1], edu4)

edu4 <- edu4 %>%
  map_df( ~ data_frame(eduno = .x), .id = "edu_name")

edu6 <- edu1[c(1:7) + 1]
edu6[[7]] <- 7:10
edu6 <- c(edu1[1], edu6)

edu6 <- edu6 %>%
  map_df( ~ data_frame(eduno = .x), .id = "edu_name")

edu10 <- edu1 %>%
  map_df( ~ data_frame(eduno = .x), .id = "edu_name")
# mutate(eduno = ifelse(edu_name == "Post Secondary", NA, eduno))


age2 <- list(
  "Five Year Age Groups" = dimen %>%
    filter(dim == "age") %>%
    pull(name),
  "Other Age Groups" = dimen %>%
    filter(dim == "bage") %>%
    pull(name)
)

age3 <- list(
  "Five Year Age Groups" = dimen %>%
    filter(dim == "age") %>%
    pull(name),
  "Other Age Groups" = dimen %>%
    filter(dim == "sage") %>%
    pull(name)
)

sage1 <- dimen %>%
  filter(dim == "sage") %>%
  mutate(name = fct_inorder(name)) %$%
  split(code, name)

bage1 <- dimen %>%
  filter(dim == "bage") %>%
  mutate(name = fct_inorder(name)) %$%
  split(code, name)

# age3<-as.list(0:9)
# names(age3) <- subset(dimen, dim=="bage")$name


##
##assumptions
##
# assump <- read_excel("./meta/assumption.xlsx")
assump <- read_csv("./meta/assumption.csv")
assump <- assump %>%
  mutate(sno = case_when(
    str_detect(scenario, "SSP1") ~ 1,
    str_detect(scenario, "SSP2") ~ 2,
    str_detect(scenario, "SSP3") ~ 3,
    str_detect(scenario, "SSP4") ~ 4,
    str_detect(scenario, "SSP5") ~ 5
  ))
#assump$country<-NULL
# assump<-assump %>% left_join(geog %>% select(name,isono))
# assump<-assump %>% rename(country=name)
head(assump)



##
##info
##
csvinfo <-
  "Source: Wittgenstein Centre for Demography and Global Human Capital (2023). Wittgenstein Centre Data Explorer Version 3.0
Available at: www.wittgensteincentre.org/dataexplorer \n\n"

pdfinfo <-
  "<br> Source: Wittgenstein Centre for Demography and Global Human Capital, (2023). <i> Wittgenstein Centre Data Explorer Version 3.0</i> <br>
Available at: <a href='www.wittgensteincentre.org/dataexplorer'>www.wittgensteincentre.org/dataexplorer</a> <br><br>"

iiasa4 <- paste0(
  "['lightgrey','",
  rgb(202, 0, 32, max = 255),
  "','",
  rgb(244, 165, 130, max = 255),
  "','",
  rgb(146, 197, 222, max = 255),
  "','",
  rgb(5, 113, 176, max = 255),
  "']"
)

iiasa6 <- paste0(
  "['lightgrey','",
  rgb(178, 24, 43, max = 255),
  "','",
  rgb(239, 138, 98, max = 255),
  "','",
  rgb(253, 219, 199, max = 255),
  "','",
  rgb(209, 229, 240, max = 255),
  "','",
  rgb(103, 169, 207, max = 255),
  "','",
  rgb(33, 102, 172, max = 255),
  "']"
)

iiasa6a <- paste0(
  "['lightgrey','",
  rgb(178, 24, 43, max = 255),
  "','",
  rgb(239, 138, 98, max = 255),
  "','",
  rgb(253, 219, 199, max = 255),
  "','"
)

iiasa6b <- paste0(
  "['",
  rgb(209, 229, 240, max = 255),
  "','",
  rgb(103, 169, 207, max = 255),
  "','",
  rgb(33, 102, 172, max = 255),
  "']"
)


iiasa8 <- paste0(
  "['lightgrey','",
  rgb(178, 24, 43, max = 255),
  "','",
  rgb(214, 96, 77, max = 255),
  "','",
  rgb(244, 165, 130, max = 255),
  "','",
  rgb(253, 219, 199, max = 255),
  "','",
  rgb(209, 229, 240, max = 255),
  "','",
  rgb(146, 197, 222, max = 255),
  "','",
  rgb(67, 147, 195, max = 255),
  "','",
  rgb(33, 102, 172, max = 255),
  "']"
)

iiasa8a <- paste0(
  "['lightgrey','",
  rgb(178, 24, 43, max = 255),
  "','",
  rgb(214, 96, 77, max = 255),
  "','",
  rgb(244, 165, 130, max = 255),
  "','",
  rgb(253, 219, 199, max = 255),
  "']"
)


iiasa8b <- paste0(
  "['",
  rgb(209, 229, 240, max = 255),
  "','",
  rgb(146, 197, 222, max = 255),
  "','",
  rgb(67, 147, 195, max = 255),
  "','",
  rgb(33, 102, 172, max = 255),
  "']"
)

# iiasa4<-paste0("['lightgrey','",
#                  rgb(192,0,0, max=255),"','",
#                  rgb(255,192,0, max=255),"','",
#                  rgb(0,102,255, max=255),"','",
#                  rgb(0,0,153, max=255),"']")
#
# iiasa6<-paste0("['lightgrey','",
#                   rgb(192,0,0, max=255),"','",
#                   rgb(255,102,0, max=255),"','",
#                   rgb(255,192,0, max=255),"','",
#                   rgb(153,204,255, max=255),"','",
#                   rgb(0,102,255, max=255),"','",
#                   rgb(0,0,153, max=255),"']")

##
##faq
##
faq <- read_excel("./meta/faq.xlsx")

##
##saving
##
save.image(file = "./label.RData")


##
## check pdf names
##
# fn <- list.files("./pdf") %>%
#   str_remove_all(pattern = ".pdf")
# fn[!fn %in% geog$name]
# fn[!fn %in% geog$name]