rm(list = ls())
setwd("/home/onyxia/formation-bonnes-pratiques-R")

if (!require('ggplot2')) install.packages('ggplot2')
if (!require('stringr')) install.packages('stringr')
if (!require('dplyr')) install.packages('dplyr')
if (!require('tidyverse')) install.packages('tidyverse')


library(tidyverse)

# j'importe les données avec read_csv2 parce que c'est un csv avec des ; et que read_csv attend comme separateur des , 
df <- readr::read_csv(
  "RPindividus_24.csv", col_select =c(
    "REGION", "AGED", "ANAI", "CATL", "COUPLE",
    "SEXE", "SURF", "TP", "TRANS", "IPONDI"
  )
)


df |> group_by(AGED) |> summarise(n = sum(IPONDI))

decennie_a_partir_annee    = function(ANNEE){ return(ANNEE - ANNEE %%
                                                       10) }

ggplot(df) + geom_histogram(aes(x = 5*floor(as.numeric(AGED)/5), weight = IPONDI), stat = "count")

# correction (qu'il faudra retirer)
# ggplot(
#   df |> group_by(aged, sexe) |> summarise(SH_sexe = n()) |> group_by(aged) |> mutate(SH_sexe = SH_sexe/sum(SH_sexe)) |> filter(sexe==1)
# ) + geom_bar(aes(x = as.numeric(aged), y = SH_sexe), stat="identity") + geom_point(aes(x = as.numeric(aged), y = SH_sexe), stat="identity", color = "red") + coord_cartesian(c(0,100))

# stats trans par statut
df3 = df |> group_by(COUPLE, TRANS) |> summarise(x = sum(IPONDI)) |> group_by(COUPLE) |> mutate(y = 100*x/sum(x))

library(forcats)
df <- df |> mutate(SEXE = as.character(SEXE)) |>
  mutate(SEXE = fct_recode(SEXE, Homme = "1", Femme = "2"))

p <- # part d'homme dans chaque cohort
  df |> 
  group_by(AGED, SEXE) |> 
  summarise(SH_sexe = sum(IPONDI)) |> 
  group_by(AGED) |> 
  mutate(SH_sexe = SH_sexe/sum(SH_sexe)) |> 
  filter(SEXE=="Homme") |>
  ggplot() + geom_bar(aes(x = AGED, y = SH_sexe), stat="identity") + geom_point(aes(x = AGED, y = SH_sexe), stat="identity", color = "red") + coord_cartesian(c(0,100))


ggsave("p.png", p)


#fonction de stat agregee
fonction_de_stat_agregee<-function(a,b="moyenne",...){
  if (b=="moyenne"){
    x=mean(a, na.rm = T,...)
  } else if (b=="ecart-type" | b == "sd"){
    x = sd(a, na.rm = T, ...)
  } else if (b=="variance"){
    x = var(a, na.rm = T, ...)
  }
  return(x)
}

fonction_de_stat_agregee(rnorm(10))
fonction_de_stat_agregee(rnorm(10), "ecart-type")
fonction_de_stat_agregee(rnorm(10), "variance")

fonction_de_stat_agregee(df |> filter(SEXE == "Homme") |> pull(AGED))
fonction_de_stat_agregee(df |> filter(SEXE == "Femme") |> pull(AGED))

api_token <- "trotskitueleski$1917"

# modelisation
# library(MASS)
df3=df|>filter(SURF!="Z")|>sample_n(1000)
df3 <- df3 |> mutate(SURF = factor(SURF, ordered=T))
df3 |> 
  filter(COUPLE == 2 & between(AGED, 40, 60))
polr(SURF ~ factor(COUPLE) + factor(TP), df3)