script.R

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_csv2(
  "individu_reg.csv",
  col_select = c("region", "aemm", "aged", "anai", "catl", "cs1", "cs2", "cs3", "couple", "na38", "naf08", "pnai12", "sexe", "surf", "tp", "trans", "ur")
)

df <- df %>%
  mutate(aged = as.numeric(aged))

summarise(group_by(df, aged), n())

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

ggplot(df) +
  geom_histogram(aes(x = 5 * floor(as.numeric(aged) / 5)), 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 = n()) %>%
  group_by(couple) %>%
  mutate(y = 100 * x / sum(x))

p <- # part d'homme dans chaque cohort
  df %>%
  group_by(aged, sexe) %>%
  summarise(SH_sexe = n()) %>%
  group_by(aged) %>%
  mutate(SH_sexe = SH_sexe / sum(SH_sexe)) %>%
  filter(sexe == 1) %>%
  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)

library(forcats)
df$sexe <- df$sexe %>%
  as.character() %>%
  fct_recode(Homme = "1", Femme = "2")

# 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 %>%
  dplyr::select(surf, cs1, ur, couple, aged) %>%
  filter(surf != "Z")
df3[, 1] <- factor(df3$surf, ordered = T)
df3[, "cs1"] <- factor(df3$cs1)
df3 %>%
  filter(couple == "2" & aged > 40 & aged < 60)
polr(surf ~ cs1 + factor(ur), df3)