inst/doc/enquete-ponderee.R

In statAfrikR: Statistical Tools for African National Statistics Institutes

## ----eval=FALSE, setup, include=FALSE-----------------------------------------
# knitr::opts_chunk$set(
#   collapse = TRUE,
#   comment  = "#>",
#   warning  = FALSE,
#   message  = FALSE,
#   fig.width  = 7,
#   fig.height = 5
# )

## ----eval=FALSE, charger------------------------------------------------------
# library(statAfrikR)
# library(dplyr)

## ----eval=FALSE, donnees-simulees---------------------------------------------
# set.seed(2024)
# n <- 5000
# 
# donnees_eds <- tibble::tibble(
#   id_menage      = paste0("MEN_", stringr::str_pad(1:n, 5, pad = "0")),
#   strate         = sample(c("Urbain_Nord", "Urbain_Sud", "Rural_Nord",
#                              "Rural_Sud"), n, replace = TRUE),
#   grappe         = sample(1:250, n, replace = TRUE),
#   poids_final    = runif(n, 0.4, 3.2),
#   region         = sample(c("Alibori", "Atacora", "Atlantique",
#                              "Borgou", "Collines", "Couffo",
#                              "Donga", "Littoral"), n, replace = TRUE),
#   milieu         = sample(c("Urbain", "Rural"), n, replace = TRUE,
#                            prob = c(0.4, 0.6)),
#   age_chef       = sample(25:75, n, replace = TRUE),
#   sexe_chef      = sample(c("Masculin", "Féminin"), n, replace = TRUE,
#                            prob = c(0.75, 0.25)),
#   taille_menage  = sample(1:12, n, replace = TRUE,
#                            prob = c(0.05, 0.1, 0.15, 0.2, 0.18,
#                                     0.12, 0.08, 0.05, 0.03,
#                                     0.02, 0.01, 0.01)),
#   depense_totale = abs(rnorm(n, 850000, 420000)),
#   acces_eau      = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.35, 0.65)),
#   electricite    = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.45, 0.55)),
#   scolarisation  = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.28, 0.72))
# )
# 
# cat("Ménages :", nrow(donnees_eds), "\n")
# cat("Régions :", length(unique(donnees_eds$region)), "\n")

## ----eval=FALSE, validation---------------------------------------------------
# qualite <- valider_qualite_donnees(
#   donnees_eds,
#   vars_cles = "id_menage",
#   seuil_na  = 0.05
# )

## ----eval=FALSE, nettoyage----------------------------------------------------
# # Harmonisation des régions
# donnees_eds <- harmoniser_regions(
#   donnees_eds,
#   var_region = "region",
#   pays       = "BJ",
#   var_sortie = "region_std",
#   signaler_non_trouves = FALSE
# )
# 
# # Journal de traitement
# etape <- tracer_flux_traitement(
#   donnees_eds,
#   "Harmonisation des régions"
# )

## ----eval=FALSE, plan-sondage-------------------------------------------------
# plan <- appliquer_ponderations(
#   data       = donnees_eds,
#   poids = "poids_final",
#   "Harmonisation des régions"
# )

## ----eval=FALSE, plan-sondage-2-----------------------------------------------
# plan <- appliquer_ponderations(
#   data       = donnees_eds,
#   poids = "poids_final",
#   "Harmonisation des régions"
# )

## ----eval=FALSE, plan-sondage-3-----------------------------------------------
# plan <- appliquer_ponderations(
#   data       = donnees_eds,
#   poids = "poids_final",
#   "Harmonisation des régions"
# )

## ----eval=FALSE, plan-sondage-4-----------------------------------------------
# plan <- appliquer_ponderations(
#   data       = donnees_eds,
#   poids = "poids_final",
#   "Harmonisation des régions"
# )

## ----eval=FALSE, plan-sondage-5-----------------------------------------------
# plan <- appliquer_ponderations(
#   data       = donnees_eds,
#   poids = "poids_final",
#   "Harmonisation des régions"
# )

## ----eval=FALSE, plan-sondage-6-----------------------------------------------
# plan <- appliquer_ponderations(
#   data       = donnees_eds,
#   poids = "poids_final",
# plan <- survey::svydesign(
#   ids     = ~grappe,
#   strata  = ~strate,
#   weights = ~poids_final,
#   data    = donnees_eds
# )
# )

## ----eval=FALSE, stats-descriptives-------------------------------------------
# stats <- stat_descr(
#   plan,
#   vars = c("depense_totale", "taille_menage", "age_chef"),
#   ic   = TRUE
# )
# knitr::kable(stats, caption = "Statistiques descriptives pondérées")

## ----eval=FALSE, tableau-croise-----------------------------------------------
# tab <- tab_croisee(
#   plan,
#   var_ligne   = "milieu",
#   var_col     = "region_std",
#   pourcentage = "colonne",
#   format_sortie = "tibble"
# )
# knitr::kable(
#   head(tab, 16),
#   caption = "Répartition par milieu et région (%)"
# )

## ----eval=FALSE, ipm----------------------------------------------------------
# indicateurs_ipm <- list(
#   sante      = c("acces_eau"),
#   education  = c("scolarisation"),
#   niveau_vie = c("electricite")
# )
# 
# resultat_ipm <- calcul_ipm(
#   donnees_eds,
#   indicateurs_ipm,
#   seuil_k = 1/3,
#   poids = "poids_final"
# )

## ----eval=FALSE, inegalite----------------------------------------------------
# inegalites <- decomposer_inegalite(
#   donnees_eds,
#   var_revenu = "depense_totale",
#   var_groupe = "milieu",
#   poids = "poids_final"
# )
# 
# cat("Indice de Gini :", inegalites$gini, "\n")
# knitr::kable(
#   inegalites$decomposition,
#   caption = "Décomposition des inégalités par milieu"
# )

## ----eval=FALSE, pyramide, fig.cap="Pyramide des âges des chefs de ménage"----
# library(ggplot2)
# pyramide_ages(
#   donnees_eds,
#   var_age   = "age_chef",
#   var_sexe  = "sexe_chef",
#   poids = "poids_final",
#   titre     = "Pyramide des âges — Chefs de ménage",
#   largeur_classe = 10L
# )

## ----eval=FALSE, barres, fig.cap="Dépense moyenne par région"-----------------
# stats_region <- stat_descr(
#   donnees_eds,
#   vars   = "depense_totale",
#   groupe = "region_std",
#   ic     = TRUE
# )
# 
# graphique_barres(
#   stats_region,
#   var_x       = "region_std",
#   var_y       = "moyenne",
#   var_ic_bas  = "ic_bas",
#   var_ic_haut = "ic_haut",
#   titre       = "Dépense moyenne par région (FCFA)",
#   label_y     = "Dépense moyenne (FCFA)",
#   trier       = TRUE
# ) + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1))

## ----eval=FALSE, regression---------------------------------------------------
# # Déterminants de la dépense
# modele <- analyse_regression(
#   log(depense_totale) ~ age_chef + taille_menage + electricite + acces_eau,
#   data          = donnees_eds,
#   type          = "lineaire",
#   format_sortie = "tibble"
# )
# 
# knitr::kable(
#   modele[, c("terme", "estimateur", "ic_bas", "ic_haut",
#              "p_valeur", "significatif")],
#   caption = "Déterminants de la dépense des ménages"
# )

## ----eval=FALSE, diffusion, eval=FALSE----------------------------------------
# # Anonymisation
# donnees_anon <- anonymiser_donnees(
#   donnees_eds,
#   vars_supprimer   = c("id_menage"),
#   vars_generaliser = list(age_chef = 10, depense_totale = 100000),
#   rapport          = FALSE
# )
# 
# # Métadonnées DDI
# generer_metadonnees_ddi(
#   data           = donnees_anon,
#   titre          = "Enquête Démographique et de Santé — 2024",
#   pays           = "Bénin",
#   annee          = 2024,
#   institution    = "INSAE",
#   fichier_sortie = "outputs/eds_2024_ddi.xml"
# )
# 
# # Package de diffusion
# compresser_package_diffusion(
#   donnees           = donnees_anon,
#   repertoire_sortie = "diffusion/",
#   nom_package       = "EDS_BEN_2024_v1"
# )