inst/doc/indicateurs-odd.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)
# library(ggplot2)

## ----eval=FALSE, idh-benin----------------------------------------------------
# idh_benin <- calcul_idh(
#   esperance_vie   = 61.8,
#   annees_scol_moy = 4.5,
#   annees_scol_att = 13.1,
#   rnb_habitant    = 3360,
#   annee           = 2023
# )
# 
# cat("IDH Bénin 2023 :\n")
# cat("  IDH global    :", idh_benin$idh, "\n")
# cat("  Santé         :", idh_benin$indice_sante, "\n")
# cat("  Éducation     :", idh_benin$indice_education, "\n")
# cat("  Revenu        :", idh_benin$indice_revenu, "\n")
# cat("  Catégorie     :", idh_benin$categorie, "\n")

## ----eval=FALSE, idh-comparaison----------------------------------------------
# pays_afrique <- tibble::tibble(
#   pays            = c("Bénin", "Burkina Faso", "Sénégal",
#                        "Côte d'Ivoire", "Mali", "Niger",
#                        "Togo", "Guinée"),
#   esperance_vie   = c(61.8, 61.6, 68.7, 59.1, 59.3, 62.4, 61.5, 58.9),
#   annees_scol_moy = c(4.5,  2.0,  3.5,  5.3,  2.4,  2.1,  5.5,  3.1),
#   annees_scol_att = c(13.1, 9.3, 14.5, 12.1, 7.5,  9.9, 13.1, 11.2),
#   rnb_habitant    = c(3360, 2310, 3690, 5510, 2200, 1340, 2780, 2420)
# )
# 
# idh_regional <- pays_afrique |>
#   dplyr::rowwise() |>
#   dplyr::mutate(
#     res           = list(calcul_idh(esperance_vie, annees_scol_moy,
#                                     annees_scol_att, rnb_habitant)),
#     idh           = res$idh,
#     indice_sante  = res$indice_sante,
#     indice_educ   = res$indice_education,
#     indice_revenu = res$indice_revenu,
#     categorie     = res$categorie
#   ) |>
#   dplyr::select(-res) |>
#   dplyr::ungroup() |>
#   dplyr::arrange(dplyr::desc(idh))
# 
# knitr::kable(
#   idh_regional[, c("pays", "idh", "categorie",
#                     "indice_sante", "indice_educ", "indice_revenu")],
#   caption = "IDH — Comparaison régionale Afrique de l'Ouest",
#   digits  = 3
# )

## ----eval=FALSE, idh-graphique, fig.cap="IDH et composantes — Afrique de l'Ouest"----
# idh_long <- idh_regional |>
#   tidyr::pivot_longer(
#     cols      = c(indice_sante, indice_educ, indice_revenu),
#     names_to  = "composante",
#     values_to = "valeur"
#   ) |>
#   dplyr::mutate(
#     composante = dplyr::recode(composante,
#       indice_sante  = "Santé",
#       indice_educ   = "Éducation",
#       indice_revenu = "Revenu"
#     ),
#     pays = forcats::fct_reorder(pays, idh)
#   )
# 
# graphique_barres(
#   idh_long,
#   var_x      = "pays",
#   var_y      = "valeur",
#   var_groupe = "composante",
#   position   = "stack",
#   titre      = "Composantes de l'IDH — Afrique de l'Ouest",
#   label_y    = "Valeur de l'indice"
# ) + ggplot2::coord_flip()

## ----eval=FALSE, donnees-ipm--------------------------------------------------
# set.seed(2024)
# n <- 3000
# donnees_menages <- tibble::tibble(
#   id_menage       = 1:n,
#   region          = sample(c("Nord", "Sud", "Est", "Ouest"), n, replace = TRUE),
#   milieu          = sample(c("Urbain", "Rural"), n, replace = TRUE,
#                             prob = c(0.35, 0.65)),
#   poids           = runif(n, 0.5, 2.5),
#   # Dimension Santé
#   malnutrition    = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.72, 0.28)),
#   mortalite_enf   = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.88, 0.12)),
#   # Dimension Éducation
#   scol_adulte     = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.62, 0.38)),
#   scol_enfants    = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.71, 0.29)),
#   # Dimension Niveau de vie
#   electricite     = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.52, 0.48)),
#   eau_potable     = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.58, 0.42)),
#   assainissement  = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.55, 0.45)),
#   combustible     = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.60, 0.40)),
#   logement        = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.75, 0.25)),
#   actifs          = sample(c(0L, 1L), n, replace = TRUE, prob = c(0.68, 0.32))
# )

## ----eval=FALSE, calcul-ipm---------------------------------------------------
# # Définition des dimensions IPM standard OPHI
# indicateurs_ipm <- list(
#   sante      = c("malnutrition", "mortalite_enf"),
#   education  = c("scol_adulte", "scol_enfants"),
#   niveau_vie = c("electricite", "eau_potable", "assainissement",
#                   "combustible", "logement", "actifs")
# )
# 
# resultat_ipm <- calcul_ipm(
#   donnees_menages,
#   var_nutrition      = 'malnutrition',
#   var_mortalite_inf  = 'mortalite_enf',
#   var_scolarisation  = 'scol_enfants',
#   var_annees_scol    = 'scol_adulte',
#   var_electricite    = 'electricite',
#   var_eau            = 'eau_potable',
#   var_assainissement = 'assainissement',
#   var_combustible    = 'combustible',
#   var_logement       = 'logement',
#   var_actifs         = 'actifs',
#   poids   = 'poids',
#   seuil_k = 1/3
# )

## ----eval=FALSE, ipm-regional-------------------------------------------------
# donnees_enrichies <- resultat_ipm$donnees_enrichies
# 
# ipm_region <- donnees_enrichies |>
#   dplyr::group_by(region) |>
#   dplyr::summarise(
#     n_menages        = dplyr::n(),
#     taux_pauvrete_md = round(mean(.est_pauvre_multi) * 100, 1),
#     score_moyen      = round(mean(.score_privation), 3),
#     .groups          = "drop"
#   ) |>
#   dplyr::arrange(dplyr::desc(taux_pauvrete_md))
# 
# knitr::kable(
#   ipm_region,
#   caption = "Pauvreté multidimensionnelle par région",
#   col.names = c("Région", "Ménages", "Taux pauvreté (%)", "Score moyen")
# )

## ----eval=FALSE, inegalite-simulation-----------------------------------------
# set.seed(2024)
# donnees_revenus <- tibble::tibble(
#   menage         = 1:2000,
#   milieu         = sample(c("Urbain", "Rural"), 2000, replace = TRUE,
#                            prob = c(0.4, 0.6)),
#   quintile       = sample(1:5, 2000, replace = TRUE),
#   depense_totale = exp(rnorm(2000, log(500000), 0.8)),
#   poids          = runif(2000, 0.5, 2.0)
# )

## ----eval=FALSE, calcul-inegalite---------------------------------------------
# inegalites <- decomposer_inegalite(
#   donnees_revenus,
#   var_revenu = "depense_totale",
#   var_groupe = "milieu",
#   poids = "poids"
# )

## ----eval=FALSE, inegalite-tableau--------------------------------------------
# knitr::kable(
#   inegalites$decomposition,
#   caption = "Décomposition des inégalités par milieu",
#   digits  = 3
# )

## ----eval=FALSE, courbe-lorenz, fig.cap="Courbe de Lorenz — Dépenses des ménages"----
# # Construction manuelle de la courbe de Lorenz
# x_sorted <- sort(donnees_revenus$depense_totale)
# n         <- length(x_sorted)
# lorenz_df <- tibble::tibble(
#   pop_cumulee = c(0, seq_len(n) / n),
#   rev_cumulee = c(0, cumsum(x_sorted) / sum(x_sorted))
# )
# 
# ggplot2::ggplot(lorenz_df, ggplot2::aes(pop_cumulee, rev_cumulee)) +
#   ggplot2::geom_line(color = "#1B6CA8", linewidth = 1.2) +
#   ggplot2::geom_abline(intercept = 0, slope = 1,
#                         linetype = "dashed", color = "#888888") +
#   ggplot2::annotate("text", x = 0.7, y = 0.35,
#                      label = paste0("Gini = ", inegalites$gini),
#                      color = "#1B6CA8", fontface = "bold", size = 4) +
#   ggplot2::labs(
#     title = "Courbe de Lorenz — Dépenses des ménages",
#     x     = "Part cumulée de la population",
#     y     = "Part cumulée des dépenses"
#   ) +
#   theme_ins()

## ----eval=FALSE, synthese-----------------------------------------------------
# tibble::tibble(
#   Indicateur = c("IDH Bénin 2023", "IPM (H × A)", "Incidence (H)",
#                   "Intensité (A)", "Gini"),
#   Valeur = c(
#     idh_benin$idh,
#     resultat_ipm$ipm,
#     round(resultat_ipm$H, 3),
#     round(resultat_ipm$A, 3),
#     inegalites$gini
#   ),
#   Interpretation = c(
#     idh_benin$categorie,
#     paste0(round(resultat_ipm$ipm * 100, 1), "% de pauvreté multidim."),
#     paste0(round(resultat_ipm$H * 100, 1), "% de ménages pauvres"),
#     paste0(round(resultat_ipm$A * 100, 1), "% de privations en moyenne"),
#     dplyr::case_when(
#       inegalites$gini < 0.3 ~ "Inégalités faibles",
#       inegalites$gini < 0.4 ~ "Inégalités modérées",
#       TRUE                  ~ "Inégalités élevées"
#     )
#   )
# ) |>
# knitr::kable(caption = "Tableau de bord des indicateurs ODD")