Compare revisions

Juliette Engelaere-Lefebvre · Juliette Engelaere-Lefebvre · Daniel.Kalioudjoglou · Daniel.Kalioudjoglou · Daniel.Kalioudjoglou · Daniel.Kalioudjoglou
--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,5 @@
 .Ruserdata
 extdata/
 data/
+data-raw/*.xls
+data-raw/*.csv
--- a/data-raw/data-raw-ecln-sas-R.R
+++ b/data-raw/data-raw-ecln-sas-R.R
-## code to prepare `data-raw-ecln-sas.R` dataset goes here

 library(magrittr)
 library(haven)
 library(readxl)
 library(lubridate)
-library(conflicted)
-# devtool::install_github("MaelTheuliere/COGiter")
 library(COGiter)
 library(purrr)
 library(dplyr)
@@ -13,58 +10,61 @@ library(tidyr)
 library(forcats)
 library(lubridate)
 library(stringr)
-library(glue)
 library(dtplyr)
 options(scipen = 999)
-conflict_prefer("filter", "dplyr")
-conflict_prefer("lag", "dplyr")
-conflict_prefer("union", "dplyr")
-conflict_prefer("setdiff", "dplyr")
-source("R/FormatDate.R")

-trimestre <- fs::dir_ls(path = "extdata", type = "directory") %>%
-  stringr::str_replace_all("extdata/", "") %>%
+# Recherche du dernier millesime disponible
+fichiers <- list.files("X:/SCTE/DEP/Donnees_confidentielles/Conjoncture/ECLN/Publication"
+,include.dirs = FALSE,full.names = FALSE, pattern = "stat_info")
+# fichiers <- list.files('data-raw',include.dirs = FALSE,full.names = FALSE, pattern = "stat_info")
+millesime <- fichiers %>%
+  substr(26,31) %>%
  max()
-trimestre_date <- FormatDate(trimestre, Periodicite = "Trim", sep = "T")
-
-trimestre_min <- FormatCaractere(trimestre_date - years(5) - months(3))
-file_sas <- glue("extdata/{trimestre}/stat_info_data_regionaux.sas7bdat")
-file_redressement <- glue("extdata/{trimestre}/{trimestre}rd1-commercialisation-lgts-neufs.xls")
-
-ECLN <- read_sas(file_sas)
-# Rajout du taux de redressement sur les mises en vente et le stoc --------
-valeur_redressees <- excel_sheets(path = file_redressement)[4:length(excel_sheets(path = file_redressement))] %>%
-  map(~ read_excel(
-    path = file_redressement, sheet = .x,
-    range = "B10:Q10",
-    col_names = F
-  ) %>%
-    select(1:5, 12:14) %>%
-    setNames(nm = c(
-      "Annee", "Periode", "Mises en vente - Appartements", "c_nb_resa", "Encours - Appartements",
-      "Mises en vente - Maisons", "i_nb_resa", "Encours - Maisons"
-    )) %>%
-    mutate(
-      Periode = paste0(Annee, Periode),
-      REG = .x
-    ) %>%
-    select(REG, everything(), -Annee, -c_nb_resa, -i_nb_resa)) %>%
-  bind_rows() %>%
-  gather(Indicateur, Valeur, -REG, -Periode)
-
-# calcul des indicateurs sur la carte des communes contenue dans ecln
-# passage des données au cog à jour
-
-indic_ecln <- ECLN %>%
+# fichier_donnees <- paste0("data-raw/stat_info_data_regionaux_",millesime,".csv")
+# fichier_calage <- paste0("data-raw/cale_ventile_",millesime,".xls")
+fichier_donnees <- paste0("X:/SCTE/DEP/Donnees_confidentielles/Conjoncture/ECLN/Publication/stat_info_data_regionaux_",millesime,"_.csv")
+fichier_calage <- paste0("X:/SCTE/DEP/Donnees_confidentielles/Conjoncture/ECLN/Publication/cale_ventile_",millesime,".xls")
+
+# 1. Récupération des données
+# ------------------------------------------------------------------------------------------------------
+# Données envoyées par mail ou déposées sur le serveur RStudio (/home/partage-ECLN/ECLN/AAAATN/) sont déposées dans le répertoire des données confidentielles avec accès restreint
+
+# Données ECLN brutes non secrétisées - Attention séparateur ; et décimal .
+ECLN <- read.csv2(fichier_donnees, header = T, sep = ";", dec = ".")
+
+# Fichier de calage des ventes ventilés au niveau dep et reg - Seules les ventes du dernier trimestre sont calées
+cale_reg_dep <- read_excel(fichier_calage)
+
+
+# 2. Construction de la table des indicateurs communaux par type de logement et nature du projet
+# Dans le projet initial, seuls les indicateurs par type de logement étaient construits
+# Pour pouvoir utiliser, la nouvelle variable construction sur existant, on construit à l'identique des indicateurs sur la distinction de la nature du projet
+# La table finale sera une juxtaposition des indicateurs par type de logement et par nature du projet
+
+# 2.1 Construction de la table des indicateurs par type de logement id_type_lgt (1=individuel/2=collectif)
+# ---------------------------------------------------------------------------------------------------------------------------------------------------
+# On calcule par communes (code_insee-> DEPCOM), trimestre de mises en vente (trim_mev->Periode) et type de logement (id_type_lgt), le nombre de mises en vente (mev_t)
+# On calcule par communes (code_insee-> DEPCOM), trimestres (trimestre_enquete -> periode) et type de logement (id_type_lgt) :
+# - nombre de ventes en investissement locatif ( nb_lgt_inv = nb_lgt_inv_t1 + nb_lgt_inv_t2 + nb_lgt_inv_t3 + nb_lgt_inv_t4 + nb_lgt_inv_t5 + nb_lgt_inv_t6)
+# - nombre de ventes hors investissement locatif (nb_hors_resa_inv = nb_resa - nb_lgt_inv)
+# - nombre de logements réservés à la vente (nb_resa)
+# - prix total des ventes (prix)
+# - Surface totale (surface)
+# - encours de logements proposés à la vente (stock_fin)
+# On passe au format long et on renomme les indicateurs en les suffisant avec le type de logement
+
+indic_typ <- ECLN %>%
  select(DEPCOM = code_insee, Periode = trim_mev, id_type_lgt, mev_t) %>%
  as_tibble() %>%
-  filter(Periode >= trimestre_min) %>%
+  filter(Periode >= 201701) %>%
  group_by(id_type_lgt, DEPCOM, Periode) %>%
  summarise(mev_t = sum(mev_t, na.rm = T)) %>%
  ungroup() %>%
-  full_join(ECLN %>%
-    select(DEPCOM = code_insee, Periode = trimestre_enquete, everything()) %>%
-    filter(Periode >= trimestre_min) %>%
+  full_join ( ECLN %>%
+    select(DEPCOM = code_insee, Periode = trimestre_enquete , id_type_lgt, nb_resa, nb_lgt_inv_t1, nb_lgt_inv_t2, nb_lgt_inv_t3, nb_lgt_inv_t4, nb_lgt_inv_t5, nb_lgt_inv_t6,
+            surface, prix,
+           stock_fin ) %>%
+    filter(Periode >= 201701) %>%
    mutate(
      nb_lgt_inv = nb_lgt_inv_t1 + nb_lgt_inv_t2 + nb_lgt_inv_t3 + nb_lgt_inv_t4 + nb_lgt_inv_t5 + nb_lgt_inv_t6,
      nb_hors_resa_inv = nb_resa - nb_lgt_inv
@@ -76,7 +76,7 @@ indic_ecln <- ECLN %>%
      nb_hors_resa_inv = sum(nb_hors_resa_inv, na.rm = T),
      stock_fin = sum(stock_fin, na.rm = T),
      prix = sum(prix, na.rm = T),
-      surface = sum(surface, na.rm = T)
+     surface = sum(surface, na.rm = T)
    ) %>%
    ungroup()) %>%
  gather(Indicateur, Valeur, mev_t:surface) %>%
@@ -89,54 +89,103 @@ indic_ecln <- ECLN %>%
    Indicateur = str_c(Indicateur, " - ", id_type_lgt)
  ) %>%
  select(-id_type_lgt) %>%
-  mutate(Valeur = ifelse(is.na(Valeur), 0, Valeur)) %>%
-  passer_au_cog_a_jour(code_commune = DEPCOM, aggrege = T, garder = F) %>%
+  mutate(Valeur = ifelse(is.na(Valeur), 0, Valeur))
+
+
+
+# 2.2 Construction de la table des indicateurs par type de construction id_nature_projet (1=construction neuve/2=construction sur existant)
+# ------------------------------------------------------------------------------------------------------------------------------------------------------
+# On calcule par communes (code_insee-> DEPCOM), trimestre de mises en vente (trim_mev->Periode) et type de construction (id_nature_projet), le nombre de mises en vente (mev_t)
+# On calcule par communes (code_insee-> DEPCOM), trimestres (trimestre_enquete -> periode) et type de construction (id_nature_projet) :
+# - nombre de ventes en investissement locatif ( nb_lgt_inv = nb_lgt_inv_t1 + nb_lgt_inv_t2 + nb_lgt_inv_t3 + nb_lgt_inv_t4 + nb_lgt_inv_t5 + nb_lgt_inv_t6)
+# - nombre de ventes hors investissement locatif (nb_hors_resa_inv = nb_resa - nb_lgt_inv)
+# - nombre de logements réservés à la vente (nb_resa)
+# - prix total des ventes (prix)
+# - Surface totale (surface)
+# - encours de logements proposés à la vente (stock_fin)
+# On passe au format long et on renomme les indicateurs en les suffisant avec le type de construction
+
+indic_cons <- ECLN %>%
+  select(DEPCOM = code_insee, Periode = trim_mev, id_nature_projet, mev_t) %>%
+  as_tibble() %>%
+  filter(Periode >= 201701) %>%
+  group_by(id_nature_projet, DEPCOM, Periode) %>%
+  summarise(mev_t = sum(mev_t, na.rm = T)) %>%
+  ungroup() %>%
+  full_join ( ECLN %>%
+                select(DEPCOM = code_insee, Periode = trimestre_enquete , id_nature_projet, nb_resa, nb_lgt_inv_t1, nb_lgt_inv_t2, nb_lgt_inv_t3, nb_lgt_inv_t4, nb_lgt_inv_t5, nb_lgt_inv_t6,
+                       surface, prix,
+                       stock_fin ) %>%
+                filter(Periode >= 201701) %>%
+                mutate(
+                  nb_lgt_inv = nb_lgt_inv_t1 + nb_lgt_inv_t2 + nb_lgt_inv_t3 + nb_lgt_inv_t4 + nb_lgt_inv_t5 + nb_lgt_inv_t6,
+                  nb_hors_resa_inv = nb_resa - nb_lgt_inv
+                ) %>%
+                group_by(id_nature_projet, DEPCOM, Periode) %>%
+                summarise(
+                  nb_resa = sum(nb_resa, na.rm = T),
+                  nb_resa_inv = sum(nb_lgt_inv, na.rm = T),
+                  nb_hors_resa_inv = sum(nb_hors_resa_inv, na.rm = T),
+                  stock_fin = sum(stock_fin, na.rm = T),
+                  prix = sum(prix, na.rm = T),
+                  surface = sum(surface, na.rm = T)
+                ) %>%
+                ungroup()) %>%
+  gather(Indicateur, Valeur, mev_t:surface) %>%
+  mutate(
+    Periode = factor(Periode),
+    id_nature_projet = factor(id_nature_projet),
+    DEPCOM = factor(DEPCOM),
+    id_nature_projet = fct_recode(id_nature_projet,`Constructions neuves` = "1", `Constructions sur existant` = "2"),
+    Indicateur = fct_recode(factor(Indicateur), `Mises en vente` = "mev_t", Ventes = "nb_resa", `Ventes en investissement locatif` = "nb_resa_inv", `Ventes hors investissement locatif` = "nb_hors_resa_inv", Encours = "stock_fin", Prix = "prix", Surface = "surface"),
+    Indicateur = str_c(Indicateur, " - ", id_nature_projet)
+  ) %>%
+  select(- id_nature_projet) %>%
+  mutate(Valeur = ifelse(is.na(Valeur), 0, Valeur))
+
+
+# Cogification des deux tables
+# -----------------------------------------------------------------------------
+# on passe la table au cog à jour
+# on rajoute les communes non comprises dans la table de départ (jointure à droite) avec des valeurs manquantes pour les périodes,indicateurs et valeurs non présentes dans la talbe de départ.
+# Cela va permettre de faire des agrégations par EPCI, Dep, REG
+# Puis on complète avec des valeurs à null pour toutes les combinaisons possibles pour le triplet : depcom, périodes, indicateurs
+# On recogifie cette fois et on enlève les DOM
+
+
+indic_typ <- passer_au_cog_a_jour(indic_typ, code_commune = DEPCOM, aggrege = T, garder = F) %>%
  right_join(communes %>% select(DEPCOM)) %>%
  complete(DEPCOM, Periode, Indicateur,
-    fill = list(Valeur = 0)
+           fill = list(Valeur = 0)
  ) %>%
  filter(!is.na(Indicateur), !is.na(Periode)) %>%
-  passer_au_cog_a_jour(code_commune = DEPCOM, aggrege = T, garder = T) %>%
+  passer_au_cog_a_jour(code_commune = DEPCOM, aggrege = T, garder_info_supra = T) %>%
  select(REG, DEPCOM, Periode, Indicateur, Valeur) %>%
+  filter(!(REG %in% c("01", "02", "03", "04", "05", "06"))) %>%
  mutate_if(is.character, as.factor)

-# On ne garde que les régions métro
-indic_ecln <- indic_ecln %>%
-  filter(!(REG %in% c("01", "02", "03", "04", "05", "06")))
-
-
-rm(ECLN)

-taux_redressement <- indic_ecln %>%
-  filter(
-    Indicateur %in% unique(valeur_redressees$Indicateur),
-    as.character(REG) %in% unique(valeur_redressees$REG),
-    Periode == max(as.character(Periode))
+indic_cons <- passer_au_cog_a_jour(indic_cons, code_commune = DEPCOM, aggrege = T, garder_info_supra = FALSE) %>%
+  right_join(communes %>% select(DEPCOM)) %>%
+  complete(DEPCOM, Periode, Indicateur,
+           fill = list(Valeur = 0)
  ) %>%
-  select(REG, Periode, Indicateur, Valeur) %>%
-  group_by(REG, Periode, Indicateur) %>%
-  summarise_all(funs(sum)) %>%
-  ungroup() %>%
-  left_join(valeur_redressees, by = c("REG", "Periode", "Indicateur")) %>%
-  mutate(taux_redressement = ifelse(Valeur.x > 0, Valeur.y / Valeur.x, 1)) %>%
-  select(REG, Periode, Indicateur, taux_redressement)
-
-indic_ecln <- indic_ecln %>%
-  left_join(taux_redressement) %>%
-  mutate(Valeur = ifelse(Periode == max(Periode) & Indicateur %in% unique(valeur_redressees$Indicateur),
-    Valeur * taux_redressement,
-    Valeur
-  )) %>%
-  select(-taux_redressement, -REG) %>%
+  filter(!is.na(Indicateur), !is.na(Periode)) %>%
+  passer_au_cog_a_jour(code_commune = DEPCOM, aggrege = T, garder_info_supra = T) %>%
+  select(REG, DEPCOM, Periode, Indicateur, Valeur) %>%
+  filter(!(REG %in% c("01", "02", "03", "04", "05", "06"))) %>%
  mutate_if(is.character, as.factor)


-# Calcul sur total logements ----------------------------------------------
+# 3.2 Calcul sur total logements et agrégation des deux tables d'indicateurs
+# Dans la table des indicateurs ventilés par type de logement, les indicateurs sont décomposés par type de logement (Appartements et Maisons).
+# Il faut donc calculer le total (appartements + maisons). Il suffit de le faire sur une table car le total sera le même si on ventile par nature du projet.
+# Cette opération doit se faire sur une table au cog à jour.
+# Le calcul du total ne se fait que sur une des deux tables

-
-indic_ecln <- bind_rows(
-  indic_ecln,
-  indic_ecln %>%
+indic_typ <- bind_rows(
+  indic_typ,
+  indic_typ %>%
    mutate(Indicateur = str_split_fixed(Indicateur, " - ", 2)[, 1] %>% str_c(" - Logements") %>% as.factor()) %>%
    group_by_if(is.factor) %>%
    summarise_all(funs(sum(., na.rm = T))) %>%
@@ -144,66 +193,223 @@ indic_ecln <- bind_rows(
 ) %>%
  mutate_if(is.character, as.factor)

-# Aggrégation de l'ensemble des calculs sur les zones -----------------------------------
-indic_ecln <- indic_ecln %>%
+
+# 3.3 Cogification = Aggrégation de l'ensemble des calculs sur les zones géo (EPCI, REG, FRANCE métro)
+# On empile les deux tables et on cogifie
+# On enlève le zonage communes qui ne nous servira plus pour les travaux suivants :
+# - le calage
+# - le calcul des indicateurs annuels trimestriels et glissants
+
+
+indic_ecln <- bind_rows (indic_typ, indic_cons)
+
+indic_ecln_cog <- indic_ecln %>%
+  select(-REG) %>%
  group_split(Periode) %>%
  furrr::future_map_dfr(cogifier)

-# Rajout des zones abc
-# update 2019
-# zonage_abc_r52<-zonage_abc_r52 %>%
-#   passer_au_cog_a_jour(aggrege = F,garder_info_supra = F) %>%
-#   distinct() %>%
-#   arrange(DEPCOM,zonage_abc) %>%
-#   group_by(DEPCOM) %>%
-#   slice(1) %>%
-#   ungroup()
-
-
-indic_ecln_abc <- indic_ecln %>%
-  filter(TypeZone == "Communes", CodeZone %in% (liste_zone %>%
-    filter(str_detect(REG, "52"), TypeZone == "Communes") %>%
-    pull(CodeZone))) %>%
-  left_join(zonage_abc, by = c("CodeZone" = "DEPCOM")) %>%
-  select(zonage_abc, Periode, Indicateur, Valeur) %>%
-  rename(CodeZone = zonage_abc) %>%
-  mutate(
-    Zone = CodeZone,
-    TypeZone = "ABC"
-  ) %>%
-  group_by(TypeZone, Zone, CodeZone, Periode, Indicateur) %>%
-  summarise(Valeur = sum(Valeur)) %>%
-  ungroup()
-
-# indic_ecln_pinel <- indic_ecln %>%
-#   filter(TypeZone == "Communes", CodeZone %in% (liste_zone %>%
-#     filter(str_detect(REG, "52"), TypeZone == "Communes") %>%
-#     pull(CodeZone))) %>%
-#   left_join(zonage_pinel_r52, by = c("CodeZone" = "DEPCOM")) %>%
-#   select(zonage_pinel, Periode, Indicateur, Valeur) %>%
-#   rename(CodeZone = zonage_pinel) %>%
-#   mutate(
-#     Zone = CodeZone,
-#     TypeZone = "Pinel"
-#   ) %>%
-#   group_by(TypeZone, Zone, CodeZone, Periode, Indicateur) %>%
-#   summarise(Valeur = sum(Valeur)) %>%
-#   ungroup()
-
-# Aggrégation de l'ensemble des calculs sur les zones -----------------------------------
-
-
-indic_ecln <- bind_rows(indic_ecln, indic_ecln_abc) %>%
-  arrange(TypeZone, Zone, CodeZone, Indicateur, Periode) %>%
+# Table sans les communes pour alléger les travaux suivants :
+# - le calage
+# - le calcul des indicateurs annuels trimestriels et glissants
+
+indic_ecln_ss_com <- indic_ecln_cog %>%
+arrange(TypeZone, Zone, CodeZone, Indicateur, Periode) %>%
  filter(TypeZone != "Communes")

-rm(list = setdiff(ls(), c("indic_ecln", "FormatDate")))
+# 4. Calage
+# Les Mises en ventes et le stock en fin de trimestre (= Encours) sont calées pour les données du dernier trimestre
+# La cale est actuellement uniquement calculées au niveau régional et départemental
+# De fait, pour les mises en ventes et les encours, les données à l'EPCI ne seront pas diffusées sur le dernier truimestre mais sur le précédant
+# On récupère la cale dans le fichier cale_reg_dep : elle est décomposée par type delogement (id_type_lgt), de construction (id_nature_projet) et type de pièces (typo)
+# L'objectif est de calculer pour chaque indicateur devant être calé sa cale pour les différents niveaux géographiquex (france, reg et dep) :
+#   - Mises en ventes - Maisons
+# - Mises en ventes - Appartements
+# - Mises en ventes - Logements
+# - Mises en ventes - Construction neuve
+# - Mises en ventes - Construction sur existant
+# - Encours - Maisons
+# - Encours - Appartements
+# - Encours - Logements
+# - Encours - Construction neuve
+# - Encours - Construction sur existant
+
+# 4.1 Calage des indicateurs ventilés par type de logement
+# # On transpose (lignes (modalités de typo) en colonnes) la table de calage pour pouvoir sommer
+# # la cale (=valeurs de chaque typo) par type de logement
+# # on conserve tous les échelons géo pour pouvoir calculer les différents agrégats (nat, reg, dep)
+
+Cale_typ <- cale_reg_dep %>%
+  filter(!(reg2 %in% c("01", "02", "03", "04", "05", "06"))) %>%
+  select (reg2, dep, id_type_lgt, typo, Periode = trimestre_enquete, cale) %>%
+  group_by(Periode, reg2, dep, id_type_lgt, typo) %>%
+  summarise(cale_tot = sum(cale), na.rm = T) %>%
+  pivot_wider(id_cols =c(Periode, reg2, dep, id_type_lgt),
+              names_from = typo,
+              values_from = cale_tot) %>%
+  mutate(Cale = t1 + t2 + t3 + t4 + t5 + t6,
+         Periode = factor(Periode),
+         id_type_lgt = factor(id_type_lgt),
+         Calage = fct_recode(id_type_lgt,`Cale - Maisons` = "1", `Cale - Appartements` = "2")) %>%
+  ungroup() %>%
+  select(-id_type_lgt, -c(t1:t6))
+
+Cale_typ_nat <- Cale_typ %>%
+  group_by (Periode, Calage) %>%
+  summarise(Cale= sum(Cale)) %>%
+  mutate(TypeZone = "France",
+         TypeZone = factor(TypeZone),
+         Periode = factor(Periode),
+         CodeZone = "FRMETRO",
+         CodeZone = factor(CodeZone))
+
+
+Cale_typ_reg <- Cale_typ %>%
+  group_by (Periode,reg2, Calage) %>%
+  summarise(Cale= sum(Cale)) %>%
+  mutate( TypeZone = "Régions",
+          TypeZone = factor(TypeZone),
+          Periode = factor(Periode),
+          CodeZone = factor(reg2)) %>%
+  ungroup() %>%
+  select(-reg2)
+
+Cale_typ_dep <- Cale_typ %>%
+  group_by (Periode,dep, Calage) %>%
+  summarise(Cale= sum(Cale)) %>%
+  mutate( TypeZone = "Départements",
+          TypeZone = factor(TypeZone),
+          Periode = factor(Periode),
+          CodeZone = factor(dep)) %>%
+  ungroup() %>%
+  select(-dep)
+
+
+# On empile les tables cela nous permettra de calculer le total
+Cale_typ <- bind_rows (Cale_typ_nat, Cale_typ_reg, Cale_typ_dep)
+
+#  On calcule le total pour tout type de logement
+
+Cale_typ_tot <- bind_rows(
+  Cale_typ,
+  Cale_typ %>%
+    mutate(Calage = str_split_fixed(Calage, " - ", 2)[, 1] %>% str_c(" - Logements") %>% as.factor()) %>%
+    group_by_if (is.factor) %>%
+    summarise_all(funs(sum(., na.rm = T))) %>%
+    ungroup()
+) %>%
+  mutate_if(is.character, as.factor)
+
+# 4.2 Calage pour les indicateurs ventilés par nature du projet
+# On transpose (lignes (modalités de typo) en colonnes) la table de calage pour pouvoir sommer
+# la cale (=valeurs de chaque typo) par nature du projet
+# on conserve tous les échelons géo pour pouvoir calculer les différents agrégats
+
+Cale_cons <- cale_reg_dep %>%
+  filter(!(reg2 %in% c("01", "02", "03", "04", "05", "06"))) %>%
+  select (reg2, dep, id_nature_projet, typo, Periode = trimestre_enquete, cale) %>%
+  group_by(Periode, reg2, dep, id_nature_projet, typo) %>%
+  summarise(cale_tot = sum(cale), na.rm = T) %>%
+  pivot_wider(id_cols =c(Periode, reg2, dep, id_nature_projet),
+              names_from = typo,
+              values_from = cale_tot) %>%
+  mutate(Cale = t1 + t2 + t3 + t4 + t5 + t6,
+         Periode = factor(Periode),
+         id_nature_projet = factor(id_nature_projet),
+         Calage = fct_recode(id_nature_projet,`Cale - Constructions neuves` = "1", `Cale - Constructions sur existant` = "2")) %>%
+  ungroup() %>%
+  select(-id_nature_projet,-c(t1:t6))
+
+
+Cale_cons_nat <- Cale_cons %>%
+  group_by (Periode, Calage) %>%
+  summarise(Cale= sum(Cale)) %>%
+  mutate(TypeZone = "France",
+         TypeZone = factor(TypeZone),
+         Periode = factor(Periode),
+         CodeZone = "FRMETRO",
+         CodeZone = factor(CodeZone))
+
+
+Cale_cons_reg <- Cale_cons %>%
+  group_by (Periode,reg2, Calage) %>%
+  summarise(Cale= sum(Cale)) %>%
+  mutate( TypeZone = "Régions",
+          TypeZone = factor(TypeZone),
+          Periode = factor(Periode),
+          CodeZone = factor(reg2)) %>%
+  ungroup() %>%
+  select(-reg2)
+
+Cale_cons_dep <- Cale_cons %>%
+  group_by (Periode,dep, Calage) %>%
+  summarise(Cale= sum(Cale)) %>%
+  mutate( TypeZone = "Départements",
+          TypeZone = factor(TypeZone),
+          Periode = factor(Periode),
+          CodeZone = factor(dep))%>%
+  ungroup() %>%
+  select(-dep)
+
+Cale_cons <- bind_rows (Cale_cons_nat, Cale_cons_reg, Cale_cons_dep)
+
+
+# 4.3 Calcul des valeurs des indicateurs calés
+# A partir de la table des indicateurs à caler
+
+# On empile toutes les tables de calage
+cale <- bind_rows (Cale_cons, Cale_typ_tot)
+
+# Liste des indicateurs à caler
+
+# Indic_a_caler <- str_subset(levels(indic_ecln$Indicateur), c("Encours","Mises"))
+Indic_a_caler <- c( str_subset(levels(indic_ecln$Indicateur), "Encours"),  str_subset(levels(indic_ecln$Indicateur), "Mises") )
+
+indic_ecln_ss_com <- indic_ecln_ss_com %>%
+  mutate(indic_calage = ifelse(TypeZone %in% c("France", "Régions", "Départements") &
+                                 Indicateur %in% Indic_a_caler &
+                                 Periode == millesime, "oui", "non"))
+                                 # Periode == "2023T2", "oui", "non"))

-# Utilisation du package dtplyr pour la performance
+
+# extraction dans la table des indicateurs ECLN des indicateurs à caler
+
+Indic_cale <- indic_ecln_ss_com %>%
+  filter(indic_calage == "oui") %>%
+    mutate ( Calage = case_when(Indicateur == 'Mises en vente - Appartements' |  Indicateur == 'Encours - Appartements' ~ 'Cale - Appartements',
+                                Indicateur == 'Mises en vente - Maisons' | Indicateur == 'Encours - Maisons' ~ 'Cale - Maisons',
+                                Indicateur == 'Mises en vente - Constructions neuves' | Indicateur == 'Encours - Constructions neuves' ~ 'Cale - Constructions neuves',
+                                Indicateur == 'Mises en vente - Constructions sur existant' | Indicateur == 'Encours - Constructions sur existant' ~ 'Cale - Constructions neuves sur existant',
+                                Indicateur == 'Mises en vente - Logements' | Indicateur == 'Encours - Logements' ~ 'Cale - Logements'
+                              ))
+
+
+Indic_cale <- Indic_cale %>%
+  left_join(cale) %>%
+  mutate(Valeur = ifelse(is.na(Cale), Valeur, Valeur + Cale))
+
+
+# 4.4 Table finale = table des indicateurs à caler + celle des indicateurs non calés
+
+# # On empile la table des indicateurs calés avec la table des indic_non calés
+
+
+Indic_final <- bind_rows( indic_ecln_ss_com %>%
+                          filter(indic_calage == "non") ,
+                          Indic_cale %>% select (-Cale, -Calage)) %>%
+            select(-indic_calage)
+
+
+# 5. Calcul des indicateurs annuels glissants
+# -----------------------------------------------------------------
+
+
+ # Attention les encours sont un stock, ils ne sont donc pas sommable sur une année

 indicateurs_non_sommables <- str_subset(levels(indic_ecln$Indicateur), "Encours")

-indic_ecln <- indic_ecln %>%
+# On ajoute une colonne valeur_cumul qui correspond au cumul annuel glissant sauf pour les encours = valeur du trimestre
+
+Indic_final<- Indic_final %>%
  lazy_dt() %>%
  group_by(TypeZone, Zone, CodeZone, Indicateur) %>%
  mutate(Valeur_cumul = Valeur + lag(Valeur) + lag(Valeur, 2) + lag(Valeur, 3)) %>%
@@ -212,10 +418,25 @@ indic_ecln <- indic_ecln %>%
  as_tibble()


-# Calcul des valeurs trimestrielles et cumul annuel ---------------------------
+# 5. Calcul des valeurs trimestrielles et cumul annuel
+# ----------------------------------------------------
+# L'objectif est de calculer :
+# - des indicateurs annuels glissants et de différencier les indicateurs trimestriels / annuels
+# - des taux d'évolution sur 12 mois pour chauqe indicatzeur
+# - d'autres indicateurs
+#     PrixM2_Maisons = Prix_Maisons / Surface_Maisons,
+#     PrixM2_Appartements = Prix_Appartements / Surface_Appartements,
+#     PrixM2_Logements = Prix_Logements / Surface_Logements,
+#     PrixPar_Maisons = Prix_Maisons / Ventes_Maisons,
+#     PrixPar_Appartements = Prix_Appartements / Ventes_Appartements,
+#     PrixPar_Logements

+# Dans la table finale, on aura la source, le type de l'indicateur (trimestriel, annuel)

-indic_ecln_trim <- indic_ecln %>%
+# Appel de la fonction FormatDate
+source("R/FormatDate.R")
+
+indic_ecln_trim <- Indic_final %>%
  select(-Valeur_cumul) %>%
  mutate(Indicateur = str_replace_all(Indicateur, " - ", "_")) %>%
  spread(Indicateur, Valeur, fill = 0) %>%
@@ -233,10 +454,9 @@ indic_ecln_trim <- indic_ecln %>%
    Periode = FormatDate(Periode, Periodicite = "Trim", sep = "T"),
    Source = "ECLN",
    TypeIndicateur = "Trimestriel"
-  ) %>%
-  filter(Periode > "2010-01-01")
+  )

-indic_ecln_cumul <- indic_ecln %>%
+indic_ecln_cumul <- Indic_final %>%
  select(-Valeur) %>%
  mutate(Indicateur = str_replace_all(Indicateur, " - ", "_")) %>%
  spread(Indicateur, Valeur_cumul, fill = 0) %>%
@@ -254,8 +474,9 @@ indic_ecln_cumul <- indic_ecln %>%
    Periode = FormatDate(Periode, Periodicite = "Trim", sep = "T"),
    Source = "ECLN",
    TypeIndicateur = "Cumul annuel"
-  ) %>%
-  filter(Periode > "2011-01-01")
+  )
+# %>%
+#  filter(Periode > "2011-01-01")

 # Aggrétation finale -----

@@ -270,9 +491,11 @@ TauxEvolution12Mois <- function(.data) {
 indic_ecln_cumul <- TauxEvolution12Mois(indic_ecln_cumul)
 indic_ecln_trim <- TauxEvolution12Mois(indic_ecln_trim)

-indic_ecln <- bind_rows(indic_ecln_cumul, indic_ecln_trim) %>%
+indic_ecln_non_secret <- bind_rows(indic_ecln_cumul, indic_ecln_trim) %>%
  mutate_if(is.character, as.factor) %>%
-  mutate(TypeZone = fct_relevel(TypeZone, "France", "Régions", "Départements", "Epci", "ABC"))
+  mutate(TypeZone = fct_relevel(TypeZone, "France", "Régions", "Départements", "Epci"))

+indic_ecln <- indic_ecln_non_secret

 usethis::use_data(indic_ecln, overwrite = TRUE)
+
--- a/inst/rmarkdown/templates/ecln_conjoncture/skeleton/.gitignore
+++ b/inst/rmarkdown/templates/ecln_conjoncture/skeleton/.gitignore
 skeleton.html
-www/*
--- a/inst/rmarkdown/templates/ecln_conjoncture/skeleton/skeleton.Rmd
+++ b/inst/rmarkdown/templates/ecln_conjoncture/skeleton/skeleton.Rmd
@@ -237,26 +237,26 @@ creer_graphique_series_temporelles_prix(
 ### Commercialisation des appartements par EPCI

 ```{r collectifcartes_volume}
-creer_carte_volume<-function(data=indic_ecln,
-                             code_region = params$reg,
-                             indicateurs = c("Encours","Mises en vente","Ventes"),
-                             type_logement="Appartements",
-                             type_indicateur="Cumul annuel",
-                             titre="Volumes de ventes, mises en vente et encours d'appartements",
-                             titre_legende="",
-                             bas_de_page=caption_carte_appartements)
+creer_carte_volume(data=indic_ecln,
+                   code_region = params$reg,
+                   indicateurs = c("Encours","Mises en vente","Ventes"),
+                   type_logement="Appartements",
+                   type_indicateur="Cumul annuel",
+                   titre="Volumes de ventes, mises en vente et encours d'appartements",
+                   titre_legende="",
+                   bas_de_page=caption_carte_appartements)
 ```


-```{r collectifcartes_volume}
-creer_carte_evolution<-function(data=indic_ecln,
-                             code_region = params$reg,
-                             indicateurs = c("Encours","Mises en vente","Ventes"),
-                             type_logement="Appartements",
-                             type_indicateur="Cumul annuel",
-                             titre="Volumes de ventes, mises en vente et encours d'appartements",
-                             titre_legende="",
-                             bas_de_page=caption_carte_appartements)
+```{r collectifcartes_evol}
+creer_carte_evolution(data=indic_ecln,
+                      code_region = params$reg,
+                      indicateurs = c("Encours","Mises en vente","Ventes"),
+                      type_logement="Appartements",
+                      type_indicateur="Cumul annuel",
+                      titre="Volumes de ventes, mises en vente et encours d'appartements",
+                      titre_legende="",
+                      bas_de_page=caption_carte_appartements)
 ```


@@ -341,27 +341,27 @@ creer_graphique_series_temporelles_prix(

 ### Commercialisation des maisons par EPCI

-```{r collectifcartes_volume}
-creer_carte_volume<-function(data=indic_ecln,
-                             code_region = params$reg,
-                             indicateurs = c("Encours","Mises en vente","Ventes"),
-                             type_logement="Maisons",
-                             type_indicateur="Cumul annuel",
-                             titre="Volumes de ventes, mises en vente et encours d'appartements",
-                             titre_legende="",
-                             bas_de_page=caption_carte_appartements)
+```{r collectifcartes_volume_epci}
+creer_carte_volume(data=indic_ecln,
+                   code_region = params$reg,
+                   indicateurs = c("Encours","Mises en vente","Ventes"),
+                   type_logement="Maisons",
+                   type_indicateur="Cumul annuel",
+                   titre="Volumes de ventes, mises en vente et encours d'appartements",
+                   titre_legende="",
+                   bas_de_page=caption_carte_appartements)
 ```


-```{r collectifcartes_volume}
-creer_carte_evolution<-function(data=indic_ecln,
-                             code_region = params$reg,
-                             indicateurs = c("Encours","Mises en vente","Ventes"),
-                             type_logement="Maisons",
-                             type_indicateur="Cumul annuel",
-                             titre="Volumes de ventes, mises en vente et encours d'appartements",
-                             titre_legende="",
-                             bas_de_page=caption_carte_appartements)
+```{r collectifcartes_evol_epci}
+creer_carte_evolution(data=indic_ecln,
+                      code_region = params$reg,
+                      indicateurs = c("Encours","Mises en vente","Ventes"),
+                      type_logement="Maisons",
+                      type_indicateur="Cumul annuel",
+                      titre="Volumes de ventes, mises en vente et encours d'appartements",
+                      titre_legende="",
+                      bas_de_page=caption_carte_appartements)
 ```


--- a/inst/rmarkdown/templates/ecln_conjoncture/skeleton/www/52-pays-de-la-loire-RVB.png
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