From c1decb7cb083e1997c75c2c5d4ed39c272019526 Mon Sep 17 00:00:00 2001
From: "Daniel.Kalioudjoglou"
<daniel.kalioudjoglou@developpement-durable.gouv.fr>
Date: Fri, 28 Mar 2025 14:32:00 +0000
Subject: [PATCH] =?UTF-8?q?mise=20=C3=A0=20jour=20des=20sources=20DK=2003/?=
=?UTF-8?q?2025?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
* chargement des donnes ecln sur sgbd depuis serveur ecosql
* mise à jour 2021 lgt_rgp_insee, pop_rgp_insee (Fichiers détail pop et lgt du RP INSEE 2021)
* chargement observatoire_artificialisation 2023
* chargement siclop 2024 + ajouts les indicateurs des totaux par nombre de pièces
* chargement des donnes EPTB 2023 depuis propre.eptb
---
R/poster_documenter_data.R | 194 +++++------
R/poster_documenter_ind.R | 7 +-
data-raw/chargement_ecln.R | 168 ++--------
data-raw/chargement_eptb.R | 148 +++------
data-raw/chargement_lgt_rgp_insee.R | 11 +-
...hargement_observatoire_artificialisation.R | 49 ++-
data-raw/chargement_pop_rgp_insee.R | 10 +-
data-raw/chargement_siclop.R | 104 +++++-
data-raw/cogification_ecln.R | 71 ----
data-raw/cogification_eptb.R | 75 -----
data-raw/indicateur_ecln.R | 113 -------
data-raw/indicateur_eptb.R | 107 -------
data-raw/secret_ecln.R | 302 ------------------
data-raw/secret_eptb.R | 173 ----------
metadata/Insee_RGP_lgt_lib_variable.ods | Bin 44704 -> 67338 bytes
metadata/Insee_RGP_pop_lib_variable.ods | Bin 41975 -> 47786 bytes
16 files changed, 301 insertions(+), 1231 deletions(-)
delete mode 100644 data-raw/cogification_ecln.R
delete mode 100644 data-raw/cogification_eptb.R
delete mode 100644 data-raw/indicateur_ecln.R
delete mode 100644 data-raw/indicateur_eptb.R
delete mode 100644 data-raw/secret_ecln.R
delete mode 100644 data-raw/secret_eptb.R
diff --git a/R/poster_documenter_data.R b/R/poster_documenter_data.R
index 924d10d..f6cfe16 100644
--- a/R/poster_documenter_data.R
+++ b/R/poster_documenter_data.R
@@ -1,97 +1,97 @@
-library(datalibaba)
-library(googlesheets4)
-
-poster_documenter_it <- function(df = pop10, nom_table_sgbd = "source_pop_rgp_insee", user = "does", serveur = NULL, secret = FALSE,
- comm_source_en_plus = "T\u00e9l\u00e9chargement depuis...") {
-
- # Vérif clé primaire : une ligne par commune et par date
- doublons <- df %>%
- dplyr::count(depcom, date) %>%
- dplyr::filter(n>1) %>%
- nrow()
- attempt::stop_if(doublons > 0, msg = glue::glue("La table df contient plusieurs lignes qui ont \u00e0 la fois le m\u00eame code commune et la m\u00eame date m\u00eame date"))
-
-
- # CHARGEMENT de la table dans le SGBD
- datalibaba::poster_data(data = df, server = serveur,
- db = "datamart",
- schema = "portrait_territoires",
- table = nom_table_sgbd,
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = !secret,
- pk = c("depcom", "date"), # déclaration d'une clé primaire sur la table postée : on ne doit pas avoir deux lignes avec à la fois le même code commune et la meme date
- user = user)
-
-
- # RECUPERATION DES METADONNEES
-
- ## On récupère la liste des variables qui sont à documenter dans le tableur google sheet à partir du jeu de données posté
- var <- dplyr::setdiff(names(df), c("depcom", "date"))
-
- ## récupération du nom du présent script source pour filtrer ensuite le référentiel des indicateurs
- nom_script_sce <- rstudioapi::getActiveDocumentContext()$path %>% # utilisation de rstudioapi pour récupérer le nom du présent script
- basename() %>% # on enlève le chemin d'accès pour ne garder que le nom du fichier
- gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
- message("nom_script_sce détecté ", nom_script_sce)
- ## authentification google sheet grâce au .Renviron
- googlesheets4::gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
- googlesheets4::gs4_deauth()
-
- ## chargement du référentiel indicateurs google sheet
- metadata_indicateur <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "indicateurs") %>%
- # on ne garde que les variables concernées par le présent script de chargement
- dplyr::filter(source == nom_script_sce) %>%
- # on ajoute l'unité dans le libellé de la variable
- dplyr::mutate(libelle_variable = paste0(libelle_variable, " (unit\u00e9 : ", unite, ")")) %>%
- dplyr::select(variable, libelle_variable) %>%
- # ajout des libellés pour depcom et date
- dplyr::bind_rows(
- tidyr::tribble(
- ~variable, ~libelle_variable,
- "depcom", "Code INSEE de la commune",
- "date", "Millesime"
- )
- )
-
- ## Vérification que la documentation des indicateurs est complète
- var_mq <- dplyr::setdiff(var, metadata_indicateur$variable) %>% paste(collapse = "\n")
- var_en_trop <- dplyr::setdiff(metadata_indicateur$variable, c("depcom", "date", var)) %>% paste(collapse = "\n")
- attempt::stop_if_not(all(var %in% metadata_indicateur$variable),
- msg = glue::glue("La table df contient des variables non document\u00e9es dans le r\u00e9f\u00e9rentiel google sheet : \n{var_mq}\n"))
- attempt::stop_if_not(all(metadata_indicateur$variable %in% c("depcom", "date", var)),
- msg = glue::glue("Le r\u00e9f\u00e9rentiel google sheet des indicateurs contient des variables absentes de la table df :\n{var_en_trop}\n",
- "V\u00e9rifier si df contient bien toutes les variables attendues et adpatez le r\u00e9f\u00e9rentiel des indicateurs au besoin."))
-
-
- ## Envoi des libellés de variable en commentaire de champ dans le SGBD
- datalibaba::post_dico_attr(dico = metadata_indicateur, table = nom_table_sgbd, schema = "portrait_territoires",
- db = "datamart", user = user, server = serveur)
-
- ## Récupération des métadonnées de la source
- nom_sce <- stringr::str_replace(nom_script_sce, "chargement_|ref_|specifique_", "") %>%
- stringr::str_replace("indicateur_", "") %>%
- stringr::str_replace("_cogiter|_cog$", "")
-
- metadata_source <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "sources") %>%
- dplyr::filter(source == nom_sce) %>%
- dplyr::mutate(com_table = paste0(source_lib, " - ", producteur, ".\n", descriptif_sources)) %>%
- dplyr::pull(com_table) %>%
- # ajout de complement sur la généalogie
- paste0("\n", comm_source_en_plus)
-
-
- ## commentaires de la table
- datalibaba::commenter_table(comment = metadata_source,
- db = "datamart",
- server = serveur,
- schema = "portrait_territoires",
- table = nom_table_sgbd,
- user = user)
-
-}
-
-
-
+library(datalibaba)
+library(googlesheets4)
+
+poster_documenter_it <- function(df = pop10, nom_table_sgbd = "source_pop_rgp_insee", user = "does", serveur = NULL, secret = FALSE,
+ comm_source_en_plus = "T\u00e9l\u00e9chargement depuis...") {
+
+ # Vérif clé primaire : une ligne par commune et par date
+ doublons <- df %>%
+ dplyr::count(depcom, date) %>%
+ dplyr::filter(n>1) %>%
+ nrow()
+ attempt::stop_if(doublons > 0, msg = glue::glue("La table df contient plusieurs lignes qui ont \u00e0 la fois le m\u00eame code commune et la m\u00eame date m\u00eame date"))
+
+
+ # CHARGEMENT de la table dans le SGBD
+ datalibaba::poster_data(data = df, server = serveur,
+ db = "datamart",
+ schema = "portrait_territoires",
+ table = nom_table_sgbd,
+ post_row_name = FALSE,
+ overwrite = TRUE,
+ droits_schema = !secret,
+ pk = c("depcom", "date"), # déclaration d'une clé primaire sur la table postée : on ne doit pas avoir deux lignes avec à la fois le même code commune et la meme date
+ user = user)
+
+
+ # RECUPERATION DES METADONNEES
+
+ ## On récupère la liste des variables qui sont à documenter dans le tableur google sheet à partir du jeu de données posté
+ var <- dplyr::setdiff(names(df), c("depcom", "date"))
+
+ ## récupération du nom du présent script source pour filtrer ensuite le référentiel des indicateurs
+ nom_script_sce <- rstudioapi::getActiveDocumentContext()$path %>% # utilisation de rstudioapi pour récupérer le nom du présent script
+ basename() %>% # on enlève le chemin d'accès pour ne garder que le nom du fichier
+ gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
+ message("nom_script_sce détecté ", nom_script_sce)
+ ## authentification google sheet grâce au .Renviron
+ googlesheets4::gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
+ googlesheets4::gs4_deauth()
+
+ ## chargement du référentiel indicateurs google sheet
+ metadata_indicateur <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
+ sheet = "indicateurs") %>%
+ # on ne garde que les variables concernées par le présent script de chargement
+ dplyr::filter(source == nom_script_sce) %>%
+ # on ajoute l'unité dans le libellé de la variable
+ dplyr::mutate(libelle_variable = paste0(libelle_variable, " (unit\u00e9 : ", unite, ")")) %>%
+ dplyr::select(variable, libelle_variable) %>%
+ # ajout des libellés pour depcom et date
+ dplyr::bind_rows(
+ tidyr::tribble(
+ ~variable, ~libelle_variable,
+ "depcom", "Code INSEE de la commune",
+ "date", "Millesime"
+ )
+ )
+
+ ## Vérification que la documentation des indicateurs est complète
+ var_mq <- dplyr::setdiff(var, metadata_indicateur$variable) %>% paste(collapse = "\n")
+ var_en_trop <- dplyr::setdiff(metadata_indicateur$variable, c("depcom", "date", var)) %>% paste(collapse = "\n")
+ attempt::stop_if_not(all(var %in% metadata_indicateur$variable),
+ msg = glue::glue("La table df contient des variables non document\u00e9es dans le r\u00e9f\u00e9rentiel google sheet : \n{var_mq}\n"))
+ attempt::stop_if_not(all(metadata_indicateur$variable %in% c("depcom", "date", var)),
+ msg = glue::glue("Le r\u00e9f\u00e9rentiel google sheet des indicateurs contient des variables absentes de la table df :\n{var_en_trop}\n",
+ "V\u00e9rifier si df contient bien toutes les variables attendues et adpatez le r\u00e9f\u00e9rentiel des indicateurs au besoin."))
+
+
+ ## Envoi des libellés de variable en commentaire de champ dans le SGBD
+ datalibaba::post_dico_attr(dico = metadata_indicateur, table = nom_table_sgbd, schema = "portrait_territoires",
+ db = "datamart", user = user, server = serveur)
+
+ ## Récupération des métadonnées de la source
+ nom_sce <- stringr::str_replace(nom_script_sce, "chargement_|ref_|specifique_", "") %>%
+ stringr::str_replace("indicateur_", "") %>%
+ stringr::str_replace("_cogiter|_cog$", "")
+
+ metadata_source <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
+ sheet = "sources") %>%
+ dplyr::filter(source == nom_sce) %>%
+ dplyr::mutate(com_table = paste0(source_lib, " - ", producteur, ".\n", descriptif_sources)) %>%
+ dplyr::pull(com_table) %>%
+ # ajout de complement sur la généalogie
+ paste0("\n", comm_source_en_plus)
+
+
+ ## commentaires de la table
+ datalibaba::commenter_table(comment = metadata_source,
+ db = "datamart",
+ server = serveur,
+ schema = "portrait_territoires",
+ table = nom_table_sgbd,
+ user = user)
+
+}
+
+
+
diff --git a/R/poster_documenter_ind.R b/R/poster_documenter_ind.R
index 25d80b0..408ad74 100644
--- a/R/poster_documenter_ind.R
+++ b/R/poster_documenter_ind.R
@@ -34,7 +34,7 @@ poster_documenter_ind <- function(df = indicateur_bimotor_menages, nom_table_sgb
if(is.null(nom_script_sce)) {
nom_script_sce <- rstudioapi::getSourceEditorContext()$path %>% # utilisation de rstudioapi pour récupérer le nom du présent script
basename() %>% # on enlève le chemin d'accès pour ne garder que le nom du fichier
- gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
+ gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
if(nom_script_sce == "cogifier_en_masse") {
nom_script_sce <- nom_table_sgbd
}
@@ -46,7 +46,7 @@ poster_documenter_ind <- function(df = indicateur_bimotor_menages, nom_table_sgb
## chargement du référentiel indicateurs google sheet
metadata_indicateur0 <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "indicateurs")
+ sheet = "indicateurs")
metadata_indicateur <- metadata_indicateur0 %>%
# on ne garde que les variables concernées par le présent script de chargement
dplyr::filter(source == nom_script_sce) %>%
@@ -85,7 +85,7 @@ poster_documenter_ind <- function(df = indicateur_bimotor_menages, nom_table_sgb
stringr::str_replace("_cogiter|_cog$", "")
metadata_source0 <- googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "sources")
+ sheet = "sources")
metadata_source <- metadata_source0 %>%
dplyr::filter(source == nom_sce) %>%
dplyr::mutate(com_table = paste0(source_lib, " - ", producteur, ".\n", descriptif_sources)) %>%
@@ -106,3 +106,4 @@ poster_documenter_ind <- function(df = indicateur_bimotor_menages, nom_table_sgb
+
diff --git a/data-raw/chargement_ecln.R b/data-raw/chargement_ecln.R
index c114db6..55f3237 100644
--- a/data-raw/chargement_ecln.R
+++ b/data-raw/chargement_ecln.R
@@ -1,149 +1,37 @@
+# chargement des données ecln annuelles sur X
-# chargement_ecln
-
-# librairies ------------
-library(readxl)
library(tidyverse)
-library(tricky)
-library(lubridate)
-library(COGiter)
-library(DBI)
-library(RPostgreSQL)
-library(datalibaba)
-library(googlesheets4)
rm(list = ls())
-# chargement des données ECLN (Enquete sur la commercialisation des logements neufs) --------
-# fichier cree a partir d une requete geokit 3 :
-# "Dossiers publics/geokit3/Regions Pays de la Loire/DREAL/INDICATEURS TER DATA LAB/gk3_ecln"
-ecln <- read_excel("extdata/gk3_ecln.xlsx",sheet=1)
-
-
-# liste des communes COGiter ------
-com_fr<- communes %>%
- select(DEPCOM) %>%
- mutate(DEPCOM=as.character(DEPCOM)) %>%
- pull(DEPCOM)
-
-
-# calcul ---------
-ecln1<-ecln %>%
- # filter(date>=2008)%>%
- mutate(type_construction=str_replace(type_construction,'1. Individuel', "individuel") %>%
- str_replace('2. Collectif',"collectif"))
-
-ecln2<-ecln1 %>%
- group_by(date,depcom) %>%
- summarise(nb_logt_mis_en_vente=sum(nb_logt_mis_en_vente,na.rm=T),
- nb_logt_reserves_a_la_vente=sum(nb_logt_reserves_a_la_vente,na.rm=T),
- nb_logt_reserves_a_la_vente_en_invest_loc=sum(nb_logt_reserves_a_la_vente_en_invest_loc,na.rm=T),
- nb_logt_en_cours=sum(nb_logt_en_cours,na.rm=T),
- prix_total_des_ventes=sum(prix_total_des_ventes,na.rm=T),
- surface_totale_des_ventes=sum(surface_totale_des_ventes,na.rm=T),
- nb_promoteurs_mev=sum(nb_promoteurs_mev,na.rm=T),
- nb_promoteurs_reserves=sum(nb_promoteurs_reserves,na.rm=T),
- nb_promoteurs_en_cours=sum(nb_promoteurs_en_cours,na.rm=T)
- ) %>%
- ungroup() %>%
- mutate(type_construction="total")%>%
- as.data.frame()
-
-ecln<-bind_rows(ecln1,ecln2) %>%
- rename(nb_logt_vendus = nb_logt_reserves_a_la_vente,
- nb_logt_vendus_en_invest_loc = nb_logt_reserves_a_la_vente_en_invest_loc,
- nb_promoteurs_vendus = nb_promoteurs_reserves)%>%
- gather(variable,valeur,nb_logt_mis_en_vente:`nb_promoteurs_en_cours`)%>%
- mutate(variable=paste0(variable,".",type_construction)) %>%
- select(depcom,date,variable,valeur)%>%
- mutate(date=make_date(date,12,31))%>%
- mutate_if(is.character,as.factor) %>%
- mutate(depcom = fct_expand(depcom, com_fr)) %>%
- complete(depcom,date,variable,fill = list(valeur =0)) %>%
+load("X:/SCTE/CSD/DONNEES_CONFIDENTIELLES/_niveau_2/Conjoncture/ECLN/indic_ecln.rda")
+
+# filtre pour ne garder que les données annuelles
+tab_ecln <- indic_ecln %>%
+ filter(TypeIndicateur=="Cumul annuel",
+ str_detect(Periode, "-12-31")) %>%
+ mutate(Indicateur=tolower(Indicateur),
+ Indicateur=str_replace(Indicateur," - ","."),
+ Indicateur=str_replace(Indicateur,"appartements","appart"),
+ Indicateur=str_replace(Indicateur,"constructions neuves","cons_neuves"),
+ Indicateur=str_replace(Indicateur,"constructions sur existant","cons_sur_exist"),
+ Indicateur=str_replace(Indicateur,"mises en vente","mev"))
+
+tab_valeurs <- tab_ecln %>%
+ select(TypeZone,Zone,CodeZone,date=Periode,variable=Indicateur, valeur=Valeur)
+tab_evol <- tab_ecln %>%
+ select(TypeZone,Zone,CodeZone,date=Periode,variable=Indicateur, valeur=TauxEvolution12Mois) %>%
+ mutate(variable = paste0("evol12m_",variable))
+source_ecln <- bind_rows(tab_valeurs,tab_evol) %>%
pivot_wider(names_from = variable,values_from = valeur)
+# versement dans le sgbd/datamart.portrait_territoires + documentation -------------
+source("R/poster_documenter_ind.R")
-# versement dans le sgbd/datamart.portrait_territoires -------------
-poster_data(data = ecln,
- db = "datamart",
- schema = "portrait_territoires",
- table = "source_ecln",
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- pk = c("depcom", "date"), # déclaration d'une clé primaire sur la table postée : on ne doit pas avoir deux lignes avec à la fois le même code commune et la meme date
- user = "does")
-
-# METADONNEES------------------------------------
-
-## On récupère la liste des variables qui sont à documenter dans le tableur google sheet à partir du jeu de données posté
-var <- setdiff(names(ecln), c("depcom", "date"))
-
-## récupération du nom du présent script source pour filtrer ensuite le référentiel des indicateurs
-nom_script_sce <- rstudioapi::getActiveDocumentContext()$path %>% # utilisation de rstudioapi pour récupérer le nom du présent script
- basename() %>% # on enlève le chemin d'accès pour ne garder que le nom du fichier
- gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
-
-## authentification google sheet grâce au .Renviron
-gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
-gs4_deauth()
-
-
-
-## chargement du référentiel indicateurs google sheet
-metadata_indicateur <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "indicateurs") %>%
- # on ne garde que les variables concernées par le présent script de chargement
- filter(source == nom_script_sce) %>%
- # on ajoute l'unité dans le libellé de la variable
- mutate(libelle_variable = paste0(libelle_variable, " (unit\u00e9 : ", unite, ")")) %>%
- select(variable, libelle_variable) %>%
- # ajout des libellés pour depcom et date
- bind_rows(
- tribble(
- ~variable, ~libelle_variable,
- "depcom", "Code INSEE de la commune",
- "date", "Millesime"
- )
- )
-
-## Vérification que la documentation des indicateurs est complète
-all(var %in% metadata_indicateur$variable) # doit renvoyer TRUE
-
-## Envoi des libellés de variable dans le SGBD
-post_dico_attr(dico = metadata_indicateur, table = "source_ecln", schema = "portrait_territoires",
- db = "datamart", user = "does")
-
-## Récupération des métadonnées de la source
-nom_sce <- str_replace(nom_script_sce, "chargement_|ref_|specifique_", "") %>%
- str_replace("indicateur_", "") %>%
- str_replace("_cogiter|_cog$", "")
-
-metadata_source <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "sources") %>%
- filter(source == nom_sce) %>%
- mutate(com_table = paste0(source_lib, " - ", producteur, ".\n", descriptif_sources)) %>%
- pull(com_table) %>%
- # ajout de complement sur la généalogie
- paste0(".\n", "Chargement des donn\u00e9es sur Geokit3")
-
-## commentaires de la table
-
-commenter_table(comment = metadata_source,
- db = "datamart",
- schema = "portrait_territoires",
- table = "source_ecln",
- user = "does")
-
-
-
-
-
-
-
-
-
-
-
-
+poster_documenter_ind(
+ df = source_ecln,
+ nom_table_sgbd = "cogifiee_ecln",
+ comm_source_en_plus = "Données chargées depuis le serveur RStudio du SDES pour les DREALs"
+)
+rm(list=ls())
diff --git a/data-raw/chargement_eptb.R b/data-raw/chargement_eptb.R
index a5e6024..4bf0f90 100644
--- a/data-raw/chargement_eptb.R
+++ b/data-raw/chargement_eptb.R
@@ -1,116 +1,64 @@
+# chargement_eptb (a exécuter dans sur le serveur du SDES)
-# chargement_eptb
+# installation propre.eptb
+# remotes::install_gitlab("rdes_dreal/propre.eptb")
-
-# librairies --------
+# librairies --------------
library(tidyverse)
-library(readxl)
-library(tricky)
library(lubridate)
library(DBI)
library(RPostgreSQL)
library(datalibaba)
library(googlesheets4)
+library(propre.eptb)
rm(list = ls())
+eptb <- propre.eptb::dataprep(nom_reg = "Bretagne", df = "/nfs/data/partage-EPTB/propre.EPTB/tab_result.parquet") %>%
+ filter (TypeZone != "Densité") %>%
+ select(-Zone_de_ref,-Zone_de_comp) %>%
+ rename(date=millesime) %>%
+ mutate_if(is.factor,as.character) %>%
+ mutate(date= as.Date(paste0(date,"-12-31")))
+
+# versement dans le sgbd EcoSQL/datamart.portrait_territoires -------------
+
+source("R/poster_documenter_ind.R", encoding = "UTF-8")
+poster_documenter_ind(
+ df = eptb,
+ nom_table_sgbd = "cogifiee_eptb",
+ comm_source_en_plus = "Données chargées depuis le serveur RStudio du SDES pour les DREALs",
+ secret = TRUE,
+ user = "ecosql_stat",
+ serveur = Sys.getenv("server_ecosql")
+)
+
+
+#------------------------------------------------------------------------------------------------------------------
+#------------------------------------------------------------------------------------------------------------------
+# 2e partie, à éxécuter sur le PC de bureau (hors serveur SDES)
+#------------------------------------------------------------------------------------------------------------------
+#------------------------------------------------------------------------------------------------------------------
+
+# librairies ------
+library(dplyr)
+library(COGiter)
+library(datalibaba)
+library(googlesheets4)
-# chargement et calcul ----------
-# chargement des données EPTB (Enquete sur le prix des terrains à batir) --------
-# fichier cree a partir d une requete geokit 3 :
-# "Dossiers publics/geokit3/Regions Pays de la Loire/DREAL/INDICATEURS TER DATA LAB/gk3_eptb"
-eptb <-bind_rows(read_excel("extdata/gk3_eptb.xlsx",sheet=1),
- read_excel("extdata/gk3_eptb.xlsx",sheet=2),
- read_excel("extdata/gk3_eptb.xlsx",sheet=3)) %>%
- rename(date = 'eptb - Année',
- depcom = 'eptb - Code de la commune',
- nombre_d_observations_non_pondere='eptb - Nombre d observations (non pondéré)',
- nombre_de_maisons_terrain_achete_ou_non='eptb - Nombre de maisons (terrain acheté ou non)',
- prix_total_des_maisons_en_euros='eptb - Prix total des maisons (en euros)',
- surface_totale_des_maisons_en_m2='eptb - Surface totale des maisons (en m2)',
- nombre_de_terrains_achetes='eptb - Nombre de terrains achetés',
- prix_total_des_maisons_en_euros_avec_terrain_achete='eptb - Prix total des maisons (en euros) avec terrain achete',
- prix_total_des_terrains_en_euros='eptb - Prix total des terrains (en euros)',
- superficie_totale_des_terrains_achetes_en_m2='eptb - Superficie totale des terrains achetés (en m²)') %>%
- gather(variable,valeur,nombre_d_observations_non_pondere:superficie_totale_des_terrains_achetes_en_m2) %>%
- select(depcom,date,variable,valeur) %>%
- filter(date>=2008) %>%
- mutate(date=make_date(date,12,31))%>%
- mutate_if(is.character,as.factor) %>%
- complete(depcom,date,variable,fill = list(valeur =0)) %>%
- pivot_wider(names_from = variable,values_from = valeur) %>%
- filter(depcom != "97123")
-
-# versement dans le sgbd/datamart.portrait_territoires -------------
-poster_data(data = eptb,
- db = "datamart",
- schema = "portrait_territoires",
- table = "source_eptb",
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- pk = c("depcom", "date"), # déclaration d'une clé primaire sur la table postée : on ne doit pas avoir deux lignes avec à la fois le même code commune et la meme date
- user = "does")
-
-
-# METADONNEES------------------------------------
-
-## On récupère la liste des variables qui sont à documenter dans le tableur google sheet à partir du jeu de données posté
-var <- setdiff(names(eptb), c("depcom", "date"))
-
-## récupération du nom du présent script source pour filtrer ensuite le référentiel des indicateurs
-nom_script_sce <- rstudioapi::getActiveDocumentContext()$path %>% # utilisation de rstudioapi pour récupérer le nom du présent script
- basename() %>% # on enlève le chemin d'accès pour ne garder que le nom du fichier
- gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
-
-## authentification google sheet grâce au .Renviron
-gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
-gs4_deauth()
-
-
-
-## chargement du référentiel indicateurs google sheet
-metadata_indicateur <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "indicateurs") %>%
- # on ne garde que les variables concernées par le présent script de chargement
- filter(source == nom_script_sce) %>%
- # on ajoute l'unité dans le libellé de la variable
- mutate(libelle_variable = paste0(libelle_variable, " (unit\u00e9 : ", unite, ")")) %>%
- select(variable, libelle_variable) %>%
- # ajout des libellés pour depcom et date
- bind_rows(
- tribble(
- ~variable, ~libelle_variable,
- "depcom", "Code INSEE de la commune",
- "date", "Millesime"
- )
- )
-
-1## Vérification que la documentation des indicateurs est complète
-all(var %in% metadata_indicateur$variable) # doit renvoyer TRUE
-
-## Envoi des libellés de variable dans le SGBD
-post_dico_attr(dico = metadata_indicateur, table = "source_eptb", schema = "portrait_territoires",
- db = "datamart", user = "does")
-
-## Récupération des métadonnées de la source
-nom_sce <- str_replace(nom_script_sce, "chargement_|ref_|specifique_", "") %>%
- str_replace("indicateur_", "") %>%
- str_replace("_cogiter|_cog$", "")
+rm(list = ls())
+source("R/poster_documenter_ind.R")
-metadata_source <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "sources") %>%
- filter(source == nom_sce) %>%
- mutate(com_table = paste0(source_lib, " - ", producteur, ".\n", descriptif_sources)) %>%
- pull(com_table) %>%
- # ajout de complement sur la généalogie
- paste0(".\n", "Chargement des donn\u00e9es sur Geokit3")
+# récupération des données sur EcoSQL
+df_cogifie <- importer_data(db = "datamart",
+ schema = "portrait_territoires",
+ table = "cogifiee_eptb",
+ user = "ecosql_stat",
+ server = Sys.getenv("server_ecosql"))
-## commentaires de la table
+# process SGBD
+poster_documenter_ind(df = df_cogifie, nom_table_sgbd = "cogifiee_eptb",
+ comm_source_en_plus = "", nom_script_sce = "chargement_eptb", user = "does", serveur = NULL, secret = FALSE)
-commenter_table(comment = metadata_source,
- db = "datamart",
- schema = "portrait_territoires",
- table = "source_eptb",
- user = "does")
+rm(list=ls())
diff --git a/data-raw/chargement_lgt_rgp_insee.R b/data-raw/chargement_lgt_rgp_insee.R
index d704df4..3946add 100644
--- a/data-raw/chargement_lgt_rgp_insee.R
+++ b/data-raw/chargement_lgt_rgp_insee.R
@@ -17,14 +17,15 @@ rm(list=ls())
# chargement des données logement du dernier recensement INSEE ----------
+
# doc INSEE disponible à https://www.insee.fr/fr/statistiques/7631186?sommaire=7631713
-# Logement en 2020 - Recensement de la population - Base des principaux indicateurs
+# Logement en 2021 - Recensement de la population - Base des principaux indicateurs
# paramètres à saisir
-url <- "https://www.insee.fr/fr/statistiques/fichier/7631186/base-cc-logement-2020_xlsx.zip"
-mil <- c(2020, 2014, 2009) # mettre le millesime le plus récent en premier
+url <- "https://www.insee.fr/fr/statistiques/fichier/8202349/base-cc-logement-2021_xlsx.zip"
+mil <- c(2021, 2015, 2010) # mettre le millesime le plus récent en premier
mil_court <- substr(mil, 3,4)
-fic <- "base-cc-logement-2020"
+fic <- "base-cc-logement-2021"
# préfixe des variables à enlever (indique le millésime)
var_mil <- paste0("P", mil_court,"_")
@@ -73,7 +74,7 @@ setdiff(noms_var, lib_var$variable_deb) # rien attendu (character(0))
# calcul des variables ------
-lgt_rp <- bind_rows(lgt_rp_actu, lgt_rp_old, lgt_rp_very_old) %>%
+lgt_rp <- bind_rows(lgt_rp_actu, lgt_rp_old, lgt_rp_very_old) %>%
# mutate(RP_ACHTOT = coalesce(RP_ACHTT, RP_ACHTOT)) %>%
# select(-RP_ACHTT) %>%
select(date = annee, depcom = CODGEO, everything(), -REG, -DEP, -LIBGEO) %>%
diff --git a/data-raw/chargement_observatoire_artificialisation.R b/data-raw/chargement_observatoire_artificialisation.R
index 035ef78..43a64bb 100644
--- a/data-raw/chargement_observatoire_artificialisation.R
+++ b/data-raw/chargement_observatoire_artificialisation.R
@@ -35,36 +35,33 @@ rm(list = ls())
# # [91] "surfcom2021"
-# Les données de l'observatoire de l'artificialisation 2009-2022 ont été publiées en juillet 2023 :
+# Les données de l'observatoire de l'artificialisation 2009-2023 ont été publiées en octobre 2024 :
# https://cerema.app.box.com/v/pnb-action7-indicateurs-ff
-# un clic droit sur le répertoire "donnees nationales conso espaces" fait apparaître une popup de téléchargement
-# qui permet de récupérer le fichier "donnees nationales conso espaces.zip" qu'on range dans extdata et qu'on dézipe à la main
+# ouvrir le répertoire "donnees nationales conso espaces" fait apparaître
+# le fichier "conso2009_2023_resultats_com.csv" qu'on télécharge et range dans extdata
obs_artif_conso_com <- utils::read.csv(
- file = "extdata/donnees nationales conso espaces/obs_artif_conso_com_2009_2022.csv",
+ file = "extdata/conso2009_2023_resultats_com.csv",
header = TRUE,
sep = ";",
encoding = "UTF-8"
)
-# names(obs_artif_conso_com)
-# [1] "idcom" "idcomtxt" "idreg" "idregtxt" "iddep" "iddeptxt" "epci22" "epci22txt" "scot"
-# [10] "aav2020" "aav2020txt" "aav2020_typo" "naf09art10" "art09act10" "art09hab10" "art09mix10" "art09rou10" "art09fer10"
-# [19] "art09inc10" "naf10art11" "art10act11" "art10hab11" "art10mix11" "art10rou11" "art10fer11" "art10inc11" "naf11art12"
-# [28] "art11act12" "art11hab12" "art11mix12" "art11rou12" "art11fer12" "art11inc12" "naf12art13" "art12act13" "art12hab13"
-# [37] "art12mix13" "art12rou13" "art12fer13" "art12inc13" "naf13art14" "art13act14" "art13hab14" "art13mix14" "art13rou14"
-# [46] "art13fer14" "art13inc14" "naf14art15" "art14act15" "art14hab15" "art14mix15" "art14rou15" "art14fer15" "art14inc15"
-# [55] "naf15art16" "art15act16" "art15hab16" "art15mix16" "art15rou16" "art15fer16" "art15inc16" "naf16art17" "art16act17"
-# [64] "art16hab17" "art16mix17" "art16rou17" "art16fer17" "art16inc17" "naf17art18" "art17act18" "art17hab18" "art17mix18"
-# [73] "art17rou18" "art17fer18" "art17inc18" "naf18art19" "art18act19" "art18hab19" "art18mix19" "art18rou19" "art18fer19"
-# [82] "art18inc19" "naf19art20" "art19act20" "art19hab20" "art19mix20" "art19rou20" "art19fer20" "art19inc20" "naf20art21"
-# [91] "art20act21" "art20hab21" "art20mix21" "art20rou21" "art20fer21" "art20inc21" "naf21art22" "art21act22" "art21hab22"
-# [100] "art21mix22" "art21rou22" "art21fer22" "art21inc22" "naf09art22" "art09act22" "art09hab22" "art09mix22" "art09inc22"
-# [109] "art09rou22" "art09fer22" "artcom0922" "pop13" "pop19" "pop1319" "men13" "men19" "men1319"
-# [118] "emp13" "emp19" "emp1319" "mepart1319" "menhab1319" "artpop1319" "surfcom2022"
+# > names(observatoire_artificialisation)
+# [1] "idcom" "naf09art10" "art09act10" "art09hab10" "art09mix10" "art09rou10" "art09fer10" "art09inc10" "naf10art11"
+# [10] "art10act11" "art10hab11" "art10mix11" "art10rou11" "art10fer11" "art10inc11" "naf11art12" "art11act12" "art11hab12"
+# [19] "art11mix12" "art11rou12" "art11fer12" "art11inc12" "naf12art13" "art12act13" "art12hab13" "art12mix13" "art12rou13"
+# [28] "art12fer13" "art12inc13" "naf13art14" "art13act14" "art13hab14" "art13mix14" "art13rou14" "art13fer14" "art13inc14"
+# [37] "naf14art15" "art14act15" "art14hab15" "art14mix15" "art14rou15" "art14fer15" "art14inc15" "naf15art16" "art15act16"
+# [46] "art15hab16" "art15mix16" "art15rou16" "art15fer16" "art15inc16" "naf16art17" "art16act17" "art16hab17" "art16mix17"
+# [55] "art16rou17" "art16fer17" "art16inc17" "naf17art18" "art17act18" "art17hab18" "art17mix18" "art17rou18" "art17fer18"
+# [64] "art17inc18" "naf18art19" "art18act19" "art18hab19" "art18mix19" "art18rou19" "art18fer19" "art18inc19" "naf19art20"
+# [73] "art19act20" "art19hab20" "art19mix20" "art19rou20" "art19fer20" "art19inc20" "naf20art21" "art20act21" "art20hab21"
+# [82] "art20mix21" "art20rou21" "art20fer21" "art20inc21" "naf21art22" "art21act22" "art21hab22" "art21mix22" "art21rou22"
+# [91] "art21fer22" "art21inc22" "naf22art23" "art22act23" "art22hab23" "art22mix23" "art22rou23" "art22fer23" "art22inc23"
+# [100] "naf09art23" "art09act23" "art09hab23" "art09mix23" "art09inc23" "art09rou23" "art09fer23"
-
-# Au 22/02/2024 : pas de données plus récentes.
+# Au 13/03/2025 : pas de données plus récentes.
# calcul des indicateurs communaux -----------
@@ -77,8 +74,8 @@ observatoire_artificialisation <- obs_artif_conso_com %>%
iddeptxt,
# epci21,
# epci21txt,
- epci22,
- epci22txt,
+ epci23,
+ epci23txt,
scot,
aav2020,
aav2020txt,
@@ -135,13 +132,13 @@ observatoire_artificialisation <- obs_artif_conso_com %>%
# complétude de la liste des communes -------
-liste_COG_2023 <- COGiter::communes %>%
+liste_COG <- COGiter::communes %>%
dplyr::select(depcom = DEPCOM) %>%
dplyr::pull() %>%
- as.character() # 34945 obs
+ as.character() # 34935 obs
source_observatoire_artificialisation <- observatoire_artificialisation %>%
- dplyr::mutate(depcom = forcats::fct_expand(depcom, liste_COG_2023)) %>%
+ dplyr::mutate(depcom = forcats::fct_expand(depcom, liste_COG)) %>%
tidyr::complete(
depcom, date, fill = list(
flux_naf_artificialisation_activite = 0,
diff --git a/data-raw/chargement_pop_rgp_insee.R b/data-raw/chargement_pop_rgp_insee.R
index a258b33..a231dab 100644
--- a/data-raw/chargement_pop_rgp_insee.R
+++ b/data-raw/chargement_pop_rgp_insee.R
@@ -25,11 +25,11 @@ rm(list=ls())
# var_mil <- c(paste0("P", mil, "_"), paste0("C", mil, "_")) %>% gsub("20", "", .)
# url <- "https://www.insee.fr/fr/statistiques/fichier/6456153/base-ccx-evol-struct-pop-2019.zip"
# fic <- "base-cc-evol-struct-pop-2019"
-mil <- c(2020, 2014, 2009) # mettre le plus récent en premier
-mil2 <- c(20, 14, "09")
+mil <- c(2021, 2015, 2010) # mettre le plus récent en premier
+mil2 <- c(21, 15, "10")
var_mil <- c(paste0("P", mil2, "_"), paste0("C", mil2, "_"))
-url <- "https://www.insee.fr/fr/statistiques/fichier/7632446/base-cc-evol-struct-pop-2020_xlsx.zip"
-fic <- "base-cc-evol-struct-pop-2020"
+url <- "https://www.insee.fr/fr/statistiques/fichier/8201904/base-cc-evol-struct-pop-2021_xlsx.zip"
+fic <- "base-cc-evol-struct-pop-2021"
# chargement data ---------
@@ -57,7 +57,7 @@ pop_very_old <- read_xlsx(path = paste0("extdata/", fic,".xlsx"), sheet = paste0
# retrait des variables de l'exploitation complémentaire qui sont absentes de l'exploitation principale
nom_var_pop_very_old_a_oter <- setdiff(names(pop_very_old), names(pop_actu))
-pop_very_old <- pop_very_old %>%
+pop_very_old <- pop_very_old %>%
select(-all_of(nom_var_pop_very_old_a_oter))
diff --git a/data-raw/chargement_siclop.R b/data-raw/chargement_siclop.R
index c690e98..be712a9 100644
--- a/data-raw/chargement_siclop.R
+++ b/data-raw/chargement_siclop.R
@@ -5,8 +5,6 @@
# (Si COGiter est bien à jour sur le serveur de dataviz.......)
# scripts de collecte et nettoyage : https://gitlab-forge.din.developpement-durable.gouv.fr/dreal-pdl/csd/siclop/-/tree/main/scripts
-rm(list=ls())
-
## librairies ----------
library(dplyr)
library(tidyr)
@@ -86,27 +84,105 @@ data_prep <- function(typzon) {
nombre_annonces_appart_T4 = 0,
nombre_annonces_appart_T5 = 0,
`nombre_annonces_appart_T6 et +` = 0))
+
+ # total pour maisons
+ data_prep_indic4 <- data_prep_indic0 %>%
+ filter(type_logement == "maison") %>%
+ group_by(codezone, annee_annonce) %>%
+ summarise(nombre_annonces_maison = length(id_annonce),
+ loy_hc_moyen_maison = round(mean(loyer_hc, na.rm = T), 2),
+ surf_habitable_moyenne_maison = round(mean(surf_habitable, na.rm = T), 2),
+ loy_hc_mediane_maison = round(median(loyer_hc, na.rm = T), 2)
+ ) %>%
+ ungroup() %>%
+ complete(codezone, annee_annonce, fill = list(nombre_annonces_appart = 0))
+ # maisons par nombre de pieces
+ data_prep_indic5 <- data_prep_indic0 %>%
+ filter(type_logement == "maison") %>%
+ group_by(codezone, annee_annonce, nombre_pieces) %>%
+ summarise(nombre_annonces_maison = length(id_annonce),
+ loy_hc_moyen_maison = round(mean(loyer_hc, na.rm = T), 2),
+ surf_habitable_moyenne_maison = round(mean(surf_habitable, na.rm = T), 2),
+ loy_hc_mediane_maison = round(median(loyer_hc, na.rm = T), 2)) %>%
+ pivot_wider(names_from = nombre_pieces,
+ values_from = c(nombre_annonces_maison, loy_hc_moyen_maison, surf_habitable_moyenne_maison, loy_hc_mediane_maison)) %>%
+ ungroup() %>%
+ complete(codezone, annee_annonce, fill = list(nombre_annonces_maison_T1 = 0,
+ nombre_annonces_maison_T2 = 0,
+ nombre_annonces_maison_T3 = 0,
+ nombre_annonces_maison_T4 = 0,
+ nombre_annonces_maison_T5 = 0,
+ `nombre_annonces_maison_T6 et +` = 0))
+ # ensemble par nombre de pieces
+ data_prep_indic6 <- data_prep_indic0 %>%
+ group_by(codezone, annee_annonce, nombre_pieces) %>%
+ summarise(nombre_annonces_ensemble = length(id_annonce),
+ loy_hc_moyen_ensemble = round(mean(loyer_hc, na.rm = T), 2),
+ surf_habitable_moyenne_ensemble = round(mean(surf_habitable, na.rm = T), 2),
+ loy_hc_mediane_ensemble = round(median(loyer_hc, na.rm = T), 2)) %>%
+ pivot_wider(names_from = nombre_pieces,
+ values_from = c(nombre_annonces_ensemble, loy_hc_moyen_ensemble, surf_habitable_moyenne_ensemble, loy_hc_mediane_ensemble)) %>%
+ ungroup() %>%
+ complete(codezone, annee_annonce, fill = list(nombre_annonces_ensemble_T1 = 0,
+ nombre_annonces_ensemble_T2 = 0,
+ nombre_annonces_ensemble_T3 = 0,
+ nombre_annonces_ensemble_T4 = 0,
+ nombre_annonces_ensemble_T5 = 0,
+ `nombre_annonces_ensemble_T6 et +` = 0))
+
# regroupement des données
data_prep_indic <- data_prep_indic1 %>%
left_join(data_prep_indic2, by = c("codezone", "annee_annonce")) %>%
left_join(data_prep_indic3, by = c("codezone", "annee_annonce")) %>%
+ left_join(data_prep_indic4, by = c("codezone", "annee_annonce")) %>%
+ left_join(data_prep_indic5, by = c("codezone", "annee_annonce")) %>%
+ left_join(data_prep_indic6, by = c("codezone", "annee_annonce")) %>%
mutate(date = make_date(annee_annonce, 12, 31)) %>%
- rename(CodeZone = codezone, nombre_annonces_appart_T6_et_plus = "nombre_annonces_appart_T6 et +",
+ rename(CodeZone = codezone,
+ nombre_annonces_appart_T6_et_plus = "nombre_annonces_appart_T6 et +",
surf_habitable_moyenne_appart_T6_et_plus = "surf_habitable_moyenne_appart_T6 et +",
loy_hc_moyen_appart_T6_et_plus = "loy_hc_moyen_appart_T6 et +",
- loy_hc_mediane_appart_T6_et_plus = "loy_hc_mediane_appart_T6 et +") %>%
+ loy_hc_mediane_appart_T6_et_plus = "loy_hc_mediane_appart_T6 et +",
+ nombre_annonces_maison_T6_et_plus = "nombre_annonces_maison_T6 et +",
+ surf_habitable_moyenne_maison_T6_et_plus = "surf_habitable_moyenne_maison_T6 et +",
+ loy_hc_moyen_maison_T6_et_plus = "loy_hc_moyen_maison_T6 et +",
+ loy_hc_mediane_maison_T6_et_plus = "loy_hc_mediane_maison_T6 et +",
+ nombre_annonces_ensemble_T6_et_plus = "nombre_annonces_ensemble_T6 et +",
+ surf_habitable_moyenne_ensemble_T6_et_plus = "surf_habitable_moyenne_ensemble_T6 et +",
+ loy_hc_moyen_ensemble_T6_et_plus = "loy_hc_moyen_ensemble_T6 et +",
+ loy_hc_mediane_ensemble_T6_et_plus = "loy_hc_mediane_ensemble_T6 et +"
+ ) %>%
# mutate (TypeZone = colnames(siclop_exploitation[{zonag}]) ) %>%
mutate(TypeZone = zonag) %>%
- select(TypeZone, CodeZone, date, nombre_annonces_ensemble, nombre_annonces_appart, nombre_annonces_appart_T1,
- nombre_annonces_appart_T2, nombre_annonces_appart_T3, nombre_annonces_appart_T4,
- nombre_annonces_appart_T5, nombre_annonces_appart_T6_et_plus, surf_habitable_moyenne_ensemble,
- surf_habitable_moyenne_appart, surf_habitable_moyenne_appart_T1, surf_habitable_moyenne_appart_T2,
- surf_habitable_moyenne_appart_T3, surf_habitable_moyenne_appart_T4, surf_habitable_moyenne_appart_T5,
- surf_habitable_moyenne_appart_T6_et_plus, loy_hc_moyen_ensemble, loy_hc_moyen_appart, loy_hc_moyen_appart_T1,
- loy_hc_moyen_appart_T2, loy_hc_moyen_appart_T3, loy_hc_moyen_appart_T4, loy_hc_moyen_appart_T5,
- loy_hc_moyen_appart_T6_et_plus, loy_hc_mediane_ensemble, loy_hc_mediane_appart, loy_hc_mediane_appart_T1,
- loy_hc_mediane_appart_T2, loy_hc_mediane_appart_T3, loy_hc_mediane_appart_T4, loy_hc_mediane_appart_T5,
- loy_hc_mediane_appart_T6_et_plus
+ select(TypeZone, CodeZone, date,
+ nombre_annonces_ensemble, nombre_annonces_appart, nombre_annonces_maison,
+ nombre_annonces_ensemble_T1,nombre_annonces_ensemble_T2, nombre_annonces_ensemble_T3,
+ nombre_annonces_ensemble_T4,nombre_annonces_ensemble_T5, nombre_annonces_ensemble_T6_et_plus,
+ nombre_annonces_appart_T1,nombre_annonces_appart_T2, nombre_annonces_appart_T3,
+ nombre_annonces_appart_T4,nombre_annonces_appart_T5, nombre_annonces_appart_T6_et_plus,
+ nombre_annonces_maison_T1,nombre_annonces_maison_T2, nombre_annonces_maison_T3,
+ nombre_annonces_maison_T4,nombre_annonces_maison_T5, nombre_annonces_maison_T6_et_plus,
+ surf_habitable_moyenne_ensemble, surf_habitable_moyenne_appart,surf_habitable_moyenne_maison,
+ surf_habitable_moyenne_ensemble_T1, surf_habitable_moyenne_ensemble_T2,surf_habitable_moyenne_ensemble_T3,
+ surf_habitable_moyenne_ensemble_T4, surf_habitable_moyenne_ensemble_T5,surf_habitable_moyenne_ensemble_T6_et_plus,
+ surf_habitable_moyenne_appart_T1, surf_habitable_moyenne_appart_T2,surf_habitable_moyenne_appart_T3,
+ surf_habitable_moyenne_appart_T4, surf_habitable_moyenne_appart_T5,surf_habitable_moyenne_appart_T6_et_plus,
+ surf_habitable_moyenne_maison_T1, surf_habitable_moyenne_maison_T2,surf_habitable_moyenne_maison_T3,
+ surf_habitable_moyenne_maison_T4, surf_habitable_moyenne_maison_T5,surf_habitable_moyenne_maison_T6_et_plus,
+ loy_hc_moyen_ensemble,loy_hc_moyen_appart,loy_hc_moyen_maison,
+ loy_hc_moyen_ensemble_T1,loy_hc_moyen_ensemble_T2, loy_hc_moyen_ensemble_T3,
+ loy_hc_moyen_ensemble_T4, loy_hc_moyen_ensemble_T5,loy_hc_moyen_ensemble_T6_et_plus,
+ loy_hc_moyen_appart_T1,loy_hc_moyen_appart_T2, loy_hc_moyen_appart_T3,
+ loy_hc_moyen_appart_T4, loy_hc_moyen_appart_T5,loy_hc_moyen_appart_T6_et_plus,
+ loy_hc_moyen_maison_T1,loy_hc_moyen_maison_T2, loy_hc_moyen_maison_T3,
+ loy_hc_moyen_maison_T4, loy_hc_moyen_maison_T5,loy_hc_moyen_maison_T6_et_plus,
+ loy_hc_mediane_ensemble,loy_hc_mediane_appart,loy_hc_mediane_maison,
+ loy_hc_mediane_ensemble_T1,loy_hc_mediane_ensemble_T2, loy_hc_mediane_ensemble_T3,
+ loy_hc_mediane_ensemble_T4, loy_hc_mediane_ensemble_T5,loy_hc_mediane_ensemble_T6_et_plus,
+ loy_hc_mediane_appart_T1,loy_hc_mediane_appart_T2, loy_hc_mediane_appart_T3,
+ loy_hc_mediane_appart_T4, loy_hc_mediane_appart_T5,loy_hc_mediane_appart_T6_et_plus,
+ loy_hc_mediane_maison_T1,loy_hc_mediane_maison_T2, loy_hc_mediane_maison_T3,
+ loy_hc_mediane_maison_T4, loy_hc_mediane_maison_T5,loy_hc_mediane_maison_T6_et_plus
)
return(data_prep_indic)
}
diff --git a/data-raw/cogification_ecln.R b/data-raw/cogification_ecln.R
deleted file mode 100644
index 9fbb158..0000000
--- a/data-raw/cogification_ecln.R
+++ /dev/null
@@ -1,71 +0,0 @@
-
-# cogification_ecln
-
-
-# librairies ------
-library(DBI)
-library(RPostgreSQL)
-library(dplyr)
-library(COGiter)
-library(datalibaba)
-
-rm(list = ls())
-
-source_ecln <- importer_data(db = "datamart",
- schema = "portrait_territoires",
- table = "source_ecln")
-
-cogifiee_ecln<-cogifier(source_ecln %>% rename(DEPCOM=depcom))%>%
- mutate_if(is.factor,as.character)
-
-poster_data(data = cogifiee_ecln,
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_ecln",
- pk = c("TypeZone", "Zone", "CodeZone", "date"),
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- user = "does")
-
-# commentaires de la table et des variables -------------
-
-# récupération des commentaires de la table source
-dico_var <- get_table_comments(
- db = "datamart",
- schema = "portrait_territoires",
- table = "source_ecln",
- user = "does")
-
-# commentaire de la table
-comm_table <- filter(dico_var, is.na(nom_col)) %>%
- pull(commentaire) %>%
- gsub("\nCommentaire.*$", "", .)
-
-commenter_table(
- comment = comm_table,
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_ecln",
- user = "does"
-)
-
-# commentaire des variables
-comm_champ <- select(dico_var, nom_col, commentaire) %>%
- filter(!is.na(nom_col), nom_col != "depcom") %>%
- bind_rows(
- tribble(
- ~nom_col, ~commentaire,
- "TypeZone", "Type de territoire",
- "Zone", " Nom du territoire",
- "CodeZone", "Code INSEE du territoire"
- )
- )
-
-post_dico_attr(
- dico = comm_champ,
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_ecln",
- user = "does"
-)
diff --git a/data-raw/cogification_eptb.R b/data-raw/cogification_eptb.R
deleted file mode 100644
index a75f6c7..0000000
--- a/data-raw/cogification_eptb.R
+++ /dev/null
@@ -1,75 +0,0 @@
-
-# cogification_eptb
-
-
-# librairies ------
-library(DBI)
-library(RPostgreSQL)
-library(dplyr)
-library(COGiter)
-library(datalibaba)
-
-rm(list = ls())
-
-
-source_eptb <- importer_data(db = "datamart",
- schema = "portrait_territoires",
- table = "source_eptb")
-
-cogifiee_eptb<-cogifier(source_eptb %>% rename(DEPCOM=depcom))%>%
- mutate_if(is.factor,as.character)
-
-
-poster_data(data = cogifiee_eptb,
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_eptb",
- pk = c("TypeZone", "Zone", "CodeZone", "date"),
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- user = "does")
-
-
-# commentaires de la table et des variables -------------
-
-# récupération des commentaires de la table source
-dico_var <- get_table_comments(
- db = "datamart",
- schema = "portrait_territoires",
- table = "source_eptb",
- user = "does")
-
-# commentaire de la table
-comm_table <- filter(dico_var, is.na(nom_col)) %>%
- pull(commentaire) %>%
- gsub("\nCommentaire.*$", "", .)
-
-commenter_table(
- comment = comm_table,
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_eptb",
- user = "does"
-)
-
-# commentaire des variables
-comm_champ <- select(dico_var, nom_col, commentaire) %>%
- filter(!is.na(nom_col), nom_col != "depcom") %>%
- bind_rows(
- tribble(
- ~nom_col, ~commentaire,
- "TypeZone", "Type de territoire",
- "Zone", " Nom du territoire",
- "CodeZone", "Code INSEE du territoire"
- )
- )
-
-post_dico_attr(
- dico = comm_champ,
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_eptb",
- user = "does"
-)
-
diff --git a/data-raw/indicateur_ecln.R b/data-raw/indicateur_ecln.R
deleted file mode 100644
index 653df31..0000000
--- a/data-raw/indicateur_ecln.R
+++ /dev/null
@@ -1,113 +0,0 @@
-
-# indicateur_ecln
-
-# librairies ---------
-library(dplyr)
-library(tidyr)
-library(DBI)
-library(RPostgreSQL)
-library(datalibaba)
-library(googlesheets4)
-library(tidyverse)
-
-rm(list=ls())
-
-# chargement data -------------
-secretise_ecln <- importer_data(db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_ecln")
-
-
-# calcul -------
-indicateur_ecln<-secretise_ecln %>%
- mutate(cout_moyen_d_un_logement_vendu_par_les_promoteurs.collectif = round(prix_total_des_ventes.collectif / nb_logt_vendus.collectif,digits = 1)) %>%
- mutate(cout_moyen_d_un_logement_vendu_par_les_promoteurs.individuel = round(prix_total_des_ventes.individuel / nb_logt_vendus.individuel,digits = 1)) %>%
- mutate(cout_moyen_d_un_logement_vendu_par_les_promoteurs.total = round(prix_total_des_ventes.total / nb_logt_vendus.total,digits = 1)) %>%
-
- # mutate(cout_moyen_par_m2_de_SHON_d_un_logement_vendu_par_les_promoteurs.collectif = round(prix_total_des_ventes.collectif / surface_totale_des_ventes.collectif,digits = 1)) %>%
- # mutate(cout_moyen_par_m2_de_SHON_d_un_logement_vendu_par_les_promoteurs.individuel = round(prix_total_des_ventes.individuel / surface_totale_des_ventes.individuel,digits = 1))%>%
- # mutate(cout_moyen_par_m2_de_SHON_d_un_logement_vendu_par_les_promoteurs.total = round(prix_total_des_ventes.total / surface_totale_des_ventes.total,digits = 1))%>%
-
- mutate(cout_moyen_par_m2_de_SHON_logt_vendu_par_promoteurs.collectif = round(prix_total_des_ventes.collectif / surface_totale_des_ventes.collectif,digits = 1)) %>%
- mutate(cout_moyen_par_m2_de_SHON_logt_vendu_par_promoteurs.individuel = round(prix_total_des_ventes.individuel / surface_totale_des_ventes.individuel,digits = 1))%>%
- mutate(cout_moyen_par_m2_de_SHON_logt_vendu_par_promoteurs.total = round(prix_total_des_ventes.total / surface_totale_des_ventes.total,digits = 1))%>%
-
- select(-c(nb_logt_en_cours.collectif:`nb_logt_mis_en_vente.total`)) %>%
- mutate_if(is.factor, as.character)
-
-
-# versement dans le sgbd/datamart.portrait_territoires -------------
-poster_data(data = indicateur_ecln,
- db = "datamart",
- schema = "portrait_territoires",
- table = "indicateur_ecln",
- pk = c("TypeZone", "Zone", "CodeZone", "date"),
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- user = "does")
-
-# METADONNEES------------------------------------
-
-## On récupère la liste des variables qui sont à documenter dans le tableur google sheet à partir du jeu de données posté
-var <- setdiff(names(indicateur_ecln), c("TypeZone", "Zone" , "CodeZone" , "date"))
-
-## récupération du nom du présent script source pour filtrer ensuite le référentiel des indicateurs
-nom_script_sce <- rstudioapi::getActiveDocumentContext()$path %>% # utilisation de rstudioapi pour récupérer le nom du présent script
- basename() %>% # on enlève le chemin d'accès pour ne garder que le nom du fichier
- gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
-
-## authentification google sheet grâce au .Renviron
-gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
-gs4_deauth()
-
-
-
-## chargement du référentiel indicateurs google sheet
-metadata_indicateur <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "indicateurs") %>%
- # on ne garde que les variables concernées par le présent script de chargement
- filter(source == nom_script_sce) %>%
- # on ajoute l'unité dans le libellé de la variable
- mutate(libelle_variable = paste0(libelle_variable, " (unit\u00e9 : ", unite, ")")) %>%
- select(variable, libelle_variable) %>%
- # ajout des libellés pour depcom et date
- bind_rows(
- tribble(
- ~variable, ~libelle_variable,
- "TypeZone", "Type de territoire",
- "Zone", " Nom du territoire",
- "CodeZone", "Code INSEE du territoire",
- "date", "Millesime"
- )
- )
-
-## Vérification que la documentation des indicateurs est complète
-all(var %in% metadata_indicateur$variable) # doit renvoyer TRUE
-
-## Envoi des libellés de variable dans le SGBD
-post_dico_attr(dico = metadata_indicateur, table = "indicateur_ecln", schema = "portrait_territoires",
- db = "datamart", user = "does")
-
-## Récupération des métadonnées de la source
-nom_sce <- str_replace(nom_script_sce, "chargement_|ref_|specifique_", "") %>%
- str_replace("indicateur_", "") %>%
- str_replace("_cogiter|_cog$", "")
-
-metadata_source <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "sources") %>%
- filter(source == nom_sce) %>%
- mutate(com_table = paste0(source_lib, " - ", producteur, ".\n", descriptif_sources)) %>%
- pull(com_table) %>%
- # ajout de complement sur la généalogie
- paste0(".\n", "Chargement des donn\u00e9es sur Geokit3")
-
-## commentaires de la table
-
-commenter_table(comment = metadata_source,
- db = "datamart",
- schema = "portrait_territoires",
- table = "indicateur_ecln",
- user = "does")
-
-rm(list=ls())
diff --git a/data-raw/indicateur_eptb.R b/data-raw/indicateur_eptb.R
deleted file mode 100644
index 3545e75..0000000
--- a/data-raw/indicateur_eptb.R
+++ /dev/null
@@ -1,107 +0,0 @@
-
-# indicateur_eptb
-
-# librairies ---------
-library(googlesheets4)
-library(tidyverse)
-library(dplyr)
-library(tidyr)
-library(DBI)
-library(RPostgreSQL)
-library(datalibaba)
-
-rm(list=ls())
-
-
-secretise_eptb <- importer_data(db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_eptb")
-
-
-# calcul -----------
-indicateur_eptb<-secretise_eptb %>%
- mutate(cout_global_moyen_constructions_neuves_de_particuliers = round((prix_total_des_maisons_en_euros_avec_terrain_achete + prix_total_des_terrains_en_euros) /nombre_de_terrains_achetes ,digits = 1)) %>%
- mutate(cout_moyen_maison_par_m2_pour_constructions_neuves_individuelle = round(prix_total_des_maisons_en_euros / surface_totale_des_maisons_en_m2,digits = 1)) %>%
- mutate(cout_moyen_maison_pour_constructions_neuves_individuelles = round(prix_total_des_maisons_en_euros / nombre_de_maisons_terrain_achete_ou_non,digits = 1)) %>%
- mutate(cout_moyen_terrain_par_m2_pour_constructions_neuves_individuell = round(prix_total_des_terrains_en_euros / superficie_totale_des_terrains_achetes_en_m2,digits = 1))%>%
- mutate(cout_moyen_terrain_pour_constructions_neuves_individuelles = round(prix_total_des_terrains_en_euros / nombre_de_terrains_achetes,digits = 1)) %>%
- mutate(superficie_moyenne_des_terrains_achetes = round(superficie_totale_des_terrains_achetes_en_m2 / nombre_de_terrains_achetes,digits = 1)) %>%
- mutate(surface_moyenne_des_maisons_construites = round(surface_totale_des_maisons_en_m2 / nombre_de_maisons_terrain_achete_ou_non,digits = 1)) %>%
- mutate(part_du_terrain_dans_le_cout_total_du_projet = round(prix_total_des_terrains_en_euros / (prix_total_des_maisons_en_euros_avec_terrain_achete + prix_total_des_terrains_en_euros)*100,digits = 1)) %>%
- select(-c(nombre_d_observations_non_pondere:surface_totale_des_maisons_en_m2)) %>%
- mutate_if(is.factor, as.character)
-
-
-# versement dans le sgbd/datamart.portrait_territoires -------------
-poster_data(data = indicateur_eptb,
- db = "datamart",
- schema = "portrait_territoires",
- table = "indicateur_eptb",
- pk = c("TypeZone", "Zone", "CodeZone", "date"),
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- user = "does")
-
-# METADONNEES------------------------------------
-
-## On récupère la liste des variables qui sont à documenter dans le tableur google sheet à partir du jeu de données posté
-var <- setdiff(names(indicateur_eptb), c("TypeZone", "Zone" , "CodeZone" , "date"))
-
-## récupération du nom du présent script source pour filtrer ensuite le référentiel des indicateurs
-nom_script_sce <- rstudioapi::getActiveDocumentContext()$path %>% # utilisation de rstudioapi pour récupérer le nom du présent script
- basename() %>% # on enlève le chemin d'accès pour ne garder que le nom du fichier
- gsub(pattern = ".R$", "", .) # on enlève l'extension '.R'
-
-## authentification google sheet grâce au .Renviron
-gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
-gs4_deauth()
-
-
-
-## chargement du référentiel indicateurs google sheet
-metadata_indicateur <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "indicateurs") %>%
- # on ne garde que les variables concernées par le présent script de chargement
- filter(source == nom_script_sce) %>%
- # on ajoute l'unité dans le libellé de la variable
- mutate(libelle_variable = paste0(libelle_variable, " (unit\u00e9 : ", unite, ")")) %>%
- select(variable, libelle_variable) %>%
- # ajout des libellés pour depcom et date
- bind_rows(
- tribble(
- ~variable, ~libelle_variable,
- "TypeZone", "Type de territoire",
- "Zone", " Nom du territoire",
- "CodeZone", "Code INSEE du territoire",
- "date", "Millesime"
- )
- )
-
-## Vérification que la documentation des indicateurs est complète
-all(var %in% metadata_indicateur$variable) # doit renvoyer TRUE
-
-## Envoi des libellés de variable dans le SGBD
-post_dico_attr(dico = metadata_indicateur, table = "indicateur_eptb", schema = "portrait_territoires",
- db = "datamart", user = "does")
-
-## Récupération des métadonnées de la source
-nom_sce <- str_replace(nom_script_sce, "chargement_|ref_|specifique_", "") %>%
- str_replace("indicateur_", "") %>%
- str_replace("_cogiter|_cog$", "")
-
-metadata_source <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "sources") %>%
- filter(source == nom_sce) %>%
- mutate(com_table = paste0(source_lib, " - ", producteur, ".\n", descriptif_sources)) %>%
- pull(com_table) %>%
- # ajout de complement sur la généalogie
- paste0(".\n", "Chargement des donn\u00e9es sur Geokit3")
-
-## commentaires de la table
-
-commenter_table(comment = metadata_source,
- db = "datamart",
- schema = "portrait_territoires",
- table = "indicateur_eptb",
- user = "does")
diff --git a/data-raw/secret_ecln.R b/data-raw/secret_ecln.R
deleted file mode 100644
index 5bfe6c6..0000000
--- a/data-raw/secret_ecln.R
+++ /dev/null
@@ -1,302 +0,0 @@
-
-# secret_ecln
-
-# librairies -------------
-library(dplyr)
-library(tidyr)
-library(lubridate)
-library(COGiter)
-library(DBI)
-library(RPostgreSQL)
-library(datalibaba)
-library(googlesheets4)
-
-rm(list=ls())
-
-
-# chargement data -------------
-cogifiee_ecln <- importer_data(db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_ecln")
-
-data_cogifiee<-pivot_longer(cogifiee_ecln,
- cols = nb_logt_en_cours.collectif : surface_totale_des_ventes.total,
- names_to = "variable",
- values_to = "valeur") %>%
- mutate_if(is.character,as.factor)
-
-# tranches_ecln <- read.csv2('metadata/secret_ecln.csv', as.is = TRUE, encoding = "UTF-8" )
-
-## authentification google sheet grâce au .Renviron
-gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
-gs4_deauth()
-
-## chargement du référentiel indicateurs google sheet
-tranches_ecln <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "secret_ecln")
-
-
-data_ecln <- left_join(data_cogifiee, liste_zone, by= c("CodeZone","TypeZone","Zone")) %>%
- left_join(epci %>% select(EPCI,NOM_EPCI), by = "EPCI")
-
-
-# distinction des données à secrétiser ---------
-
-# listes des variables logt et promoteurs
-variables_logt <- tranches_ecln %>%
- pull(variable)
-variables_promoteurs <- tranches_ecln %>%
- pull(variable_associee) %>%
- unique()
-
-# table des nb promoteurs
-data_promoteurs <- data_ecln %>%
- filter (variable %in% variables_promoteurs) %>%
- rename(variable_associee=variable, nb_promoteurs=valeur)
-
-#table des données
-data_ecln_donnees <- data_ecln %>%
- filter (variable %in% variables_logt)
-
-data_ecln_donnees <- left_join(data_ecln_donnees, tranches_ecln, by= "variable")
-data_ecln_donnees <- left_join(data_ecln_donnees, data_promoteurs %>%
- select(TypeZone,CodeZone,date,variable_associee,nb_promoteurs),
- by= c("TypeZone","CodeZone","date", "variable_associee") )
-
-#liste des variables publiques ou non
-variables_publiques <- tranches_ecln %>%
- filter(secret=="0") %>%
- pull(variable)
-variables_non_publiques <- tranches_ecln %>%
- filter(secret !="0") %>%
- pull(variable)
-
-#table des données publiques
-data_ecln_A <- data_ecln_donnees %>%
- filter (variable %in% variables_publiques|
- TypeZone == "France" ) %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur) %>%
- mutate(valeur=as.character(valeur))
-
-#table des données à secrétiser
-data_ecln_B <- data_ecln_donnees %>%
- filter (variable %in% variables_non_publiques &
- TypeZone %in% c("Communes","Epci","Régions","Départements"))
-
-
-# secrétisation des données -----------
-secret_communes<-data_ecln_B %>%
- filter(TypeZone=="Communes")
-
-#secret induit, pour une meme variable et un meme epci, pour ne pas retrouver la valeur d'une commune
-#en faisant la somme des communes
-
-secret_communes<-secret_communes %>%
- group_by(variable,EPCI,date) %>%
- mutate(A_nb_inf3=length(which(nb_promoteurs<3))) %>% #compte combien inférieur à 3
- mutate(A_rang=rank(nb_promoteurs, ties.method = "first")) #classe pour repérer les 2 plus petites valeurs
-secret_communes$A_sec<-case_when(
- secret_communes$nb_promoteurs<3 ~ 1, #secret pour toutes les nb_promoteurs inférieurs à 3
- secret_communes$A_nb_inf3== 0 ~ 0, #enlève le secret si aucune des communes rang 1 et 2 inférieur à 3
- secret_communes$A_rang<3 ~ 1, #secret sur les 2 communes avec valeurs les plus basses
- TRUE ~ 0)
-
-# secret induit, pour une meme categorie et une meme commune, pour ne pas retrouver la valeur d'une variable
-# en faisant la somme des variables de la commune
-
-secret_communes<-secret_communes %>%
- group_by(categorie,Zone,date) %>%
- mutate(B_nb_inf3=length(which(nb_promoteurs<3))) %>% #compte combien inférieur à 3
- mutate(B_rang=rank(nb_promoteurs, ties.method = "first")) #classe pour repérer les 2 plus petites valeurs
-secret_communes$B_sec<-case_when(
- secret_communes$B_nb_inf3== 0 ~ 0, #enlève le secret si aucune des communes rang 1 et 2 inférieur à 11
- secret_communes$B_rang<3 ~ 1, #secret sur les 2 communes avec valeurs les plus basses
- TRUE ~ 0)
-
-# masque le collectif pour une commune dont l'individuel a été masqué par le secret induit
-secret_communes <- secret_communes %>%
- mutate (somme_secrets = A_sec + B_sec) %>%
- group_by(CodeZone,categorie,date) %>% #pour chaque commune
- mutate(a_secretiser=length(which(somme_secrets>0))) # secretise toutes les donnes groupees
-
-# regroupement des secrets, masquage des valeurs
-secret_communes<-secret_communes %>%
- mutate(valeur=as.character(valeur))
-secret_communes$valeur<-case_when(
- is.na(secret_communes$nb_promoteurs)~ "nc",
- secret_communes$a_secretiser > 0 ~ "nc", #remplace valeur par "nc" si secret stat
- TRUE ~ secret_communes$valeur)
-secret_communes<-secret_communes %>%
- ungroup() %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur)
-
-
-# secretisation des EPCI -----
-
-secret_epci<- data_ecln_B %>%
- filter(TypeZone =="Epci")
-
-# secret induit, pour une meme categorie et un meme epci, pour ne pas retrouver la valeur d'une variable
-# en faisant la somme des variables de l'Epci
-
-secret_epci<-secret_epci %>%
- group_by(categorie,CodeZone,date) %>%
- mutate(B_nb_inf3=length(which(nb_promoteurs<3))) %>% #compte combien inférieur à 3
- mutate(B_rang=rank(nb_promoteurs, ties.method = "first")) #classe pour repérer les 2 plus petites valeurs
-secret_epci$B_sec<-case_when(
- secret_epci$valeur<3 ~ 1, #secret pour toutes les valeurs inférieures à 3
- secret_epci$B_nb_inf3== 0 ~ 0, #enlève le secret si aucune des communes rang 1 et 2 inférieur à 3
- secret_epci$B_rang<3 ~ 1, #secret sur les 2 communes avec valeurs les plus basses
- TRUE ~ 0)
-
-# regroupement des secrets, masquage des valeurs
-secret_epci<-secret_epci %>%
- mutate(valeur=as.character(valeur))
-secret_epci$valeur<-case_when(
- is.na(secret_epci$nb_promoteurs)~ "nc",
- secret_epci$B_sec== 1 ~ "nc", #remplace valeur par "nc" si secret stat
- TRUE ~ secret_epci$valeur)
-secret_epci<-secret_epci %>%
- ungroup() %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur)
-
-
-# secretisation Départements et Régions -----
-
-secret_dep_reg<- data_ecln_B %>%
- filter(TypeZone %in% c("Régions","Départements"))
-
-
- #table des données publiques departement et regions
-public_dep_reg_A <- secret_dep_reg %>%
- filter (!(variable %in% c("prix_total_des_ventes.collectif","prix_total_des_ventes.individuel","prix_total_des_ventes.total" ))) %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur) %>%
- mutate(valeur=as.character(valeur))
-
- #table des données à secrétiser departement et regions
-secret_dep_reg_B <- secret_dep_reg %>%
- filter (variable %in% c("prix_total_des_ventes.collectif","prix_total_des_ventes.individuel","prix_total_des_ventes.total" ))
-
-#secret induit, pour une meme variable et une meme regioçn, pour ne pas retrouver la valeur d'un département
-#en faisant la somme des partements
-
-secret_dep_reg_B1<-secret_dep_reg_B %>%
- mutate(REG = as.character(REG)) %>%
- group_by(variable,REG,TypeZone,date) %>%
- mutate(A_nb_inf3=length(which(nb_promoteurs<3))) %>% #compte combien inférieur à 3
- mutate(A_rang=rank(nb_promoteurs, ties.method = "first")) #classe pour repérer les 2 plus petites valeurs
-secret_dep_reg_B1$A_sec<-case_when(
- secret_dep_reg_B1$nb_promoteurs<3 ~ 1, #secret pour toutes les nb_promoteurs inférieurs à 3
- secret_dep_reg_B1$A_nb_inf3== 0 ~ 0, #enlève le secret si aucun des departements rang 1 et 2 inférieur à 3
- secret_dep_reg_B1$A_rang<3 ~ 1, #secret sur les 2 DEPARTEMENTS avec valeurs les plus basses
- TRUE ~ 0)
-
- # secret induit, pour une meme categorie et un meme departement, pour ne pas retrouver la valeur d'une variable
- # en faisant la somme des variables du departement
-secret_dep1<-secret_dep_reg_B1 %>%
- ungroup() %>%
- group_by(categorie,TypeZone,CodeZone,date) %>% #groupe par categorie, zone et annee
- mutate(B_nb_inf3=length(which(nb_promoteurs<3))) # compte le nombre de fois ou nb promoteurs <3 par groupe
-secret_dep1$B_sec<-case_when(
- secret_dep1$B_nb_inf3== 0 ~ 0, #enleve secret (affiche 0) si le nb de fois où groupe <3 est égal à 0
- TRUE ~ 1) #pour les autres, code 1 (secret)
-# secret_dep1$C_sec<-case_when(
-# secret_dep1$B_nb_inf3== 0 ~ 0, #enleve secret (affiche 0) si le nb de fois où groupe <3 est égal à 0
-# TRUE ~ 1)
-
-# masque le collectif pour un dep dont l'individuel a été masqué par le secret induit
-secret_dep2 <- secret_dep1 %>%
- mutate (somme_secrets = A_sec + B_sec) %>%
- group_by(TypeZone,DEP,REG,categorie,date) %>% #pour chaque departement
- mutate(a_secretiser=length(which(somme_secrets>0))) # secretise toutes les donnes groupees
-
-
-
- # regroupement des secrets, masquage des valeurs
-secret_dep2<-secret_dep2 %>%
- mutate(valeur=as.character(valeur))
-secret_dep2$valeur<-case_when(
- is.na(secret_dep2$nb_promoteurs)~ "nc",
- secret_dep2$a_secretiser > 0 ~ "nc", #remplace valeur par "nc" si secret stat
- TRUE ~ secret_dep2$valeur)
-
-secret_dep2<-secret_dep2 %>%
- ungroup() %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur)
-
-# regroupement des zonages ------------
-secretise_ecln <-bind_rows(secret_communes,secret_epci,data_ecln_A,public_dep_reg_A,secret_dep2)
-
-
-# remplace "nc" par NA
-secretise_ecln$valeur<-na_if(secretise_ecln$valeur,"nc")
-
-secretise_ecln <- secretise_ecln %>%
- mutate(valeur=as.numeric(valeur)) %>%
- mutate_if(is.factor,as.character) %>%
- pivot_wider(names_from = variable,values_from = valeur)
-
-
-# versement dans le sgbd/datamart.portrait_territoires -------------
-poster_data(data = secretise_ecln,
- db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_ecln",
- pk = c("TypeZone", "Zone", "CodeZone", "date"),
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- user = "does")
-
-# commentaires de la table et des variables -------------
-
-# récupération des commentaires de la table source
-dico_var <- get_table_comments(
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_ecln",
- user = "does")
-
-# commentaire de la table
-comm_table <- filter(dico_var, is.na(nom_col)) %>%
- pull(commentaire) %>%
- gsub("\nCommentaire.*$", "", .)
-
-commenter_table(
- comment = comm_table,
- db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_ecln",
- user = "does"
-)
-
-## authentification google sheet grâce au .Renviron
-gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
-gs4_deauth()
-
-## chargement du référentiel indicateurs google sheet
-metadata_indicateur <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "indicateurs") %>%
- # on ne garde que les variables concernées par le présent script de chargement
- filter(source == "secret_ecln") %>%
- # on ajoute l'unité dans le libellé de la variable
- mutate(libelle_variable = paste0(libelle_variable, " (unit\u00e9 : ", unite, ")")) %>%
- select(variable, libelle_variable) %>%
- # ajout des libellés pour depcom et date
- bind_rows(
- tribble(
- ~variable, ~libelle_variable,
- "TypeZone", "Type de territoire",
- "Zone", " Nom du territoire",
- "CodeZone", "Code INSEE du territoire",
- "date", "Millesime"
- )
- )
-
-post_dico_attr(
- dico = metadata_indicateur,
- db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_ecln",
- user = "does"
-)
diff --git a/data-raw/secret_eptb.R b/data-raw/secret_eptb.R
deleted file mode 100644
index 5040836..0000000
--- a/data-raw/secret_eptb.R
+++ /dev/null
@@ -1,173 +0,0 @@
-
-# secret_eptb
-
-# librairies -----------
-library(dplyr)
-library(tidyr)
-library(lubridate)
-library(COGiter)
-library(stringr)
-library(DBI)
-library(RPostgreSQL)
-library(datalibaba)
-library(googlesheets4)
-
-rm(list=ls())
-
-
-# chargement data -------------
-
-cogifiee_eptb <- importer_data(db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_eptb")
-
-
-data_cogifiee<-pivot_longer(cogifiee_eptb,
- cols = nombre_d_observations_non_pondere : surface_totale_des_maisons_en_m2,
- names_to = "variable",
- values_to = "valeur")
-
-# tranches_eptb <- read.csv2('metadata/secret_eptb.csv', as.is = TRUE, encoding = "UTF-8" )
-
-## authentification google sheet grâce au .Renviron
-gs4_auth_configure(api_key = Sys.getenv("google_api_key"))
-gs4_deauth()
-
-## chargement du référentiel indicateurs google sheet
-tranches_eptb <- read_sheet("https://docs.google.com/spreadsheets/d/1n-dhtrJM3JwFVz5WSEGOQzQ8A0G7VT_VcxDe5gh6zSo/edit#gid=60292277",
- sheet = "secret_eptb")
-
-
-# distinction des données à secrétiser ---------
-
-# table des variables nombre
-data_nombres <- data_cogifiee %>%
- filter ( str_detect(variable,"^nombre")==TRUE)
-
-#table des variables prix et surfaces
-data_prix_surf <- data_cogifiee %>%
- filter ( str_detect(variable,"^nombre")==FALSE)
-
-# secrétisation des données nombre
-
-# Communes
-secret_communes<-data_nombres %>%
- filter(TypeZone=="Communes")%>%
- left_join(communes %>% rename("CodeZone"="DEPCOM")%>% select(CodeZone,EPCI)) #ajoute les noms d'epci
-
-#secret induit, pour une meme variable et un meme epci, pour ne pas retrouver la valeur d'une commune
-#en faisant la somme des communes
-secret_communes<-secret_communes %>%
- group_by(variable,EPCI,date) %>%
- mutate(A_nb_inf11=length(which(valeur<11)), #compte combien inférieur à 11
- A_rang=rank(valeur, ties.method = "first"), #classe pour repérer les 2 plus petites valeurs
- valeur2 = valeur)
-
-secret_communes<-secret_communes %>%
- mutate(valeur=as.character(valeur))
-
-secret_communes$valeur<-case_when(
- secret_communes$valeur2<11 ~ "nc", #secret pour toutes les nb_obs inférieurs à 11
- secret_communes$A_rang<3 ~ "nc", #secret sur les 2 communes avec valeurs les plus basses
- TRUE ~ secret_communes$valeur)
-
-secret_communes<-secret_communes %>%
- ungroup() %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur)
-
-# autres zones (pas de secret induit calculé)
-secret_grandes_zones<-data_nombres %>%
- filter(TypeZone %in% c("Epci","Départements","Régions","France")) %>%
- mutate(valeur2 = valeur,valeur=as.character(valeur))
-secret_grandes_zones$valeur <- case_when(
- secret_grandes_zones$valeur2 < 11 ~ "nc", #secret sur les 2 communes avec valeurs les plus basses
- TRUE ~ secret_grandes_zones$valeur)
-secret_grandes_zones<- secret_grandes_zones %>%
- ungroup() %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur)
-
-# regroupement des zonages
-data_nombres_secretise<-bind_rows(secret_communes,secret_grandes_zones) %>%
- mutate(code_ident=paste(CodeZone,date,variable)) #création d'une colonne pour identifier zone, année et variable
-
-
-# secrétisation des données prix et surface ----------
-data_prix_surf$variable_associe<- tranches_eptb$variable_associee[match(data_prix_surf$variable,tranches_eptb$variable)]
-
-data_prix_surf <- data_prix_surf %>%
- mutate(code_ident=paste(CodeZone,date,variable_associe)) #création d'une colonne pour identifier zone, année et variable
-data_prix_surf$valeur_associe <- data_nombres_secretise$valeur[match(data_prix_surf$code_ident,data_nombres_secretise$code_ident)]
-data_prix_surf<-data_prix_surf %>%
- mutate(valeur=as.character(valeur))
-data_prix_surf$valeur <- case_when(
- data_prix_surf$valeur_associe=="nc" ~ "nc", #secretise si la valeur associee est secretisee
- TRUE ~ data_prix_surf$valeur)
-data_prix_surf<-data_prix_surf %>%
- select(TypeZone,Zone,CodeZone,date,variable,valeur)
-
-
-# regroupement des donnees -----------
-data_eptb<-bind_rows(data_nombres_secretise %>% select(-code_ident),data_prix_surf)
-
-data_eptb$valeur<-na_if(data_eptb$valeur,"nc") #remplace "nc" par NA
-
-data_cogifiee <- data_eptb %>%
- mutate(valeur=as.numeric(valeur)) %>%
- mutate_if(is.factor,as.character)
-
-secretise_eptb<-data_cogifiee %>%
- pivot_wider(names_from = variable,values_from = valeur)
-
-
-# versement dans le sgbd/datamart.portrait_territoires -------------
-poster_data(data = secretise_eptb,
- db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_eptb",
- pk = c("TypeZone", "Zone", "CodeZone", "date"),
- post_row_name = FALSE,
- overwrite = TRUE,
- droits_schema = TRUE,
- user = "does")
-
-# commentaires de la table et des variables -------------
-
-# récupération des commentaires de la table source
-dico_var <- get_table_comments(
- db = "datamart",
- schema = "portrait_territoires",
- table = "cogifiee_eptb",
- user = "does")
-
-# commentaire de la table
-comm_table <- filter(dico_var, is.na(nom_col)) %>%
- pull(commentaire) %>%
- gsub("\nCommentaire.*$", "", .)
-
-commenter_table(
- comment = comm_table,
- db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_eptb",
- user = "does"
-)
-
-# commentaire des variables
-comm_champ <- select(dico_var, nom_col, commentaire) %>%
- filter(!is.na(nom_col), nom_col != "depcom") %>%
- bind_rows(
- tribble(
- ~nom_col, ~commentaire,
- "TypeZone", "Type de territoire",
- "Zone", " Nom du territoire",
- "CodeZone", "Code INSEE du territoire"
- )
- )
-
-post_dico_attr(
- dico = comm_champ,
- db = "datamart",
- schema = "portrait_territoires",
- table = "secretise_eptb",
- user = "does"
-)
diff --git a/metadata/Insee_RGP_lgt_lib_variable.ods b/metadata/Insee_RGP_lgt_lib_variable.ods
index ed0a1dea969d3f7ebec4926b8e88bbae7c560b54..494e382482bc73609c124152e59a2502594266e5 100644
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diff --git a/metadata/Insee_RGP_pop_lib_variable.ods b/metadata/Insee_RGP_pop_lib_variable.ods
index d32133f5e34df36f409d155c9234f25413998eb2..e74666e38bac88c48ab02e8468416b6f26b65ef7 100644
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