diff --git a/.gitignore b/.gitignore
index ebd418282458bf4e7f8e237c11f8ef79e3bf8a59..f68810f7aa82130304e6148940ddf2e79b5b9e1d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,4 @@ Meta
www
/doc/
/Meta/
+/tableurs/
diff --git a/data-raw/data_tableurs.R b/data-raw/data_tableurs.R
new file mode 100644
index 0000000000000000000000000000000000000000..465baad3fcc2e0380e7f3154205a85e31844d51b
--- /dev/null
+++ b/data-raw/data_tableurs.R
@@ -0,0 +1,422 @@
+
+library(dplyr)
+
+ocsge <- propre.artificialisation::ocsge
+observatoire_artificialisation <- propre.artificialisation::observatoire_artificialisation
+stock_artificialise <- propre.artificialisation::stock_artificialise
+population_legale <- propre.artificialisation::population_legale
+
+# parametres
+code_reg <- "52"
+millesime_ocsge <- 2016
+millesime_obs_artif <- 2020
+millesime_population <- 2019
+millesime_stock_artif <- 2020
+millesime_debut <- 2016 #millesime debut stock artificialisation (fixe)
+
+
+# carte 1_3 (source OCSGE) ---------------
+data <- ocsge %>%
+ dplyr::filter(grepl(millesime_ocsge, .data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::mutate(espace_artificialise_en_hectares=round(.data$espace_artificialise / 10000,0))
+
+xlsx::write.xlsx(data,file = "inst/tableurs/ocsge_1_3.xlsx")
+
+
+# graphe 1_6 (source OCSGE) -------------
+ocsge2 <- ocsge %>%
+ dplyr::mutate(date = lubridate::year(lubridate::as_date(.data$date)),
+ espace_naturel = .data$autre_surface_naturelle + .data$surface_en_eau + .data$surface_naturelle_boisee) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$date == millesime_ocsge) %>%
+ dplyr::select(.data$TypeZone, .data$Zone, .data$CodeZone,
+ .data$espace_naturel,
+ .data$espace_agricole, .data$espace_artificialise)
+data <- ocsge2 %>%
+ tidyr::pivot_longer(.data$espace_naturel:.data$espace_artificialise,
+ names_to = "variable",
+ values_to = "valeur") %>%
+ dplyr::group_by(.data$CodeZone) %>%
+ dplyr::mutate(variable = .data$variable,
+ valeur = .data$valeur / 10000,
+ taux = .data$valeur / sum(.data$valeur, na.rm = T) * 100) %>%
+ dplyr::mutate(variable = forcats::fct_relevel(variable,"espace_naturel","espace_agricole","espace_artificialise")) %>%
+ dplyr::ungroup()
+rm(ocsge2)
+
+xlsx::write.xlsx(data,file = "inst/tableurs/ocsge_1_6_et_1_7.xlsx")
+
+
+# source observatoire artificialisation 2_2_et_2_3_2_4_et_2_7---------
+# donnees observatoire artificialisation completes sur 10 ans
+# communes et epci region
+observatoire_artificialisation_0a <- observatoire_artificialisation %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$TypeZone == "Communes" | .data$TypeZone == "Epci")
+ # autres zones france
+observatoire_artificialisation_0b <- observatoire_artificialisation %>%
+ dplyr::filter(.data$TypeZone != "Communes" , .data$TypeZone != "Epci")
+# regroupement des donnees
+observatoire_artificialisation_0 <- dplyr::bind_rows(observatoire_artificialisation_0a,observatoire_artificialisation_0b) %>%
+ dplyr::mutate(date = as.character(lubridate::year(.data$date - 1))) %>%
+ dplyr::filter(.data$date < millesime_obs_artif, .data$date > millesime_obs_artif - 11) # conserve les 10 derniers millesimes
+rm(observatoire_artificialisation_0a,observatoire_artificialisation_0b)
+
+xlsx::write.xlsx(observatoire_artificialisation_0,file = "inst/tableurs/obs_artif_flux_par_annee_2_4.xlsx")
+
+# somme des flux 10 ans
+data <- observatoire_artificialisation_0 %>%
+ dplyr::select(.data$CodeZone, .data$TypeZone, .data$Zone, .data$date, .data$flux_naf_artificialisation_total) %>%
+ dplyr::mutate(flux_naf_artificialisation_total = .data$flux_naf_artificialisation_total / 10000) %>%
+ dplyr::arrange(.data$CodeZone) %>%
+ dplyr::group_by(.data$CodeZone, .data$TypeZone, .data$Zone) %>%
+ dplyr::summarise(`surf artificialisees 10 ans` = sum(.data$flux_naf_artificialisation_total, na.rm = T)) %>%
+ ungroup()
+
+xlsx::write.xlsx(data,file = "inst/tableurs/obs_artif_flux_10_ans_2_2_et_2_3_et_2_7.xlsx")
+rm(observatoire_artificialisation_0)
+
+
+# graphe 2_5 (source observatoire artificialisation) ---------
+
+data_dep_pdl <- observatoire_artificialisation %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$TypeZone == "D\u00e9partements") %>%
+ dplyr::arrange(.data$CodeZone) %>%
+ dplyr::mutate(Zone=factor(.data$Zone) %>% forcats::fct_inorder())%>%
+ dplyr::select(.data$TypeZone,
+ .data$CodeZone,
+ .data$Zone,
+ .data$date,
+ .data$flux_naf_artificialisation_activite,
+ .data$flux_naf_artificialisation_habitation,
+ .data$flux_naf_artificialisation_mixte) %>%
+ dplyr::mutate(Zone = forcats::fct_drop(.data$Zone)) %>%
+ dplyr::arrange(.data$Zone) %>%
+ dplyr::mutate(date = as.character(lubridate::year(.data$date - 1))) %>%
+ dplyr::filter(.data$date < millesime_obs_artif, .data$date > millesime_obs_artif - 11) %>% # conserve les 10 derniers millesimes
+ tidyr::gather(variable, valeur, .data$flux_naf_artificialisation_activite:.data$flux_naf_artificialisation_mixte) %>%
+ dplyr::mutate(valeur = .data$valeur / 10000) %>%
+ dplyr::mutate(
+ variable = replace(.data$variable, .data$variable == "flux_naf_artificialisation_activite", "activit\u00e9"),
+ variable = replace(.data$variable, .data$variable == "flux_naf_artificialisation_habitation", "habitation"),
+ variable = replace(.data$variable, .data$variable == "flux_naf_artificialisation_mixte", "mixte"))
+
+data_com_pdl <- observatoire_artificialisation %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$TypeZone == "Communes") %>%
+ dplyr::arrange(.data$CodeZone) %>%
+ dplyr::mutate(Zone=factor(.data$Zone) %>% forcats::fct_inorder())%>%
+ dplyr::select(.data$TypeZone,
+ .data$CodeZone,
+ .data$Zone,
+ .data$date,
+ .data$flux_naf_artificialisation_activite,
+ .data$flux_naf_artificialisation_habitation,
+ .data$flux_naf_artificialisation_mixte) %>%
+ dplyr::mutate(Zone = forcats::fct_drop(.data$Zone)) %>%
+ dplyr::arrange(.data$Zone) %>%
+ dplyr::mutate(date = as.character(lubridate::year(.data$date - 1))) %>%
+ dplyr::filter(.data$date < millesime_obs_artif, .data$date > millesime_obs_artif - 11) %>% # conserve les 10 derniers millesimes
+ tidyr::gather(variable, valeur, .data$flux_naf_artificialisation_activite:.data$flux_naf_artificialisation_mixte) %>%
+ dplyr::mutate(valeur = .data$valeur / 10000) %>%
+ dplyr::mutate(
+ variable = replace(.data$variable, .data$variable == "flux_naf_artificialisation_activite", "activit\u00e9"),
+ variable = replace(.data$variable, .data$variable == "flux_naf_artificialisation_habitation", "habitation"),
+ variable = replace(.data$variable, .data$variable == "flux_naf_artificialisation_mixte", "mixte"))
+
+data <- dplyr::bind_rows(data_com_pdl,data_dep_pdl)
+
+rm(data_com_pdl,data_dep_pdl)
+
+xlsx::write.xlsx(data,file = "inst/tableurs/obs_artif_2_5.xlsx")
+
+
+# graphe 2_6 (source stock artificialisé) ------------
+stock_artificialise_0 <- COGiter::filtrer_cog(stock_artificialise,
+ reg = code_reg)
+xlsx::write.xlsx(data,file = "inst/tableurs/stock_artif_origine.xlsx")
+
+data <- stock_artificialise_0 %>%
+ # dplyr::filter(.data$TypeZone == "Communes") %>%
+ dplyr::mutate(date = lubridate::year(.data$date)) %>%
+ dplyr::filter(.data$date == millesime_stock_artif | .data$date == millesime_debut) %>%
+ dplyr::select (-.data$surf_cadastree) %>%
+ dplyr::group_by(.data$CodeZone) %>%
+ dplyr::mutate(evolution = (.data$surface_artificialisee - dplyr::lag(.data$surface_artificialisee))*100/ dplyr::lag(.data$surface_artificialisee)) %>%
+ dplyr::ungroup() %>%
+ dplyr::arrange(.data$CodeZone)
+rm(stock_artificialise_0)
+
+xlsx::write.xlsx(data,file = "inst/tableurs/stock_artif_evolution_2_6_et_2_8_et_3_1.xlsx")
+
+
+# graphe 3_1 (sources population légale et stock artificialisé) ---------
+millesime_population_debut <- millesime_population - (millesime_stock_artif - millesime_debut)
+
+evol_popul <- population_legale %>%
+ dplyr::mutate(date = lubridate::year(.data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$date == millesime_population | .data$date == millesime_population_debut) %>%
+ dplyr::arrange(.data$TypeZone, .data$Zone, .data$CodeZone, .data$date) %>%
+ dplyr::group_by(.data$TypeZone, .data$Zone, .data$CodeZone) %>%
+ dplyr::mutate(evolution_population = round(.data$population_municipale * 100 / dplyr::lag(.data$population_municipale) - 100, 1)) %>%
+ dplyr::ungroup() %>%
+ dplyr::arrange(.data$CodeZone)
+
+xlsx::write.xlsx(evol_popul,file = "inst/tableurs/population_evolution_3_1.xlsx")
+
+rm(evol_popul)
+
+# graphe 3_3 (sources population légale et stock artificialisé) -----------------
+millesime_population_debut <- millesime_population - (millesime_stock_artif - millesime_debut)
+
+# seuils population
+pop_3 <- 40000
+pop_2 <- 10000
+pop_1 <- 2000
+
+# population du millesime
+population <- population_legale %>%
+ dplyr::mutate(date=lubridate::year(.data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$date == millesime_population,
+ .data$TypeZone =="Communes") %>%
+ dplyr::mutate(seuil_pop = dplyr::case_when(
+ .data$population_municipale > pop_3 ~ glue::glue("communes de plus\nde ",pop_3," habitants"),
+ .data$population_municipale > pop_2 ~ glue::glue("communes entre\n",pop_2," et ",pop_3, " habitants"),
+ .data$population_municipale > pop_1 ~ glue::glue("communes entre\n",pop_1," et ",pop_2, " habitants"),
+ .data$population_municipale > 0 ~ glue::glue("communes de moins\nde ",pop_1," habitants"),
+ TRUE ~ "")
+ ) %>%
+ dplyr::mutate(seuil_code = dplyr::case_when(
+ .data$population_municipale > pop_3 ~ "D",
+ .data$population_municipale > pop_2 ~ "C",
+ .data$population_municipale > pop_1 ~ "B",
+ .data$population_municipale > 0 ~ "A",
+ TRUE ~ "")
+ ) %>%
+ dplyr::rename ("population_n"="population_municipale") %>%
+ dplyr::select (-.data$date)
+
+# table des seuils
+seuil_population <- population %>%
+ dplyr::select (.data$CodeZone,.data$seuil_pop)
+
+
+# preparation des donnees evolution_artificialisation
+
+evol_artif <- stock_artificialise %>%
+ dplyr::mutate(date = lubridate::year(.data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$TypeZone == "Communes",
+ .data$date == millesime_stock_artif | .data$date == millesime_debut) %>%
+ dplyr::select (-.data$surf_cadastree)
+
+evol_artif1 <- evol_artif %>%
+ dplyr::left_join(seuil_population) %>%
+ dplyr::mutate(dep = substr(.data$CodeZone,1,2)) %>%
+ dplyr::filter (.data$date == millesime_stock_artif) %>%
+ dplyr::group_by(.data$dep, .data$seuil_pop, .data$date) %>%
+ dplyr::summarise(surface_artificialisee = sum(.data$surface_artificialisee, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::left_join(COGiter::departements %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::select(.data$DEP, .data$NOM_DEP),
+ by=c("dep"= "DEP")) %>%
+ dplyr::rename("Zone" = "NOM_DEP") %>%
+ dplyr::select(-.data$dep)
+
+evol_artif1_2 <- evol_artif1 %>%
+ dplyr::group_by(.data$seuil_pop,.data$date) %>%
+ dplyr::summarise(surface_artificialisee = sum(.data$surface_artificialisee, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::mutate(Zone = COGiter::regions %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::pull(.data$NOM_REG))
+
+evol_artif1 <-dplyr::bind_rows(evol_artif1,evol_artif1_2) %>%
+ dplyr::select(.data$seuil_pop, .data$Zone, .data$date, .data$surface_artificialisee)
+
+evol_artif2 <- evol_artif %>%
+ dplyr::left_join(seuil_population) %>%
+ dplyr::mutate(dep = substr(.data$CodeZone,1,2)) %>%
+ dplyr::filter (.data$date == millesime_debut) %>%
+ dplyr::group_by(.data$dep, .data$seuil_pop, .data$date) %>%
+ dplyr::summarise(surface_artificialisee = sum(.data$surface_artificialisee, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::left_join(COGiter::departements %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::select(.data$DEP, .data$NOM_DEP),
+ by=c("dep"= "DEP")) %>%
+ dplyr::rename("Zone" = "NOM_DEP") %>%
+ dplyr::select(-.data$dep)
+
+evol_artif2_2 <- evol_artif2 %>%
+ dplyr::group_by(.data$seuil_pop, .data$date) %>%
+ dplyr::summarise(surface_artificialisee = sum(.data$surface_artificialisee, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::mutate(Zone = COGiter::regions %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::pull(.data$NOM_REG))
+
+
+evol_artif2 <-dplyr::bind_rows(evol_artif2,evol_artif2_2) %>%
+ dplyr::select(.data$seuil_pop, .data$Zone, .data$date, .data$surface_artificialisee)
+
+evol_artif3 <- rbind(evol_artif1,evol_artif2) %>% #regroupement des annees
+ dplyr::arrange(.data$seuil_pop, .data$Zone, .data$date) %>%
+ dplyr::group_by(.data$seuil_pop, .data$Zone) %>%
+ dplyr::mutate(evolution_artificialisation = round(.data$surface_artificialisee * 100 / dplyr::lag(.data$surface_artificialisee) - 100, 1)) %>%
+ dplyr::ungroup() %>%
+ dplyr::filter(.data$date == millesime_stock_artif) %>%
+ dplyr::select(.data$seuil_pop, .data$Zone, .data$evolution_artificialisation)
+
+# preparation des donnees evol_population
+
+evol_popul <- population_legale %>%
+ dplyr::mutate(date = lubridate::year(.data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$TypeZone == "Communes",
+ .data$date == millesime_population | .data$date == millesime_population_debut)
+
+evol_popul1 <- evol_popul %>%
+ dplyr::left_join(seuil_population) %>%
+ dplyr::mutate(dep = substr(.data$CodeZone,1,2)) %>%
+ dplyr::filter (.data$date == millesime_population) %>%
+ dplyr::group_by(.data$dep, .data$seuil_pop, .data$date) %>%
+ dplyr::summarise(population_municipale = sum(.data$population_municipale, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::left_join(COGiter::departements %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::select(.data$DEP, .data$NOM_DEP),
+ by=c("dep"= "DEP")) %>%
+ dplyr::rename("Zone" = "NOM_DEP") %>%
+ dplyr::select(-.data$dep)
+
+evol_popul1_2 <- evol_popul1 %>%
+ dplyr::group_by(.data$seuil_pop, .data$date) %>%
+ dplyr::summarise(population_municipale = sum(.data$population_municipale, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::mutate(Zone = COGiter::regions %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::pull(.data$NOM_REG))
+
+evol_popul1 <-dplyr::bind_rows(evol_popul1,evol_popul1_2) %>%
+ dplyr::select(.data$seuil_pop, .data$Zone, .data$date, .data$population_municipale)
+
+evol_popul2 <- evol_popul %>%
+ dplyr::left_join(seuil_population) %>%
+ dplyr::mutate(dep = substr(.data$CodeZone,1,2)) %>%
+ dplyr::filter (.data$date == millesime_population_debut) %>%
+ dplyr::group_by(.data$dep, .data$seuil_pop, .data$date) %>%
+ dplyr::summarise(population_municipale = sum(.data$population_municipale, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::left_join(COGiter::departements %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::select(.data$DEP, .data$NOM_DEP),
+ by=c("dep"= "DEP")) %>%
+ dplyr::rename("Zone" = "NOM_DEP") %>%
+ dplyr::select(-.data$dep)
+
+evol_popul2_2 <- evol_popul2 %>%
+ dplyr::group_by(.data$seuil_pop, .data$date) %>%
+ dplyr::summarise(population_municipale = sum(.data$population_municipale, na.rm = T)) %>%
+ dplyr::ungroup() %>%
+ dplyr::mutate(Zone = COGiter::regions %>%
+ dplyr::filter (.data$REG == code_reg) %>%
+ dplyr::pull(.data$NOM_REG))
+
+evol_popul2 <-dplyr::bind_rows(evol_popul2,evol_popul2_2) %>%
+ dplyr::select(.data$seuil_pop, .data$Zone, .data$date, .data$population_municipale)
+
+evol_popul3 <- rbind(evol_popul1,evol_popul2) %>% #regroupement des annees
+ dplyr::arrange(.data$seuil_pop, .data$Zone, .data$date) %>%
+ dplyr::group_by(.data$seuil_pop, .data$Zone) %>%
+ dplyr::mutate(evolution_population = round(.data$population_municipale * 100 / dplyr::lag(.data$population_municipale) - 100, 1)) %>%
+ dplyr::ungroup() %>%
+ dplyr::filter(.data$date == millesime_population) %>%
+ dplyr::select(.data$seuil_pop, .data$Zone, .data$evolution_population)
+
+data <- evol_artif3 %>%
+ dplyr::full_join(evol_popul3) %>%
+ dplyr::mutate(`Seuil de population` = factor(.data$seuil_pop, levels = c(glue::glue("communes de moins\nde ",pop_1," habitants"),
+ glue::glue("communes entre\n",pop_1," et ",pop_2, " habitants"),
+ glue::glue("communes entre\n",pop_2," et ",pop_3, " habitants"),
+ glue::glue("communes de plus\nde ",pop_3," habitants")))) %>%
+ dplyr::select(`Seuil de population`,dplyr::everything(),-seuil_pop)
+
+rm(population,seuil_population)
+rm(evol_artif,evol_artif1,evol_artif1_2,evol_artif2,evol_artif2_2,evol_artif3)
+rm(evol_popul,evol_popul1,evol_popul1_2,evol_popul2,evol_popul2_2,evol_popul3)
+
+xlsx::write.xlsx(data,file = "inst/tableurs/stock_artif_population_3_3.xlsx")
+
+
+# graphe 3_4 (sources population légale et stock artificialisé) ------------
+# code_reg <- "52"
+# millesime_population <- 2019
+# millesime_stock_artif <- 2020
+# millesime_debut <- 2016
+millesime_debut_population <- millesime_population - (millesime_stock_artif - millesime_debut)
+millesime_fin_population <- millesime_population
+
+stock_depart <- stock_artificialise %>%
+ dplyr::mutate(date=lubridate::year(.data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$date == millesime_debut) %>%
+ dplyr::mutate(surface_artificialisee = .data$surface_artificialisee) %>%
+ dplyr::select (-.data$surf_cadastree,-.data$date) %>%
+ dplyr::rename ("stock_depart"="surface_artificialisee")
+
+# preparation des flux
+flux <- stock_artificialise %>%
+ dplyr::mutate(date=lubridate::year(.data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter((.data$date == millesime_debut) |(.data$date == millesime_stock_artif)) %>%
+ dplyr::select (-.data$surf_cadastree) %>%
+ dplyr::arrange(.data$TypeZone, .data$Zone, .data$CodeZone, .data$date) %>%
+ dplyr::group_by(.data$TypeZone, .data$Zone, .data$CodeZone) %>%
+ dplyr::mutate(flux_artificialisation = .data$surface_artificialisee - dplyr::lag(.data$surface_artificialisee) ) %>%
+ dplyr::ungroup() %>%
+ dplyr::filter(.data$date == millesime_stock_artif) %>%
+ dplyr::select (-.data$date , -.data$surface_artificialisee)
+
+# preparation des donnees population
+evol_popul <- population_legale %>%
+ dplyr::mutate(date=lubridate::year(.data$date)) %>%
+ COGiter::filtrer_cog(reg = code_reg) %>%
+ dplyr::filter(.data$date == millesime_debut_population | .data$date == millesime_fin_population)
+
+popul_debut <- evol_popul %>%
+ dplyr::filter(.data$date == millesime_debut_population) %>%
+ dplyr::rename ("population_debut"="population_municipale") %>%
+ dplyr::select (-.data$date)
+
+evol_popul <- evol_popul %>%
+ dplyr::filter(.data$date %in% c(millesime_debut_population,millesime_fin_population)) %>%
+ dplyr::arrange(.data$date) %>%
+ dplyr::group_by(.data$TypeZone,.data$Zone,.data$CodeZone) %>%
+ dplyr::mutate(evolution_population=.data$population_municipale - dplyr::lag(.data$population_municipale)) %>%
+ dplyr::ungroup() %>%
+ dplyr::filter(.data$date %in% c(millesime_fin_population)) %>%
+ dplyr::select(.data$TypeZone,.data$Zone,.data$CodeZone,.data$evolution_population)
+
+data <- stock_depart %>%
+ dplyr::left_join(flux) %>%
+ dplyr::left_join(popul_debut) %>%
+ dplyr::left_join(evol_popul) %>%
+ dplyr::mutate(surf_artif_par_hab_an=.data$stock_depart / .data$population_debut *10000 ) %>%
+ dplyr::mutate(surf_artif_par_nouv_hab =.data$flux_artificialisation / .data$evolution_population *10000) %>%
+ dplyr::select (.data$TypeZone,.data$CodeZone,.data$Zone,.data$surf_artif_par_hab_an,.data$surf_artif_par_nouv_hab) %>%
+ tidyr::gather(variable, valeur, 4:5) %>%
+ # dplyr::arrange(desc(.data$TypeZone),desc(.data$Zone)) %>%
+ dplyr::mutate(variable=forcats::fct_relevel(.data$variable,"surf_artif_par_nouv_hab","surf_artif_par_hab_an")) %>%
+ dplyr::mutate(Zone = forcats::fct_drop(.data$Zone) %>%
+ forcats::fct_inorder()) %>%
+ dplyr::mutate(valeur = ifelse(.data$valeur<0, 0, valeur))
+
+rm(stock_depart,flux,evol_popul,popul_debut)
+
+xlsx::write.xlsx(data,file = "inst/tableurs/stock_artif_population_3_4_et_4_2.xlsx")
diff --git a/data-raw/dataprep.R b/data-raw/dataprep.R
index 461ced94e971518faf54e00f10f0537906d8b39e..3d99eb930fb39bde51100771762d64a0e93408ec 100644
--- a/data-raw/dataprep.R
+++ b/data-raw/dataprep.R
@@ -56,7 +56,7 @@ teruti <- read.csv2("extdata/FDS_W0020_Moy-2017-2018-2019.csv", as.is = TRUE, en
# téléchargement des tables sur le SGBD -------------
-observatoire_artificialisation <- importer_data(db = "datamart",
+observatoire_artificialisation <- importer_data(db = "datamart", #surface en m2
schema = "portrait_territoires",
table = "cogifiee_observatoire_artificialisation")
@@ -68,7 +68,7 @@ population_legale <- importer_data(db = "datamart",
schema = "portrait_territoires",
table = "cogifiee_population_legale")
-ocsge <- importer_data(db = "datamart",
+ocsge <- importer_data(db = "datamart", #surface en m2
schema = "portrait_territoires",
table = "cogifiee_ocsge")
diff --git a/inst/tableurs/obs_artif_2_5.xlsx b/inst/tableurs/obs_artif_2_5.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..df77701e392ee5192a631a8eee57dbb950c302cd
Binary files /dev/null and b/inst/tableurs/obs_artif_2_5.xlsx differ
diff --git a/inst/tableurs/obs_artif_flux_10_ans_2_2_et_2_3_et_2_7.xlsx b/inst/tableurs/obs_artif_flux_10_ans_2_2_et_2_3_et_2_7.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..cb6690eba5f0ff43eebd1ecb55ab707a388119ed
Binary files /dev/null and b/inst/tableurs/obs_artif_flux_10_ans_2_2_et_2_3_et_2_7.xlsx differ
diff --git a/inst/tableurs/obs_artif_flux_par_annee_2_4.xlsx b/inst/tableurs/obs_artif_flux_par_annee_2_4.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..c5345c3efb70e34187d6dcb9783c9c13f44c4394
Binary files /dev/null and b/inst/tableurs/obs_artif_flux_par_annee_2_4.xlsx differ
diff --git a/inst/tableurs/ocsge_1_3.xlsx b/inst/tableurs/ocsge_1_3.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..e8bb7ab7e278032550405890c6fc2820a0452f73
Binary files /dev/null and b/inst/tableurs/ocsge_1_3.xlsx differ
diff --git a/inst/tableurs/ocsge_1_6_et_1_7.xlsx b/inst/tableurs/ocsge_1_6_et_1_7.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..14e48ae2fdccbd1e24a6af658378aa911df74f9e
Binary files /dev/null and b/inst/tableurs/ocsge_1_6_et_1_7.xlsx differ
diff --git a/inst/tableurs/population_evolution_3_1.xlsx b/inst/tableurs/population_evolution_3_1.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..dac79f127b4b8a939b83d9346f5d800c5471b1b7
Binary files /dev/null and b/inst/tableurs/population_evolution_3_1.xlsx differ
diff --git a/inst/tableurs/stock_artif_evolution_2_6_et_2_8_et_3_1.xlsx b/inst/tableurs/stock_artif_evolution_2_6_et_2_8_et_3_1.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..9d483353b6d8ad75ae66f88c334cc42957486b46
Binary files /dev/null and b/inst/tableurs/stock_artif_evolution_2_6_et_2_8_et_3_1.xlsx differ
diff --git a/inst/tableurs/stock_artif_origine.xlsx b/inst/tableurs/stock_artif_origine.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..fca9905d833cd9c90d8030563b488bb20dbbd717
Binary files /dev/null and b/inst/tableurs/stock_artif_origine.xlsx differ
diff --git a/inst/tableurs/stock_artif_population_3_3.xlsx b/inst/tableurs/stock_artif_population_3_3.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..f632607750f6134b678a42dd5c22569bd0cf95b5
Binary files /dev/null and b/inst/tableurs/stock_artif_population_3_3.xlsx differ
diff --git a/inst/tableurs/stock_artif_population_3_4_et_4_2.xlsx b/inst/tableurs/stock_artif_population_3_4_et_4_2.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..b76f3fe1a5ab4ab9a6b723f69ba72ae056a08f16
Binary files /dev/null and b/inst/tableurs/stock_artif_population_3_4_et_4_2.xlsx differ