diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index e7311ac86e1c3bb8caaa7c3f5f2e438f7d28b63c..fd5d66b3fcecba3b8501e22e8176909eb738bc8a 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,4 +1,4 @@
-image: rocker/geospatial:4.0.1
+image: rocker/geospatial:latest
 
 variables:
   R_LIBS_USER: "ci/lib"
@@ -17,11 +17,12 @@ stages:
 
 building:
   stage: build
+  allow_failure: true
   script:
     - mkdir -p -m777 $R_LIBS_USER
     - Rscript -e '.libPaths(c(Sys.getenv("R_LIBS_USER"), .libPaths()));remotes::install_deps(dependencies = TRUE, upgrade = "always")'
     - Rscript -e '.libPaths(c(Sys.getenv("R_LIBS_USER"), .libPaths()));remotes::install_cran(c("pkgdown", "DT"), upgrade = "always")'
-    - Rscript -e '.libPaths(c(Sys.getenv("R_LIBS_USER"), .libPaths()));devtools::check()'
+    - Rscript -e '.libPaths(c(Sys.getenv("R_LIBS_USER"), .libPaths()));devtools::check(error_on = "error")'
 
 # To have the coverage percentage appear as a gitlab badge follow these
 # instructions:
diff --git a/DESCRIPTION b/DESCRIPTION
index 939f2fe3463511e14b9d3ca68f2bf6e71ab02411..4c279225f97ac977f708db6efcebceeade1061c8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: propre.artificialisation
 Title: Publication template on land use
-Version: 0.0.0.9000
+Version: 0.0.0.9001
 Authors@R: c(
     person("Daniel", "Kalioudjoglou", , "daniel.kalioudjoglou@developpement-durable.gouv.fr", role = "aut"),
     person("Franck", "Gaspard", , "franck.gaspard@developpement-durable.gouv.fr", role = "aut"),
@@ -36,15 +36,14 @@ Imports:
     stringr,
     tibble,
     tidyr,
-    tricky,
-    utils
+    tricky
 Suggests: 
     rmarkdown,
     testthat (>= 3.0.0)
 VignetteBuilder: 
     knitr
 Remotes: 
-    gitlab::dreal-datalab/mapfactory,
+    git::https://gitlab-forge.din.developpement-durable.gouv.fr/dreal-pdl/csd/mapfactory.git,
     maeltheuliere/COGiter,
     pachevalier/tricky,
     spyrales/gouvdown
@@ -53,4 +52,4 @@ Encoding: UTF-8
 LazyData: true
 LazyDataCompression: bzip2
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
diff --git a/R/creer_carte_1_3.R b/R/creer_carte_1_3.R
index bfc4d21e220a9a64cf213df8bfccfec396ee1ccf..c705154dac41e3ce82a7660442f7c2b8673933ab 100644
--- a/R/creer_carte_1_3.R
+++ b/R/creer_carte_1_3.R
@@ -16,7 +16,7 @@
 #' @export
 #'
 #' @examples
-#' creer_carte_1_3(millesime_ocsge=2016, code_reg = '52')
+#' creer_carte_1_3(millesime_ocsge = 2016, code_reg = '52')
 
 
 creer_carte_1_3 <- function(millesime_ocsge,code_reg){
@@ -34,9 +34,9 @@ creer_carte_1_3 <- function(millesime_ocsge,code_reg){
   if (code_reg %in% c('52')) {
   data <- ocsge %>%
     dplyr::filter(grepl(millesime_ocsge, .data$date)) %>%
-    dplyr::select(.data$TypeZone,.data$Zone,.data$CodeZone,.data$date,.data$espace_artificialise) %>%
-    dplyr::mutate(valeur=round(.data$espace_artificialise / 10000,0)) %>%
-    dplyr::select(-.data$espace_artificialise)
+    dplyr::select("TypeZone","Zone","CodeZone","date","espace_artificialise") %>%
+    dplyr::mutate(valeur_ind = round(.data$espace_artificialise / 10000, 0)) %>%
+    dplyr::select(-"espace_artificialise")
 
   mois <- lubridate::month(data[1,"date"],label=TRUE)
 
@@ -50,7 +50,7 @@ creer_carte_1_3 <- function(millesime_ocsge,code_reg){
   carte_1_3 <- mapfactory::creer_carte_communes_prop(data = data,
                                                      code_region = code_reg,
                                                      carto = fond_carto,
-                                                     indicateur = valeur,
+                                                     indicateur = valeur_ind,
                                                      pourcent = FALSE,
                                                      decimales=0,
                                                      palette = "pal_gouv_h",
diff --git a/R/creer_carte_1_7.R b/R/creer_carte_1_7.R
index 18ada4c5d33d982c550fd36a036acff1b4705659..7aa912107f53ae01eaf168433c4881dc6234ad39 100644
--- a/R/creer_carte_1_7.R
+++ b/R/creer_carte_1_7.R
@@ -49,21 +49,21 @@ creer_carte_1_7 <- function(millesime_ocsge,
     dplyr::summarise(variable=.data$variable,valeur=.data$valeur / sum(.data$valeur,na.rm=T))  %>%
     dplyr::filter(.data$variable == "espace_artificialise") %>%
     dplyr::ungroup() %>%
-    dplyr::select(.data$TypeZone,.data$CodeZone,.data$Zone,.data$date,.data$valeur) %>%
-    dplyr::mutate(valeur=.data$valeur * 100)
+    dplyr::select("TypeZone","CodeZone","Zone","date","valeur") %>%
+    dplyr::mutate(valeur_ind =.data$valeur * 100)
 
   nom_region <- COGiter::regions %>%
-    dplyr::filter (.data$REG == code_reg) %>%
+    dplyr::filter(.data$REG == code_reg) %>%
     dplyr::mutate(NCCENR = as.character(forcats::fct_drop(.data$NCCENR))) %>%
     dplyr::pull(.data$NCCENR)
 
   fond_carto <- mapfactory::fond_carto(nom_reg = nom_region)
-  bins  <- stats::quantile(data$valeur,probs = c(0,0.1, 0.25, 0.5,0.75,0.9,1),na.rm=TRUE)
+  bins <- stats::quantile(data$valeur_ind, probs = c(0, 0.1, 0.25, 0.5, 0.75, 0.9, 1), na.rm=TRUE)
 
   carte_1_7 <- mapfactory::creer_carte_communes(data = data,
                                                 code_region=code_reg,
                                                 carto = fond_carto,
-                                                indicateur = valeur,
+                                                indicateur = valeur_ind,
                                                 bornes = bins,
                                                 pourcent = TRUE,
                                                 decimales = 1,
diff --git a/R/creer_carte_2_2.R b/R/creer_carte_2_2.R
index 5876088ef2ac2af4c30d348e2abd135ca9dc5bd7..19d3df9d5b00c26ad04d05502da6a4802c9326d1 100644
--- a/R/creer_carte_2_2.R
+++ b/R/creer_carte_2_2.R
@@ -30,7 +30,7 @@ creer_carte_2_2 <- function(millesime_obs_artif) {
   data <- observatoire_artificialisation %>%
     dplyr::filter(.data$TypeZone == "D\u00e9partements") %>%
     dplyr::filter(!(.data$CodeZone %in% c("971", "972", "973", "974", "975", "976"))) %>%
-    dplyr::select(.data$CodeZone, .data$TypeZone, .data$Zone, .data$date, .data$flux_naf_artificialisation_total) %>%
+    dplyr::select("CodeZone", "TypeZone", "Zone", "date", "flux_naf_artificialisation_total") %>%
     dplyr::mutate(flux_naf_artificialisation_total = .data$flux_naf_artificialisation_total / 10000) %>%
     dplyr::mutate(date = as.character(lubridate::year(.data$date - 1))) %>%
     dplyr::filter(.data$date < millesime_obs_artif, .data$date > millesime_obs_artif - 11) %>% # conserve les 10 derniers millesimes
@@ -42,7 +42,7 @@ creer_carte_2_2 <- function(millesime_obs_artif) {
   # creation de la carte
   monde_file <- system.file("maps", "countries_voisins-10m.gpkg", package = "mapfactory")
   monde <- sf::read_sf(monde_file) %>%
-    dplyr::select(.data$name)
+    dplyr::select("name")
 
   bbox_reg <- sf::st_bbox(sf::st_buffer(data, 50000))
 
diff --git a/R/creer_carte_2_7.R b/R/creer_carte_2_7.R
index 4aa04eb6c072da12c852d0b5e810de8a3de3600d..52f34b402d987b9fa838497b45b0d7d73a86d7bc 100644
--- a/R/creer_carte_2_7.R
+++ b/R/creer_carte_2_7.R
@@ -43,7 +43,7 @@ creer_carte_2_7 <- function(millesime_obs_artif= NULL, code_reg = NULL) {
                   .data$date >= millesime_obs_artif - 9) %>%
     dplyr::mutate(flux_naf_artificialisation_total = .data$flux_naf_artificialisation_total / 10000) %>%
     dplyr::group_by(.data$CodeZone, .data$TypeZone, .data$Zone) %>%
-    dplyr::summarise(valeur = sum(.data$flux_naf_artificialisation_total, na.rm = T)) %>%
+    dplyr::summarise(valeur_ind = sum(.data$flux_naf_artificialisation_total, na.rm = T)) %>%
     dplyr::ungroup()
 
   nom_region <- COGiter::regions %>%
@@ -54,13 +54,13 @@ creer_carte_2_7 <- function(millesime_obs_artif= NULL, code_reg = NULL) {
   fond_cartographique <- mapfactory::fond_carto(nom_reg = nom_region)
   # bins  <- stats::quantile(data$valeur,probs = c(0,0.1, 0.25, 0.5,0.75,0.9,1),na.rm=TRUE)
   # bins  <- stats::quantile(data$valeur,probs = c(0,0.2, 0.4, 0.6,0.8,1),na.rm=TRUE)
-  bins <- stats::quantile(data$valeur,probs = c(0, 0.25, 0.50, 0.75, 1), na.rm=TRUE)
+  bins <- stats::quantile(data$valeur_ind, probs = c(0, 0.25, 0.50, 0.75, 1), na.rm=TRUE)
 
 
   carte_2_7 <- mapfactory::creer_carte_communes(data = data,
                                                 code_region=code_reg,
                                                 carto = fond_cartographique,
-                                                indicateur = valeur,
+                                                indicateur = valeur_ind,
                                                 bornes = bins,
                                                 decimales = 1,
                                                 palette = "pal_gouv_h",
diff --git a/R/creer_carte_2_8.R b/R/creer_carte_2_8.R
index f4f52beb16c84d8a78c25ea4966e3b6133f97621..6845bb2bc2b70b2d2e41f92020dd28143d5bef12 100644
--- a/R/creer_carte_2_8.R
+++ b/R/creer_carte_2_8.R
@@ -21,8 +21,7 @@
 #' @examples
 #' creer_carte_2_8(millesime_stock_artif = 2020, code_reg = 52)
 #'
-creer_carte_2_8 <- function(millesime_stock_artif = NULL,
-                             code_reg = NULL) {
+creer_carte_2_8 <- function(millesime_stock_artif = NULL, code_reg = NULL) {
   attempt::stop_if(millesime_stock_artif, is.null, msg = "millesime_stock_artif n\'est pas renseign\u00e9")
   attempt::stop_if_not(millesime_stock_artif, is.numeric, msg = "millesime_stock_artif n\'est pas un nombre")
   attempt::stop_if(code_reg, is.null, msg = "code_reg n\'est pas renseign\u00e9")
@@ -44,31 +43,31 @@ creer_carte_2_8 <- function(millesime_stock_artif = NULL,
     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) %>%
+    dplyr::select(-"surf_cadastree") %>%
     dplyr::arrange(.data$TypeZone, .data$Zone, .data$CodeZone, .data$date) %>%
     dplyr::group_by(.data$TypeZone, .data$Zone, .data$CodeZone) %>%
-    dplyr::mutate(valeur = round(.data$surface_artificialisee * 100 / dplyr::lag(.data$surface_artificialisee) - 100, 1)) %>%
+    dplyr::mutate(valeur_ind = round(.data$surface_artificialisee * 100 / dplyr::lag(.data$surface_artificialisee) - 100, 1)) %>%
     dplyr::ungroup() %>%
     dplyr::filter(.data$date == millesime_stock_artif) %>%
-    dplyr::select(.data$TypeZone, .data$CodeZone, .data$Zone, .data$valeur)
+    dplyr::select("TypeZone", "CodeZone", "Zone", "valeur_ind")
 
   nom_region <- COGiter::regions %>%
-    dplyr::filter (.data$REG == code_reg) %>%
+    dplyr::filter(.data$REG == code_reg) %>%
     dplyr::mutate(NCCENR = as.character(forcats::fct_drop(.data$NCCENR))) %>%
-    dplyr::pull(.data$NCCENR)
+    dplyr::pull("NCCENR")
 
   fond_cartographique <- mapfactory::fond_carto(nom_reg = nom_region)
 
   # p <- c(0, 0.1, 0.25, 0.5, 0.75, 0.9, 1)
   # p <- c(0.30, 0.55, 0.75, 0.9, 1)
   p <- c(0.1, 0.25, 0.5, 0.75, 0.9)
-  bins  <- stats::quantile(data$valeur, probs = p, na.rm=TRUE)
+  bins  <- stats::quantile(data$valeur_ind, probs = p, na.rm=TRUE)
 
 
   carte_2_8 <- mapfactory::creer_carte_communes(data = data,
-                                                code_region=code_reg,
+                                                code_region = code_reg,
                                                 carto = fond_cartographique,
-                                                indicateur = valeur,
+                                                indicateur = valeur_ind,
                                                 bornes = bins,
                                                 decimales = 1,
                                                 palette = "pal_gouv_h",
diff --git a/R/creer_graphe_1_1.R b/R/creer_graphe_1_1.R
index 12f64c884cafb3f343f189f3e63620e869ba6586..4d00a1d2522b904ce14d627202b5dbab7d146bfa 100644
--- a/R/creer_graphe_1_1.R
+++ b/R/creer_graphe_1_1.R
@@ -63,7 +63,7 @@ creer_graphe_1_1 <- function(code_reg = NULL){
                      .data$codezone==code_reg~ 1,
                      TRUE ~ 0)
     ) %>%
-    dplyr::select(.data$zone,.data$taux_artificialisation,.data$part_dans_surface_nationale,.data$couleur_barre)
+    dplyr::select("zone", "taux_artificialisation", "part_dans_surface_nationale", "couleur_barre")
 
   valeur_max <- max(data$taux_artificialisation,na.rm=T)
 
diff --git a/R/creer_graphe_1_4.R b/R/creer_graphe_1_4.R
index beb472de0a9f2c7111b8dcf8ed6ce92d5a54ff36..3552eb08220a830a5fd3c33b2a083f2482b05f78 100644
--- a/R/creer_graphe_1_4.R
+++ b/R/creer_graphe_1_4.R
@@ -5,12 +5,12 @@
 #'
 #' @return Un diagramme en barres
 #'
-#' @importFrom dplyr filter select mutate group_by desc arrange case_when
+#' @importFrom dplyr filter select mutate group_by desc arrange case_when pull
 #' @importFrom COGiter filtrer_cog
 #' @importFrom forcats fct_inorder fct_drop
 #' @importFrom ggplot2 ggplot aes scale_y_continuous theme geom_text geom_col scale_fill_manual scale_x_discrete coord_flip
 #' @importFrom glue glue
-#' @importFrom lubridate make_date
+#' @importFrom lubridate make_date year
 #' @importFrom tidyr spread gather
 #' @importFrom tricky set_standard_names
 #' @importFrom ggtext element_markdown
@@ -25,10 +25,10 @@
 creer_graphe_1_4 <- function(code_reg){
 
   millesime_teruti <- teruti  %>%
-    dplyr::select(date) %>%
+    dplyr::select("date") %>%
     # unique() %>%
-    pull() %>%
-    year() %>%
+    dplyr::pull() %>%
+    lubridate::year() %>%
     max()
 
   attempt::stop_if(millesime_teruti, is.null, msg = "millesime_teruti n'est pas renseign\u00e9")
@@ -53,20 +53,20 @@ creer_graphe_1_4 <- function(code_reg){
     dplyr::arrange(.data$typezone,.data$zone) %>%
     dplyr::mutate(zone = forcats::fct_drop(.data$zone) %>% forcats::fct_inorder(),
                   voiries=.data$sols_revetus,
-                  hors_voiries=(.data$sols_batis+ .data$sols_stabilises+ .data$autres_sols_artificialises )
+                  hors_voiries=(.data$sols_batis + .data$sols_stabilises + .data$autres_sols_artificialises )
     ) %>%
-    dplyr::select(.data$typezone,.data$codezone,.data$zone,.data$voiries,.data$hors_voiries)%>%
-    tidyr::gather(variable,valeur,.data$voiries:.data$hors_voiries)%>%
-    dplyr::mutate(variable = replace(.data$variable, .data$variable=="hors_voiries","surfaces artificialis\u00e9es hors voiries"))%>%
-    dplyr::mutate(variable=factor(.data$variable,levels=c("surfaces artificialis\u00e9es hors voiries","voiries"))%>% forcats::fct_inorder()) %>%
+    dplyr::select("typezone", "codezone", "zone", "voiries", "hors_voiries")%>%
+    tidyr::gather("variable", "valeur", .data$voiries:.data$hors_voiries)%>%
+    dplyr::mutate(variable = replace(.data$variable, .data$variable == "hors_voiries", "surfaces artificialis\u00e9es hors voiries"))%>%
+    dplyr::mutate(variable = factor(.data$variable, levels = c("surfaces artificialis\u00e9es hors voiries","voiries")) %>% forcats::fct_inorder()) %>%
     dplyr::group_by(.data$typezone,.data$codezone,.data$zone) %>%
     dplyr::arrange(.data$codezone, dplyr::desc(.data$variable))%>%
     dplyr::mutate(position = cumsum(.data$valeur) - 0.5 * .data$valeur)
 
-  graph_1_4<-data  %>%
+  graph_1_4 <- data  %>%
     ggplot2::ggplot(ggplot2::aes(x=.data$zone,y=.data$valeur)) +
-    ggplot2::geom_col(ggplot2::aes(fill = .data$variable), width = 0.9)+
-    ggplot2::geom_text(ggplot2::aes(y = .data$position, label = mapfactory::format_fr(.data$valeur,0), group =.data$variable), color = "white", size=3)+
+    ggplot2::geom_col(ggplot2::aes(fill = .data$variable), width = 0.9) +
+    ggplot2::geom_text(ggplot2::aes(y = .data$position, label = mapfactory::format_fr(.data$valeur,0), group =.data$variable), color = "white", size=3) +
     ggplot2::labs(title= glue::glue("Etat des surfaces artificialis\u00e9es en {millesime_teruti} (en hectares)"),
                   subtitle=glue::glue("<span style = 'color:{couleur_hors_voirie}'> Hors voiries</span> et <span style = 'color:{couleur_voirie}'> en voiries</span>"),
                   x="",
@@ -74,11 +74,11 @@ creer_graphe_1_4 <- function(code_reg){
                   fill="",
                   caption = glue::glue("Source : Teruti-Lucas {millesime_teruti}"))+
     ggplot2::theme(legend.position = "none",
-                   plot.subtitle = ggtext::element_markdown(size = 12, lineheight = 1.2))+
-    ggplot2::scale_y_continuous(labels = scales::number_format(suffix = "", accuracy = 1)) +
+                   plot.subtitle = ggtext::element_markdown(size = 12, lineheight = 1.2)) +
+    ggplot2::scale_y_continuous(labels = scales::number_format(suffix = "",  accuracy = 1)) +
     ggplot2::coord_flip() +
-    ggplot2::scale_x_discrete(limits=rev) +
-    ggplot2::scale_fill_manual(values = c(couleur_voirie,couleur_hors_voirie))
+    ggplot2::scale_x_discrete(limits = rev) +
+    ggplot2::scale_fill_manual(values = c(couleur_voirie, couleur_hors_voirie))
 
   return(graph_1_4)
 
diff --git a/R/creer_graphe_1_5.R b/R/creer_graphe_1_5.R
index cf402155d18d4d67ca70ac4c460a84bef65e33a0..442c83c54b7da95c3c09f23a85ef199a5cf05ded 100644
--- a/R/creer_graphe_1_5.R
+++ b/R/creer_graphe_1_5.R
@@ -55,7 +55,7 @@ creer_graphe_1_5 <- function(code_reg){
                    voiries=(.data$sols_revetus )/.data$tous_sols*100,
                    hors_voiries=(.data$sols_batis+ .data$sols_stabilises+ .data$autres_sols_artificialises )/.data$tous_sols*100
     ) %>%
-    dplyr::select(.data$typezone,.data$codezone,.data$zone,.data$voiries,.data$hors_voiries)%>%
+    dplyr::select("typezone", "codezone", "zone", "voiries", "hors_voiries")%>%
     tidyr::gather(variable,valeur,.data$voiries:.data$hors_voiries)%>%
     dplyr::mutate(variable = replace(.data$variable, .data$variable=="hors_voiries","surfaces artificialis\u00e9es hors voiries"))%>%
     dplyr::mutate(variable=factor(.data$variable,levels=c("surfaces artificialis\u00e9es hors voiries","voiries"))%>% forcats::fct_inorder()) %>%
@@ -63,7 +63,7 @@ creer_graphe_1_5 <- function(code_reg){
     dplyr::arrange(.data$codezone, dplyr::desc(.data$variable)) %>%
     dplyr::mutate(position = cumsum(.data$valeur) - 0.5 * .data$valeur)
 
-  graph_1_5<-data  %>%
+  graph_1_5 <- data  %>%
     ggplot2::ggplot(ggplot2::aes(x=.data$zone,y=.data$valeur)) +
     ggplot2::geom_col(ggplot2::aes(fill = .data$variable), width = 0.9)+
     ggplot2::geom_text(ggplot2::aes(y = .data$position, label = mapfactory::format_fr(.data$valeur,1,pourcent = TRUE), group =.data$variable), color = "white", size=3)+
diff --git a/R/creer_graphe_1_6.R b/R/creer_graphe_1_6.R
index bffe0d1fb8d667bc0455720a2623d5b9c38e17d5..bcca7de8efe446599e92b4b3c38e41726ad0aff8 100644
--- a/R/creer_graphe_1_6.R
+++ b/R/creer_graphe_1_6.R
@@ -65,15 +65,15 @@ creer_graphe_1_6 <- function(millesime_ocsge = NULL, millesime_population = NULL
         TRUE ~ "")
         ) %>%
       dplyr::rename ("population_n"="population_municipale") %>%
-      dplyr::select (-.data$date)
+      dplyr::select(-"date")
 
     # table des seuils
     seuil_population <- population %>%
-      dplyr::select (.data$CodeZone,.data$seuil_pop)
+      dplyr::select("CodeZone", "seuil_pop")
 
     # population du millesime
     population_n <- population %>%
-      dplyr::select (.data$seuil_pop,.data$seuil_code, .data$population_n) %>%
+      dplyr::select("seuil_pop", "seuil_code", "population_n") %>%
       dplyr::group_by(.data$seuil_pop,.data$seuil_code) %>%
       dplyr::summarise(population_n = sum(.data$population_n, na.rm = T)) %>%
       dplyr::ungroup()
diff --git a/R/creer_graphe_2_3.R b/R/creer_graphe_2_3.R
index 721f8478fc3221c45a1ba9464df5633be52dc7bd..23037f45e904aa0bfaeb315991ba34cceb9ee313 100644
--- a/R/creer_graphe_2_3.R
+++ b/R/creer_graphe_2_3.R
@@ -31,18 +31,18 @@ creer_graphe_2_3 <- function(millesime_obs_artif,code_reg = NULL){
     dplyr::mutate(date = as.character(lubridate::year(.data$date - 1))) %>%
     dplyr::filter(.data$TypeZone == "R\u00e9gions",
                   !(.data$CodeZone %in% c("01","02","03","04","06"))) %>%
-    dplyr::select(.data$CodeZone, .data$TypeZone, .data$Zone, .data$date, .data$flux_naf_artificialisation_total) %>%
+    dplyr::select("CodeZone", "TypeZone", "Zone", "date", "flux_naf_artificialisation_total") %>%
     dplyr::mutate(flux_naf_artificialisation_total = .data$flux_naf_artificialisation_total / 10000) %>%
     dplyr::filter(.data$date < millesime_obs_artif, .data$date > millesime_obs_artif - 11) %>% # conserve les 10 derniers millesimes
     dplyr::arrange(.data$Zone) %>%
     dplyr::group_by(.data$CodeZone, .data$TypeZone, .data$Zone) %>%
     dplyr::summarise(`evolution` = sum(.data$flux_naf_artificialisation_total, na.rm = T)) %>%
-    dplyr::select(.data$TypeZone,.data$Zone,.data$CodeZone,.data$evolution) %>%
+    dplyr::select("TypeZone", "Zone", "CodeZone", "evolution") %>%
     dplyr::mutate(couleur_barre = dplyr::case_when(
-      .data$CodeZone==code_reg~ 1,
+      .data$CodeZone == code_reg ~ 1,
       TRUE ~ 0))
 
-  valeur_max <- max(data$evolution,na.rm=T)
+  valeur_max <- max(data$evolution, na.rm = TRUE)
   millesime_debut <- millesime_obs_artif - 10
 
   graph_2_3<-data  %>%
diff --git a/R/creer_graphe_2_4.R b/R/creer_graphe_2_4.R
index 3cbdfb52519854aa10149286d1315587e00678f5..66a67908060e5698c0325fc055a1f2b56353a064 100644
--- a/R/creer_graphe_2_4.R
+++ b/R/creer_graphe_2_4.R
@@ -42,7 +42,7 @@ creer_graphe_2_4 <- function(millesime_obs_artif = NULL, code_reg = NULL) {
     dplyr::filter(.data$TypeZone == "D\u00e9partements") %>%
     dplyr::arrange(.data$CodeZone) %>%
     dplyr::mutate(Zone=factor(.data$Zone) %>% forcats::fct_inorder())%>%
-    dplyr::select(.data$Zone, .data$date, .data$flux_naf_artificialisation_total) %>%
+    dplyr::select("Zone", "date", "flux_naf_artificialisation_total") %>%
     dplyr::mutate(
       Zone = forcats::fct_drop(.data$Zone),
       flux_naf_artificialisation_total = .data$flux_naf_artificialisation_total / 10000
diff --git a/R/creer_graphe_2_5.R b/R/creer_graphe_2_5.R
index d79b17e95fdf57c706be215d6a2424bfe3461029..e6f8d697b8bb01da9a9441ba34c0f259d892b48d 100644
--- a/R/creer_graphe_2_5.R
+++ b/R/creer_graphe_2_5.R
@@ -41,7 +41,7 @@ creer_graphe_2_5 <- function(millesime_obs_artif=NULL, code_reg = NULL) {
     dplyr::filter(.data$TypeZone == "D\u00e9partements") %>%
     dplyr::arrange(.data$CodeZone) %>%
     dplyr::mutate(Zone=factor(.data$Zone) %>% forcats::fct_inorder())%>%
-    dplyr::select(.data$Zone, .data$date, .data$flux_naf_artificialisation_activite, .data$flux_naf_artificialisation_habitation, .data$flux_naf_artificialisation_mixte) %>%
+    dplyr::select("Zone", "date", "flux_naf_artificialisation_activite", "flux_naf_artificialisation_habitation", "flux_naf_artificialisation_mixte") %>%
     dplyr::mutate(Zone = forcats::fct_drop(.data$Zone)) %>%
     dplyr::arrange(.data$Zone) %>%
     dplyr::mutate(date = as.character(lubridate::year(.data$date - 1))) %>%
diff --git a/R/creer_graphe_2_6.R b/R/creer_graphe_2_6.R
index 195ecc3bc772eed567184c54beb248d1a207b9b3..143f24f3e957cc5290d88ab37d3b50404a4f1732 100644
--- a/R/creer_graphe_2_6.R
+++ b/R/creer_graphe_2_6.R
@@ -67,15 +67,15 @@ creer_graphe_2_6 <- function(millesime_stock_artif = NULL, millesime_population
       TRUE ~ "")
     ) %>%
     dplyr::rename ("population_n"="population_municipale") %>%
-    dplyr::select (-.data$date)
+    dplyr::select(-"date")
 
   # table des seuils
   seuil_population <- population %>%
-    dplyr::select (.data$CodeZone, .data$seuil_pop)
+    dplyr::select ("CodeZone", "seuil_pop")
 
   # population du millesime
   population_n <- population %>%
-    dplyr::select (.data$seuil_pop, .data$seuil_code, .data$population_n) %>%
+    dplyr::select("seuil_pop", "seuil_code", "population_n") %>%
     dplyr::group_by(.data$seuil_pop, .data$seuil_code) %>%
     dplyr::summarise(population_n = sum(.data$population_n, na.rm = T)) %>%
     dplyr::ungroup()
@@ -89,7 +89,7 @@ creer_graphe_2_6 <- function(millesime_stock_artif = NULL, millesime_population
     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$surf_cadastree) %>%
+    dplyr::select(-"surf_cadastree") %>%
     dplyr::group_by(.data$seuil_pop,.data$date) %>%
     dplyr::summarise(surface_artificialisee = sum(.data$surface_artificialisee, na.rm = T)) %>%
     dplyr::ungroup() %>%
@@ -102,7 +102,7 @@ creer_graphe_2_6 <- function(millesime_stock_artif = NULL, millesime_population
                                                                  glue::glue("communes entre\n",pop_2," et ",pop_3, " habitants"),
                                                                  glue::glue("communes de plus\nde ",pop_3," habitants"))) %>%
                     forcats::fct_rev()) %>%
-    dplyr::select(.data$seuil_pop,.data$evolution)
+    dplyr::select("seuil_pop", "evolution")
 
 
   valeur_max <- max(data$evolution, na.rm = T)
diff --git a/R/creer_graphe_3_1.R b/R/creer_graphe_3_1.R
index 6c74c4d529a34e34df88a4d7f193cbb93be24546..3311cf10e0cc71fc344a7ab301e6c4cdf1690155 100644
--- a/R/creer_graphe_3_1.R
+++ b/R/creer_graphe_3_1.R
@@ -53,13 +53,13 @@ creer_graphe_3_1 <- function(millesime_stock_artif = NULL,
     COGiter::filtrer_cog(reg = code_reg) %>%
     dplyr::filter(.data$TypeZone %in% c("France","R\u00e9gions", "D\u00e9partements"),
            .data$date == millesime_stock_artif | .data$date == millesime_debut) %>%
-    dplyr::select (-.data$surf_cadastree) %>%
+    dplyr::select(-"surf_cadastree") %>%
     dplyr::arrange(.data$TypeZone, .data$Zone, .data$CodeZone, .data$date) %>%
     dplyr::group_by(.data$TypeZone, .data$Zone, .data$CodeZone) %>%
     dplyr::mutate(evolution_artificialisation = round(.data$surface_artificialisee * 100 / dplyr::lag(.data$surface_artificialisee) - 100, 1)) %>%
     dplyr::ungroup() %>%
     dplyr::filter(.data$date == millesime_stock_artif) %>%
-    dplyr::select(.data$TypeZone, .data$CodeZone, .data$Zone, .data$evolution_artificialisation)
+    dplyr::select("TypeZone", "CodeZone", "Zone", "evolution_artificialisation")
 
 
   evol_popul <- population_legale %>%
@@ -72,7 +72,7 @@ creer_graphe_3_1 <- function(millesime_stock_artif = NULL,
     dplyr::mutate(evolution_population = round(.data$population_municipale * 100 / dplyr::lag(.data$population_municipale) - 100, 1)) %>%
     dplyr::ungroup() %>%
     dplyr::filter(.data$date == millesime_population) %>%
-    dplyr::select(.data$TypeZone, .data$CodeZone, .data$Zone, .data$evolution_population)
+    dplyr::select("TypeZone", "CodeZone", "Zone", "evolution_population")
 
 
   data <- dplyr::full_join(evol_artif, evol_popul) %>%
diff --git a/R/creer_graphe_3_3.R b/R/creer_graphe_3_3.R
index 9e533789977537fc886cd55041c933d09b26fb3f..e720a5487036027d3f5f6ec56eda94b9bd43dc60 100644
--- a/R/creer_graphe_3_3.R
+++ b/R/creer_graphe_3_3.R
@@ -68,16 +68,16 @@ creer_graphe_3_3 <- function(millesime_stock_artif = NULL, millesime_population
       TRUE ~ "")
       ) %>%
     dplyr::rename ("population_n"="population_municipale") %>%
-    dplyr::select (-.data$date)
+    dplyr::select(-"date")
 
   # table des seuils
   seuil_population <- population %>%
-    dplyr::select (.data$CodeZone,.data$seuil_pop)
+    dplyr::select("CodeZone", "seuil_pop")
 
   # population du millesime
   population_n <- population %>%
-    dplyr::select (.data$seuil_pop,.data$seuil_code, .data$population_n) %>%
-    dplyr::group_by(.data$seuil_pop,.data$seuil_code) %>%
+    dplyr::select("seuil_pop","seuil_code", "population_n") %>%
+    dplyr::group_by(.data$seuil_pop, .data$seuil_code) %>%
     dplyr::summarise(population_n = sum(.data$population_n, na.rm = T)) %>%
     dplyr::ungroup()
 
@@ -88,22 +88,22 @@ 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$surf_cadastree)
+                  .data$date == millesime_stock_artif | .data$date == millesime_debut) %>%
+    dplyr::select(-"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::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),
+                     dplyr::select("DEP", "NOM_DEP"),
                    by=c("dep"= "DEP")) %>%
-  dplyr::rename("Zone" = "NOM_DEP") %>%
-  dplyr::select(-.data$dep)
+  dplyr::rename(Zone = "NOM_DEP") %>%
+  dplyr::select(-"dep")
 
 evol_artif1_2 <- evol_artif1 %>%
   dplyr::group_by(.data$seuil_pop,.data$date) %>%
@@ -113,8 +113,8 @@ evol_artif1_2 <- evol_artif1 %>%
                   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_artif1 <- dplyr::bind_rows(evol_artif1,evol_artif1_2) %>%
+  dplyr::select("seuil_pop", "Zone", "date", "surface_artificialisee")
 
 evol_artif2 <- evol_artif %>%
   dplyr::left_join(seuil_population) %>%
@@ -125,10 +125,10 @@ evol_artif2 <- evol_artif %>%
   dplyr::ungroup() %>%
   dplyr::left_join(COGiter::departements %>%
                      dplyr::filter (.data$REG == code_reg) %>%
-                     dplyr::select(.data$DEP, .data$NOM_DEP),
-                   by=c("dep"= "DEP")) %>%
+                     dplyr::select("DEP", "NOM_DEP"),
+                   by = c("dep"= "DEP")) %>%
   dplyr::rename("Zone" = "NOM_DEP") %>%
-  dplyr::select(-.data$dep)
+  dplyr::select(-"dep")
 
 evol_artif2_2 <- evol_artif2 %>%
   dplyr::group_by(.data$seuil_pop, .data$date) %>%
@@ -138,8 +138,8 @@ evol_artif2_2 <- evol_artif2 %>%
                   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_artif2 <-dplyr::bind_rows(evol_artif2, evol_artif2_2) %>%
+  dplyr::select("seuil_pop", "Zone", "date", "surface_artificialisee")
 
 evol_artif3 <- rbind(evol_artif1,evol_artif2) %>%  #regroupement des annees
   dplyr::arrange(.data$seuil_pop, .data$Zone, .data$date) %>%
@@ -147,7 +147,7 @@ evol_artif3 <- rbind(evol_artif1,evol_artif2) %>%  #regroupement des annees
   dplyr::mutate(evolution_artificialisation = round(.data$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)
+  dplyr::select("seuil_pop", "Zone", "evolution_artificialisation")
 
 # preparation des donnees evol_population
 
@@ -166,21 +166,21 @@ evol_popul1 <- evol_popul %>%
   dplyr::ungroup() %>%
   dplyr::left_join(COGiter::departements %>%
                      dplyr::filter (.data$REG == code_reg) %>%
-                     dplyr::select(.data$DEP, .data$NOM_DEP),
+                     dplyr::select("DEP", "NOM_DEP"),
                    by=c("dep"= "DEP")) %>%
   dplyr::rename("Zone" = "NOM_DEP") %>%
-  dplyr::select(-.data$dep)
+  dplyr::select(-"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$NOM_REG))
+                  dplyr::filter(.data$REG == code_reg) %>%
+                  dplyr::pull("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_popul1 <-dplyr::bind_rows(evol_popul1, evol_popul1_2) %>%
+  dplyr::select("seuil_pop", "Zone", "date", "population_municipale")
 
 evol_popul2 <- evol_popul %>%
   dplyr::left_join(seuil_population) %>%
@@ -191,10 +191,10 @@ evol_popul2 <- evol_popul %>%
   dplyr::ungroup() %>%
   dplyr::left_join(COGiter::departements %>%
                      dplyr::filter (.data$REG == code_reg) %>%
-                     dplyr::select(.data$DEP, .data$NOM_DEP),
+                     dplyr::select("DEP", "NOM_DEP"),
                    by=c("dep"= "DEP")) %>%
   dplyr::rename("Zone" = "NOM_DEP") %>%
-  dplyr::select(-.data$dep)
+  dplyr::select(-"dep")
 
 evol_popul2_2 <- evol_popul2 %>%
   dplyr::group_by(.data$seuil_pop, .data$date) %>%
@@ -204,8 +204,8 @@ evol_popul2_2 <- evol_popul2 %>%
                   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_popul2 <- dplyr::bind_rows(evol_popul2 ,evol_popul2_2) %>%
+  dplyr::select("seuil_pop", "Zone", "date", "population_municipale")
 
 evol_popul3 <- rbind(evol_popul1,evol_popul2) %>%  #regroupement des annees
   dplyr::arrange(.data$seuil_pop, .data$Zone, .data$date) %>%
@@ -213,10 +213,10 @@ evol_popul3 <- rbind(evol_popul1,evol_popul2) %>%  #regroupement des annees
   dplyr::mutate(evolution_population = round(.data$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)
+  dplyr::select("seuil_pop", "Zone", "evolution_population")
 
 data_final <- evol_artif3 %>%
-  dplyr::full_join(evol_popul3 %>% dplyr::select(.data$seuil_pop, .data$Zone, .data$evolution_population)) %>%
+  dplyr::full_join(evol_popul3 %>% dplyr::select("seuil_pop", "Zone", "evolution_population")) %>%
   dplyr::mutate(`Seuil de population` = factor(.data$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"),
diff --git a/R/creer_graphe_3_4.R b/R/creer_graphe_3_4.R
index 78bf48cdbb9a61e8d27baa30334dc413ef30ed24..aab97399e9e90e0ef1c60d76ff67bb05d3a2df82 100644
--- a/R/creer_graphe_3_4.R
+++ b/R/creer_graphe_3_4.R
@@ -53,7 +53,7 @@ creer_graphe_3_4 <- function(millesime_stock_artif = NULL,millesime_population =
     dplyr::filter(((.data$TypeZone == "D\u00e9partements") | (.data$TypeZone == "R\u00e9gions")),
                   .data$date == millesime_debut) %>%
     dplyr::mutate(surface_artificialisee = .data$surface_artificialisee) %>%
-    dplyr::select (-.data$surf_cadastree,-.data$date) %>%
+    dplyr::select (-"surf_cadastree", -"date") %>%
     dplyr::rename ("stock_depart"="surface_artificialisee")
 
   # preparation des flux
@@ -62,13 +62,13 @@ creer_graphe_3_4 <- function(millesime_stock_artif = NULL,millesime_population =
     COGiter::filtrer_cog(reg = code_reg) %>%
     dplyr::filter(((.data$TypeZone == "D\u00e9partements") | (.data$TypeZone == "R\u00e9gions")),
                   (.data$date == millesime_debut) |(.data$date == millesime_stock_artif)) %>%
-    dplyr::select (-.data$surf_cadastree) %>%
+    dplyr::select(-"surf_cadastree") %>%
     dplyr::arrange(.data$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)
+    dplyr::select(-"date", -"surface_artificialisee")
 
 
   # preparation des donnees population
@@ -79,8 +79,8 @@ creer_graphe_3_4 <- function(millesime_stock_artif = NULL,millesime_population =
                   .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)
+    dplyr::rename("population_debut"="population_municipale") %>%
+    dplyr::select(-"date")
   evol_popul <- evol_popul %>%
     dplyr::filter(.data$date %in% c(millesime_debut_population,millesime_fin_population)) %>%
     dplyr::arrange(.data$date) %>%
@@ -88,7 +88,7 @@ creer_graphe_3_4 <- function(millesime_stock_artif = NULL,millesime_population =
     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)
+    dplyr::select("TypeZone", "Zone", "CodeZone", "evolution_population")
 
   data <-  stock_depart %>%
     dplyr::left_join(flux) %>%
@@ -97,7 +97,7 @@ creer_graphe_3_4 <- function(millesime_stock_artif = NULL,millesime_population =
     dplyr::mutate(Zone = as.factor(.data$Zone)) %>%
     dplyr::mutate(surf_artif_par_hab_an_x=.data$stock_depart / .data$population_debut *10000 ) %>%
     dplyr::mutate(surf_artif_par_nouv_hab_entre_x_y =.data$flux_artificialisation / .data$evolution_population *10000) %>%
-    dplyr::select (.data$TypeZone,.data$Zone,.data$surf_artif_par_hab_an_x,.data$surf_artif_par_nouv_hab_entre_x_y) %>%
+    dplyr::select("TypeZone", "Zone", "surf_artif_par_hab_an_x", "surf_artif_par_nouv_hab_entre_x_y") %>%
     tidyr::gather(variable, valeur, 3:4) %>%
     dplyr::arrange(desc(.data$TypeZone),desc(.data$Zone)) %>%
     dplyr::mutate(variable=forcats::fct_relevel(.data$variable,"surf_artif_par_nouv_hab_entre_x_y","surf_artif_par_hab_an_x")) %>%
diff --git a/R/creer_graphe_4_2.R b/R/creer_graphe_4_2.R
index 016ebf807b18b3bf36207710147f30fdd6cc88af..73c55c6b4674e69dd1f22ca7a33e7d152d19d3d9 100644
--- a/R/creer_graphe_4_2.R
+++ b/R/creer_graphe_4_2.R
@@ -73,16 +73,16 @@ creer_graphe_4_2 <- function(millesime_stock_artif = NULL,millesime_population =
       .data$population_municipale > 0  ~ "A",
       TRUE ~ "")
     ) %>%
-    dplyr::rename ("population_n"="population_municipale") %>%
-    dplyr::select (-.data$date)
+    dplyr::rename("population_n" = "population_municipale") %>%
+    dplyr::select(-"date")
 
   # table des seuils
   seuil_population <- population %>%
-    dplyr::select (.data$CodeZone,.data$seuil_pop)
+    dplyr::select("CodeZone", "seuil_pop")
 
   # population du dernier millesime
   population_n <- population %>%
-    dplyr::select (.data$seuil_pop,.data$seuil_code, .data$population_n) %>%
+    dplyr::select("seuil_pop", "seuil_code", "population_n") %>%
     dplyr::group_by(.data$seuil_pop,.data$seuil_code) %>%
     dplyr::summarise(population_n = sum(.data$population_n, na.rm = T)) %>%
     dplyr::ungroup()
@@ -94,7 +94,7 @@ creer_graphe_4_2 <- function(millesime_stock_artif = NULL,millesime_population =
     dplyr::filter(.data$date == millesime_debut_population,
                   .data$TypeZone =="Communes") %>%
     dplyr::left_join(seuil_population) %>%
-    dplyr::select (.data$seuil_pop, .data$population_municipale) %>%
+    dplyr::select("seuil_pop", "population_municipale") %>%
     dplyr::group_by(.data$seuil_pop) %>%
     dplyr::summarise(population_ancienne = sum(.data$population_municipale, na.rm = T)) %>%
     dplyr::ungroup()
@@ -108,7 +108,7 @@ creer_graphe_4_2 <- function(millesime_stock_artif = NULL,millesime_population =
     dplyr::filter(.data$TypeZone == "Communes",
                   .data$date == millesime_debut) %>%
     dplyr::left_join(seuil_population) %>%
-    dplyr::select (.data$seuil_pop, .data$surface_artificialisee) %>%
+    dplyr::select ("seuil_pop", "surface_artificialisee") %>%
     dplyr::group_by(.data$seuil_pop) %>%
     dplyr::summarise(stock_depart = sum(.data$surface_artificialisee, na.rm = T)) %>%
     dplyr::ungroup()
@@ -128,7 +128,7 @@ creer_graphe_4_2 <- function(millesime_stock_artif = NULL,millesime_population =
     dplyr::group_by(.data$seuil_pop) %>%
     dplyr::mutate(flux = .data$surface_artificialisee - dplyr::lag(.data$surface_artificialisee) ) %>%
     dplyr::filter(.data$date == millesime_stock_artif) %>%
-    dplyr::select (.data$seuil_pop , .data$flux)
+    dplyr::select ("seuil_pop" , "flux")
 
 
   data <-  population_n %>%
@@ -138,10 +138,10 @@ creer_graphe_4_2 <- function(millesime_stock_artif = NULL,millesime_population =
     dplyr::mutate(seuil_pop=as.factor(.data$seuil_pop),seuil_code=as.factor(.data$seuil_code)) %>%
     dplyr::mutate(surf_artif_par_hab_an_x=.data$stock_depart / .data$population_ancienne *10000) %>%
     dplyr::mutate(surf_artif_par_nouv_hab_entre_x_y =.data$flux / (.data$population_n - .data$population_ancienne) *10000)  %>%
-    dplyr::select (.data$seuil_pop,.data$seuil_code,.data$surf_artif_par_hab_an_x,.data$surf_artif_par_nouv_hab_entre_x_y) %>%
+    dplyr::select("seuil_pop", "seuil_code", "surf_artif_par_hab_an_x", "surf_artif_par_nouv_hab_entre_x_y") %>%
     tidyr::gather(variable, valeur, 3:4) %>%
     dplyr::arrange(desc(.data$seuil_code)) %>%
-    dplyr::mutate(variable=forcats::fct_relevel(.data$variable,"surf_artif_par_nouv_hab_entre_x_y","surf_artif_par_hab_an_x")) %>%
+    dplyr::mutate(variable=forcats::fct_relevel(.data$variable,"surf_artif_par_nouv_hab_entre_x_y", "surf_artif_par_hab_an_x")) %>%
     dplyr::mutate(seuil_pop = forcats::fct_drop(.data$seuil_pop) %>%
                     forcats::fct_inorder())
 
@@ -156,8 +156,8 @@ creer_graphe_4_2 <- function(millesime_stock_artif = NULL,millesime_population =
                                     color = .data$variable),
                        position = ggplot2::position_dodge(width=1),
                        hjust=-0.1) +
-    ggplot2::scale_color_manual(values = c("#82534b","#FF8D7E")) +
-    ggplot2::scale_fill_manual(values = c("#82534b","#FF8D7E")) +
+    ggplot2::scale_color_manual(values = c("#82534b", "#FF8D7E")) +
+    ggplot2::scale_fill_manual(values = c("#82534b", "#FF8D7E")) +
     ggplot2::labs(
       title = glue::glue("Surfaces cadastr\u00e9es consomm\u00e9es (en m\u00b2)", width = 60),
       subtitle = glue::glue("<span style = \'color:#FF8D7E\'>par habitant pr\u00e9sent en {millesime_debut_population}</span> et <span style = \'color:#82534b\'>par nouvel habitant entre {millesime_debut_population} et {millesime_fin_population}</span> "),
@@ -168,7 +168,7 @@ creer_graphe_4_2 <- function(millesime_stock_artif = NULL,millesime_population =
     ) +
     ggplot2::scale_y_continuous(
       limits = c(0, max(data$valeur) +500),
-      labels = scales::label_number(big.mark = " ", decimal.mark = ",")
+      labels = scales::label_number(big.mark = " ", decimal.mark = ", ")
     ) +
     ggplot2::theme(
       legend.position = "none",
diff --git a/R/creer_graphe_5_1.R b/R/creer_graphe_5_1.R
index a1c372df036a34a808aa23074900003bad5fe193..1bbbaac402f0fa9869be4bfdccec7b1758c81c8c 100644
--- a/R/creer_graphe_5_1.R
+++ b/R/creer_graphe_5_1.R
@@ -39,13 +39,13 @@ creer_graphe_5_1 <- function(millesime_stock_artif,code_reg) {
   if (code_reg %in% c('52')) {
 
   data <- stock_artificialise %>%
-    dplyr::select (-.data$surf_cadastree) %>%
+    dplyr::select(-"surf_cadastree") %>%
     COGiter::filtrer_cog(reg = code_reg) %>%
     dplyr::mutate(date=lubridate::year(.data$date)) %>%
     dplyr::filter(.data$TypeZone %in% c("D\u00e9partements"),
                   .data$date %in% c(millesime_stock_artif,millesime_reference)) %>%
 
-    dplyr::select(.data$TypeZone,.data$Zone,.data$CodeZone,.data$date,.data$surface_artificialisee) %>%
+    dplyr::select("TypeZone","Zone","CodeZone","date","surface_artificialisee") %>%
     dplyr::mutate(date = dplyr::case_when(
       .data$date == millesime_reference ~ "stock_mill_debut",
       .data$date == millesime_stock_artif ~ "stock_mill_fin",
diff --git a/R/creer_graphe_5_2.R b/R/creer_graphe_5_2.R
index 1043ebcfa2ea4218b1cb1029b515508126757d02..4307e7f8fd91f22705d20563b87a490570d585f8 100644
--- a/R/creer_graphe_5_2.R
+++ b/R/creer_graphe_5_2.R
@@ -35,7 +35,7 @@ creer_graphe_5_2 <- function(millesime_obs_artif,code_reg) {
   data <- observatoire_artificialisation %>%
     COGiter::filtrer_cog(reg = code_reg) %>%
     dplyr::filter(.data$TypeZone %in% c("R\u00e9gions","D\u00e9partements")) %>%
-    dplyr::select(.data$TypeZone,.data$Zone,.data$date,.data$flux_naf_artificialisation_total) %>%
+    dplyr::select( "TypeZone", "Zone", "date", "flux_naf_artificialisation_total") %>%
     dplyr::arrange(.data$TypeZone,.data$Zone,.data$date) %>%
     dplyr::mutate(simul=FALSE,
                   flux_naf_artificialisation_total = .data$flux_naf_artificialisation_total / 10000) %>%
@@ -82,7 +82,7 @@ creer_graphe_5_2 <- function(millesime_obs_artif,code_reg) {
 
   data_segment_departements = data_departements %>%
     dplyr::filter(.data$date ==max(date)) %>%
-    dplyr::select(.data$TypeZone,.data$Zone,.data$date,.data$valeur_objectif,.data$valeur_tendance) %>%
+    dplyr::select( "TypeZone", "Zone", "date", "valeur_objectif", "valeur_tendance") %>%
     dplyr::mutate(y = .data$valeur_objectif+0.5*(.data$valeur_tendance-.data$valeur_objectif),
                   rank = rank(.data$y),
                   nrow = max(dplyr::row_number()),
@@ -92,7 +92,7 @@ creer_graphe_5_2 <- function(millesime_obs_artif,code_reg) {
                                    {mapfactory::format_fr_nb(valeur_tendance-valeur_objectif,0)} ha en moins \u00e0 r\u00e9aliser"))
   data_segment_region = data_region %>%
     dplyr::filter(.data$date ==max(date)) %>%
-    dplyr::select(.data$TypeZone,.data$Zone,.data$date,.data$valeur_objectif,.data$valeur_tendance) %>%
+    dplyr::select( "TypeZone", "Zone", "date", "valeur_objectif", "valeur_tendance") %>%
     dplyr::mutate(y = .data$valeur_objectif+0.5*(.data$valeur_tendance-.data$valeur_objectif),
                   position = .data$y,
                   label = glue::glue("**{Zone}** :
diff --git a/R/creer_tableau_1_6.R b/R/creer_tableau_1_6.R
index 7fed2aa474a3ee0c01c360b009b4ce0c7a7778f2..027551fd58b466916b34e4ad91c90c37fbd600c8 100644
--- a/R/creer_tableau_1_6.R
+++ b/R/creer_tableau_1_6.R
@@ -63,11 +63,11 @@ creer_tableau_1_6 <- function(millesime_ocsge = NULL, millesime_population = NUL
         TRUE ~ "")
         ) %>%
       dplyr::rename ("population_n"="population_municipale") %>%
-      dplyr::select (-.data$date)
+      dplyr::select(-"date")
 
     # table des seuils
     seuil_population <- population %>%
-      dplyr::select (.data$CodeZone,.data$seuil_pop)
+      dplyr::select("CodeZone", "seuil_pop")
 
 
     # preparation des donnees
@@ -80,8 +80,8 @@ creer_tableau_1_6 <- function(millesime_ocsge = NULL, millesime_population = NUL
       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::select (.data$TypeZone,.data$Zone,.data$CodeZone,
-                     .data$espace_naturel,.data$espace_agricole,.data$espace_artificialise)  %>%
+      dplyr::select ("TypeZone", "Zone", "CodeZone",
+                    "espace_naturel", "espace_agricole", "espace_artificialise")  %>%
       dplyr::filter(.data$TypeZone == "Communes")
 
     ocsge3 <- ocsge2 %>%
@@ -104,14 +104,14 @@ creer_tableau_1_6 <- function(millesime_ocsge = NULL, millesime_population = NUL
 
     # donnees du tableau
     data2 <- data1 %>%
-      dplyr::select (.data$seuil_pop,.data$dep,.data$variable,.data$taux) %>%
+      dplyr::select("seuil_pop", "dep", "variable", "taux") %>%
       tidyr::pivot_wider(names_from = "variable", values_from = "taux", values_fill = 0) %>%
       dplyr::left_join(COGiter::departements %>%
-                         dplyr::filter (.data$REG == code_reg) %>%
-                         dplyr::select(.data$DEP, .data$NOM_DEP),
+                         dplyr::filter(.data$REG == code_reg) %>%
+                         dplyr::select("DEP", "NOM_DEP"),
                        by=c("dep"= "DEP")) %>%
       dplyr::rename("Departement" = "NOM_DEP") %>%
-      dplyr::select (.data$seuil_pop, .data$Departement, .data$espace_artificialise, .data$espace_agricole, .data$espace_naturel)
+      dplyr::select("seuil_pop",  "Departement",  "espace_artificialise",  "espace_agricole",  "espace_naturel")
 
     data <- data2 %>%
       dplyr::group_by(.data$seuil_pop) %>%
@@ -125,12 +125,7 @@ creer_tableau_1_6 <- function(millesime_ocsge = NULL, millesime_population = NUL
       dplyr::arrange(.data$seuil_pop) %>%
       dplyr::mutate(seuil_pop = as.character(.data$seuil_pop)) %>%
       dplyr::mutate(seuil_pop = ifelse(.data$n == 1, .data$seuil_pop, "")) %>%
-      dplyr::select(.data$seuil_pop,
-                    .data$Departement,
-                    .data$espace_artificialise,
-                    .data$espace_agricole,
-                    .data$espace_naturel,
-                    .data$nmax)
+      dplyr::select("seuil_pop", "Departement", "espace_artificialise", "espace_agricole", "espace_naturel", "nmax")
 
 
     # Render a bar chart with a label on the left
@@ -141,7 +136,7 @@ creer_tableau_1_6 <- function(millesime_ocsge = NULL, millesime_population = NUL
     }
 
     tableau_1_6 <- reactable::reactable(
-      data %>% dplyr::select(-.data$nmax),
+      data %>% dplyr::select(-"nmax"),
       defaultPageSize = 100,
       sortable = FALSE,
       rowStyle = function(index) {
diff --git a/R/creer_tableau_6_1.R b/R/creer_tableau_6_1.R
index 9b54a69a3848b99fa78451196083bb8ec4708ae3..1413b5cf874a8c770f4f7cc019b369e40c696edc 100644
--- a/R/creer_tableau_6_1.R
+++ b/R/creer_tableau_6_1.R
@@ -33,6 +33,10 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
 
   millesime_debut <- 2016
   millesime_population_debut <- millesime_population - (millesime_stock_artif - millesime_debut)
+  dep_reg <- COGiter::departements %>%
+    dplyr::filter (.data$REG == code_reg) %>%
+    dplyr::select("DEP", "NOM_DEP")
+
   if (is.numeric(code_reg)) {
     code_reg = as.character(code_reg)
   }
@@ -68,11 +72,11 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
       TRUE ~ "")
     ) %>%
     dplyr::rename ("population_n"="population_municipale") %>%
-    dplyr::select (-.data$date)
+    dplyr::select(-"date")
 
   # table des seuils
   seuil_population <- population %>%
-    dplyr::select (.data$CodeZone, .data$seuil_pop)
+    dplyr::select("CodeZone", "seuil_pop")
 
 
   # preparation des donnees evolution_artificialisation
@@ -92,15 +96,11 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
     dplyr::filter (.data$date == millesime_stock_artif) %>%
     dplyr::group_by(.data$dep, .data$seuil_pop, .data$date) %>%
     dplyr::summarise(surface_artificialisee = sum(.data$surface_artificialisee, na.rm = T),
-                     surf_cadastree = sum(.data$surf_cadastree, na.rm = T)
-                     ) %>%
+                     surf_cadastree = sum(.data$surf_cadastree, na.rm = T)) %>%
     dplyr::ungroup() %>%
-    dplyr::left_join(COGiter::departements %>%
-                       dplyr::filter (.data$REG == code_reg) %>%
-                       dplyr::select(.data$DEP, .data$NOM_DEP),
-                     by=c("dep"= "DEP")) %>%
+    dplyr::left_join(dep_reg, by = c("dep"= "DEP")) %>%
     dplyr::rename("Zone" = "NOM_DEP") %>%
-    dplyr::select(-.data$dep)
+    dplyr::select(-"dep")
 
   evol_artif1_2 <- evol_artif1 %>%
     dplyr::group_by(.data$seuil_pop, .data$date) %>%
@@ -110,11 +110,11 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
     dplyr::ungroup() %>%
     dplyr::mutate(Zone = COGiter::regions %>%
                     dplyr::filter (.data$REG == code_reg) %>%
-                    dplyr::pull(.data$NOM_REG))
+                    dplyr::pull("NOM_REG"))
 
 
-  evol_artif1 <-dplyr::bind_rows(evol_artif1,evol_artif1_2) %>%
-    dplyr::select(.data$seuil_pop, .data$Zone, .data$date, .data$surface_artificialisee, .data$surf_cadastree) %>%
+  evol_artif1 <- dplyr::bind_rows(evol_artif1, evol_artif1_2) %>%
+    dplyr::select("seuil_pop", "Zone", "date", "surface_artificialisee", "surf_cadastree") %>%
     dplyr::mutate (part_artificialisee_du_territoire_cadastre = paste0( round((.data$surface_artificialisee / .data$surf_cadastree)*100 , 1), " %"))
 
   evol_artif2 <- evol_artif %>%
@@ -124,12 +124,9 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
     dplyr::group_by(.data$dep, .data$seuil_pop, .data$date) %>%
     dplyr::summarise(surface_artificialisee_ancienne = sum(.data$surface_artificialisee, na.rm = T)) %>%
     dplyr::ungroup() %>%
-    dplyr::left_join(COGiter::departements %>%
-                       dplyr::filter (.data$REG == code_reg) %>%
-                       dplyr::select(.data$DEP, .data$NOM_DEP),
-                     by=c("dep"= "DEP")) %>%
+    dplyr::left_join(dep_reg, by=c("dep"= "DEP")) %>%
     dplyr::rename("Zone" = "NOM_DEP") %>%
-    dplyr::select(-.data$dep)
+    dplyr::select(-"dep")
 
   evol_artif2_2 <- evol_artif2 %>%
     dplyr::group_by(.data$seuil_pop, .data$date) %>%
@@ -139,8 +136,8 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
                     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$surface_artificialisee_ancienne)
+  evol_artif2 <- dplyr::bind_rows(evol_artif2,evol_artif2_2) %>%
+    dplyr::select("seuil_pop", "Zone", "surface_artificialisee_ancienne")
 
 
   # preparation des donnees evol_population
@@ -158,12 +155,9 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, 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$NOM_DEP),
-                     by=c("dep"= "DEP")) %>%
+    dplyr::left_join(dep_reg, by = c("dep" = "DEP")) %>%
     dplyr::rename("Zone" = "NOM_DEP") %>%
-    dplyr::select(-.data$dep)
+    dplyr::select(-"dep")
 
   evol_popul1_2 <- evol_popul1 %>%
     dplyr::group_by(.data$seuil_pop, .data$date) %>%
@@ -171,10 +165,10 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
     dplyr::ungroup() %>%
     dplyr::mutate(Zone = COGiter::regions %>%
                     dplyr::filter (.data$REG == code_reg) %>%
-                    dplyr::pull(.data$NOM_REG))
+                    dplyr::pull("NOM_REG"))
 
   evol_popul1 <-dplyr::bind_rows(evol_popul1, evol_popul1_2) %>%
-    dplyr::select(.data$seuil_pop, .data$Zone, .data$population_municipale)
+    dplyr::select("seuil_pop", "Zone", "population_municipale")
 
 
   evol_popul2 <- evol_popul %>%
@@ -184,12 +178,9 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
     dplyr::group_by(.data$dep, .data$seuil_pop, .data$date) %>%
     dplyr::summarise(population_municipale_ancienne = sum(.data$population_municipale, na.rm = T)) %>%
     dplyr::ungroup() %>%
-    dplyr::left_join(COGiter::departements %>%
-                       dplyr::filter (.data$REG == code_reg) %>%
-                       dplyr::select(.data$DEP, .data$NOM_DEP),
-                     by=c("dep"= "DEP")) %>%
+    dplyr::left_join(dep_reg, by=c("dep"= "DEP")) %>%
     dplyr::rename("Zone" = "NOM_DEP") %>%
-    dplyr::select(-.data$dep)
+    dplyr::select(-"dep")
 
   evol_popul2_2 <- evol_popul2 %>%
     dplyr::group_by(.data$seuil_pop, .data$date) %>%
@@ -197,10 +188,10 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
     dplyr::ungroup() %>%
     dplyr::mutate(Zone = COGiter::regions %>%
                     dplyr::filter (.data$REG == code_reg) %>%
-                    dplyr::pull(.data$NOM_REG))
+                    dplyr::pull("NOM_REG"))
 
-  evol_popul2 <-dplyr::bind_rows(evol_popul2, evol_popul2_2) %>%
-    dplyr::select(.data$seuil_pop, .data$Zone, .data$population_municipale_ancienne)
+  evol_popul2 <- dplyr::bind_rows(evol_popul2, evol_popul2_2) %>%
+    dplyr::select("seuil_pop", "Zone", "population_municipale_ancienne")
 
 
 # regroupement des donnees
@@ -222,18 +213,18 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
                                                            glue::glue("communes entre\n",pop_2," et ",pop_3, " habitants"),
                                                            glue::glue("communes de plus\nde ",pop_3," habitants")))) %>%
     dplyr::arrange(.data$seuil_pop) %>%
-    dplyr::select (.data$seuil_pop,
-                   .data$Zone,
-                   .data$surf_cadastree,
-                   .data$surface_artificialisee,
-                   .data$population_municipale,
-                   .data$evolution_surf_artificialisee,
-                   .data$part_artificialisee_du_territoire_cadastre,
-                   .data$tc_surfaces_artificialisees,
-                   .data$tc_population,
-                   .data$nb_annees_pour_doubler,
-                   .data$surf_artif_par_hab_an_x,
-                   .data$surf_artif_par_nouv_hab_entre_x_y)
+    dplyr::select("seuil_pop",
+                   "Zone",
+                   "surf_cadastree",
+                   "surface_artificialisee",
+                   "population_municipale",
+                   "evolution_surf_artificialisee",
+                   "part_artificialisee_du_territoire_cadastre",
+                   "tc_surfaces_artificialisees",
+                   "tc_population",
+                   "nb_annees_pour_doubler",
+                   "surf_artif_par_hab_an_x",
+                   "surf_artif_par_nouv_hab_entre_x_y")
 
 # separation des tableaux
 
@@ -257,7 +248,7 @@ creer_tableau_6_1 <- function(millesime_stock_artif = NULL, millesime_population
 
 
 tableau_6_1 <- data_regroupe %>%
-  dplyr::select(-.data$seuil_pop) %>%
+  dplyr::select(-"seuil_pop") %>%
   knitr::kable("html", caption = "Tableau de synth\u00e8se",
                digits= c(0,0,0,0,0,1,1,1,0,1,1),
                align = "c",
diff --git a/R/globals.R b/R/globals.R
index 606a3cce221e81f17791b2ffc5099cd823b048bc..2ec21ca4bfc83e1463cdd4c2ae70d1b4de798370 100644
--- a/R/globals.R
+++ b/R/globals.R
@@ -1,5 +1,5 @@
-utils::globalVariables(
+globalVariables(
   c("teruti","observatoire_artificialisation",".data","variable",".",
-    "valeur","etalement_urbain","observatoire_artificialisation_gk3",
+    "valeur", "valeur_ind", "etalement_urbain","observatoire_artificialisation_gk3",
     "stock_artificialise","population_legale","ocsge","regions")
 )
diff --git a/data-raw/data_tableurs.R b/data-raw/data_tableurs.R
index 55675d4d63a3dcfcde95a5f69674bc2f006e78d3..3c788aa9b07a95dbb2d26bab77c1d34f9ef9ba5f 100644
--- a/data-raw/data_tableurs.R
+++ b/data-raw/data_tableurs.R
@@ -15,7 +15,7 @@ millesime_ocsge <- millesimes  %>% dplyr::filter (donnee == "ocsge") %>% pull ()
 millesime_obs_artif <- millesimes  %>% dplyr::filter (donnee == "observatoire_artificialisation") %>% pull ()
 millesime_population <- millesimes  %>% dplyr::filter (donnee == "population_legale") %>% pull ()
 millesime_stock_artif <- millesimes  %>% dplyr::filter (donnee == "stock_artificialise") %>% pull ()
-millesime_debut <- 2016  #millesime debut stock artificialisation (fixe)
+millesime_debut <- 2016  # millesime debut stock artificialisation (fixe)
 
 
 # carte 1_3 (source OCSGE) ---------------
diff --git a/man/creer_carte_1_3.Rd b/man/creer_carte_1_3.Rd
index 29a1d0beac122d093907fc49c8359edeb7adc996..0d763ee9260becfb6bc28bbc8ca4332a66d01d30 100644
--- a/man/creer_carte_1_3.Rd
+++ b/man/creer_carte_1_3.Rd
@@ -18,5 +18,5 @@ Une carte
 Carte communale regionale de l'artificialisation en volume selon OCSGE
 }
 \examples{
-creer_carte_1_3(millesime_ocsge=2016, code_reg = '52')
+creer_carte_1_3(millesime_ocsge = 2016, code_reg = '52')
 }
diff --git a/propre.artificialisation.Rproj b/propre.artificialisation.Rproj
index 4adc5c8d1d1009ce845fc96061fbb03ab53f5c0c..69fafd4b6dddad27500cfc67efb9fb16e86a96bd 100644
--- a/propre.artificialisation.Rproj
+++ b/propre.artificialisation.Rproj
@@ -19,7 +19,4 @@ LineEndingConversion: Posix
 BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
-PackageBuildArgs: --no-build-vignettes
-PackageBuildBinaryArgs: --no-build-vignettes
-PackageCheckArgs: --no-build-vignettes
 PackageRoxygenize: rd,collate,namespace