diff --git a/DESCRIPTION b/DESCRIPTION
index 3ed9a2ab901682dfeaa03c7a9768569775e52a57..e479dd1eef10af67222bfbda30822200fabfdcef 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -8,7 +8,7 @@ Description: Application shiny de valorisation des mesures de présence de
     nitrates dans les cours d'eau et les nappes des Pays de la Loire.
 License: MIT + file LICENSE
 Depends: 
-    R (>= 2.10)
+    R (>= 3.5)
 Imports: 
     config (>= 0.3.2),
     dplyr,
diff --git a/NAMESPACE b/NAMESPACE
index 65aa63e2a456deb46c8b1797b93edd3953fdff75..37036e8c1ec1b625e56c62454844c4783269f376 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -46,7 +46,6 @@ export(prepa_masse_eau_bassin_versant_PDL_fct)
 export(prepa_nitrates_P90_fct)
 export(prepa_nitrates_P90_telecharger_fct)
 export(prepa_nitrates_bassin_versant_fct)
-export(prepa_nitrates_fct)
 export(prepa_nitrates_maxP90_bassin_versant_fct)
 export(prepa_nitrates_maxP90_bassin_versant_telecharger_fct)
 export(prepa_nitrates_moyP90_bassin_versant_fct)
@@ -86,7 +85,6 @@ importFrom(dplyr,do)
 importFrom(dplyr,filter)
 importFrom(dplyr,first)
 importFrom(dplyr,group_by)
-importFrom(dplyr,inner_join)
 importFrom(dplyr,lag)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
diff --git a/R/data_doc-bassin_versant_col_pa.R b/R/data_doc-bassin_versant_col_pa.R
index aaf1dc40de4119b940896dc0c8b9004d33e5c523..c7b9602b6a8f66a3018f1c244a279dbdfd3f1339 100644
--- a/R/data_doc-bassin_versant_col_pa.R
+++ b/R/data_doc-bassin_versant_col_pa.R
@@ -2,7 +2,7 @@
 #'
 #' pression azotée (minérale, organique) par bassin versant par campagne culturale.
 #'
-#' @format A data frame with 4616 rows and 5 variables:
+#' @format A data frame with 5538 rows and 5 variables:
 #' \describe{
   #'   \item{ code_bassin_versant }{  character }
   #'   \item{ nom_bassin_versant }{  character }
diff --git a/R/data_doc-bassin_versant_pa_telecharger.R b/R/data_doc-bassin_versant_pa_telecharger.R
index 0ff35df82377d09c7d2fe888dedf255d00b20e20..f7f4dd4f1ca519b35f60ce5bfae39943e011d858 100644
--- a/R/data_doc-bassin_versant_pa_telecharger.R
+++ b/R/data_doc-bassin_versant_pa_telecharger.R
@@ -2,7 +2,7 @@
 #'
 #' données des bassins versants pour la page Pression azotée à télécharger.
 #'
-#' @format A data frame with 2308 rows and 7 variables:
+#' @format A data frame with 2769 rows and 7 variables:
 #' \describe{
   #'   \item{ code_bassin_versant }{  character }
   #'   \item{ nom_bassin_versant }{  character }
diff --git a/R/data_doc-choix_couleur_P90.R b/R/data_doc-choix_couleur_P90.R
index 5cc5f96b71c9e4d59168363f7fe0c192c89eec8d..6063c3b56d4fd038fac9bdc1dee04e85cb67611a 100644
--- a/R/data_doc-choix_couleur_P90.R
+++ b/R/data_doc-choix_couleur_P90.R
@@ -2,7 +2,7 @@
 #'
 #' définition de la couleur afférente à chacune des classes de percentiles 90.
 #'
-#' @format A data frame with 8 rows and 2 variables:
+#' @format A data frame with 7 rows and 2 variables:
 #' \describe{
   #'   \item{ classe }{  character }
   #'   \item{ couleur }{  character }
diff --git a/R/data_doc-correspondance_sage_bassin_versant.R b/R/data_doc-correspondance_sage_bassin_versant.R
index b3b72af0bfdd4cf6e2458adfd634e79cdd86b4c4..4f778fd5efe873f31b1fd631e59031fcf093db24 100644
--- a/R/data_doc-correspondance_sage_bassin_versant.R
+++ b/R/data_doc-correspondance_sage_bassin_versant.R
@@ -2,11 +2,11 @@
 #'
 #' correspondance entre les SAGE et les bassins versants qui y sont inclus en totalité ou en partie pour la région des Pays de la Loire.
 #'
-#' @format A data frame with 457 rows and 4 variables:
+#' @format A data frame with 463 rows and 4 variables:
 #' \describe{
   #'   \item{ code_sage }{  character }
-  #'   \item{ code_bassin_versant }{  character }
   #'   \item{ nom_sage }{  character }
+  #'   \item{ code_bassin_versant }{  character }
   #'   \item{ nom_bassin_versant }{  character }
   #' }
   #' @source DREAL
diff --git a/R/data_doc-departement_col_pa.R b/R/data_doc-departement_col_pa.R
index 21b740f5937640bd96d06938bf8b2be626079854..42c4b0884456ad74728ff5569190b65fc85b6e0c 100644
--- a/R/data_doc-departement_col_pa.R
+++ b/R/data_doc-departement_col_pa.R
@@ -2,7 +2,7 @@
 #'
 #' pression azotée (globale, minérale, organique) par département et région par campagne culturale.
 #'
-#' @format A data frame with 60 rows and 5 variables:
+#' @format A data frame with 72 rows and 5 variables:
 #' \describe{
   #'   \item{ annee_declaration }{  character }
   #'   \item{ Nom }{  character }
diff --git a/R/data_doc-departement_pa_telecharger.R b/R/data_doc-departement_pa_telecharger.R
index 17b65adb3406bda71e7209e2e32217e4091e34ca..79fc7731c4288dc91b470d3e6e92cbeacbfcb1a1 100644
--- a/R/data_doc-departement_pa_telecharger.R
+++ b/R/data_doc-departement_pa_telecharger.R
@@ -2,7 +2,7 @@
 #'
 #' données des départements et de la région pour la page Pression azotée à télécharger.
 #'
-#' @format A data frame with 30 rows and 5 variables:
+#' @format A data frame with 36 rows and 5 variables:
 #' \describe{
   #'   \item{ Nom }{  character }
   #'   \item{ annee_declaration }{  character }
diff --git a/R/data_doc-depassement_seuil_eso.R b/R/data_doc-depassement_seuil_eso.R
index 8fbd00b348f5c50262a4ad4725fa2912647d5f27..694cb6c0829d922ffaef22dcc6a7934c84a20b8e 100644
--- a/R/data_doc-depassement_seuil_eso.R
+++ b/R/data_doc-depassement_seuil_eso.R
@@ -2,7 +2,7 @@
 #'
 #' pourcentage des mesures nitrates des eaux souterraines dépassant le seuil de 50 mg/l au niveau régional ou par SAGE ou par bassin versant, par année.
 #'
-#' @format A data frame with 867 rows and 5 variables:
+#' @format A data frame with 921 rows and 5 variables:
 #' \describe{
   #'   \item{ annee }{  factor }
   #'   \item{ depassement }{  numeric }
diff --git a/R/data_doc-depassement_seuil_esu.R b/R/data_doc-depassement_seuil_esu.R
index ee2bf53c465965a79651f871eb8e27f2d24e8db4..df24fa3cb0b45a0fc8c135045744f57335f20778 100644
--- a/R/data_doc-depassement_seuil_esu.R
+++ b/R/data_doc-depassement_seuil_esu.R
@@ -2,7 +2,7 @@
 #'
 #' pourcentage des mesures nitrates des eaux superficielles dépassant le seuil de 18 mg/l au niveau régional ou par SAGE ou par bassin versant, par année.
 #'
-#' @format A data frame with 3444 rows and 5 variables:
+#' @format A data frame with 3694 rows and 5 variables:
 #' \describe{
   #'   \item{ annee }{  factor }
   #'   \item{ depassement }{  numeric }
diff --git a/R/data_doc-masse_eau_bassin_versant_PDL.R b/R/data_doc-masse_eau_bassin_versant_PDL.R
index 5081d872d9ce3f5b8eab9e39df0acd4ef797260c..19a681855b08eb2bc2c4fb95f0e66f8e67a41281 100644
--- a/R/data_doc-masse_eau_bassin_versant_PDL.R
+++ b/R/data_doc-masse_eau_bassin_versant_PDL.R
@@ -2,7 +2,7 @@
 #'
 #' géodataframe des bassins versants de la région des Pays de la Loire.
 #'
-#' @format A data frame with 457 rows and 3 variables:
+#' @format A data frame with 463 rows and 3 variables:
 #' \describe{
   #'   \item{ code_bassin_versant }{  character }
   #'   \item{ nom_bassin_versant }{  character }
diff --git a/R/data_doc-n_sage_r52.R b/R/data_doc-n_sage_r52.R
index 701d6f173a14674cac6add1c5f521575d62c6d8d..a91a2a25fd8fc36868cc332209a538f38a5ed6b1 100644
--- a/R/data_doc-n_sage_r52.R
+++ b/R/data_doc-n_sage_r52.R
@@ -2,10 +2,9 @@
 #'
 #' géodataframe des SAGE de la région des Pays de la Loire.
 #'
-#' @format A data frame with 24 rows and 16 variables:
+#' @format A data frame with 24 rows and 15 variables:
 #' \describe{
   #'   \item{ nid }{  character }
-  #'   \item{ vid }{  character }
   #'   \item{ nom_sage }{  character }
   #'   \item{ type }{  character }
   #'   \item{ code_sage }{  character }
diff --git a/R/data_doc-nitrates_P90.R b/R/data_doc-nitrates_P90.R
index f5cbd086c5ec7126bff184fa9abdee901d0edada..cac24a8fcdedb013cb57312b2a4f83cc05d05959 100644
--- a/R/data_doc-nitrates_P90.R
+++ b/R/data_doc-nitrates_P90.R
@@ -2,12 +2,15 @@
 #'
 #' percentiles 90 par station par annee.
 #'
-#' @format A data frame with 11972 rows and 9 variables:
+#' @format A data frame with 12848 rows and 12 variables:
 #' \describe{
   #'   \item{ code_station }{  factor }
+  #'   \item{ code_bss }{  factor }
+  #'   \item{ code_sise_eaux }{  factor }
+  #'   \item{ code_naiades }{  factor }
   #'   \item{ libelle_station }{  factor }
-  #'   \item{ source_station }{  factor }
   #'   \item{ annee }{  numeric }
+  #'   \item{ source_station }{  factor }
   #'   \item{ id_usage }{  factor }
   #'   \item{ P90 }{  numeric }
   #'   \item{ code_nature_eau }{  factor }
diff --git a/R/data_doc-nitrates_P90_telecharger.R b/R/data_doc-nitrates_P90_telecharger.R
index bdbff40527e60a85135fcd20aa9b3746aa626b62..757ae57afbb531cb337317637515feffb7c0252e 100644
--- a/R/data_doc-nitrates_P90_telecharger.R
+++ b/R/data_doc-nitrates_P90_telecharger.R
@@ -2,7 +2,7 @@
 #'
 #' données à télécharger des percentiles 90 par station par annee .
 #'
-#' @format A data frame with 7781 rows and 13 variables:
+#' @format A data frame with 12848 rows and 13 variables:
 #' \describe{
   #'   \item{ annee }{  numeric }
   #'   \item{ code_nature_eau }{  factor }
diff --git a/R/data_doc-nitrates_maxP90_bassin_versant.R b/R/data_doc-nitrates_maxP90_bassin_versant.R
index 3b24d34003f39eebb6e77bb62b00f02620caa464..1e5b09f09927e3215f27b10f4a18a4e2d8a957f9 100644
--- a/R/data_doc-nitrates_maxP90_bassin_versant.R
+++ b/R/data_doc-nitrates_maxP90_bassin_versant.R
@@ -2,7 +2,7 @@
 #'
 #' maxima des percentiles 90 par bassin versant par année.
 #'
-#' @format A data frame with 5797 rows and 5 variables:
+#' @format A data frame with 6231 rows and 5 variables:
 #' \describe{
   #'   \item{ code_bassin_versant }{  character }
   #'   \item{ annee }{  numeric }
diff --git a/R/data_doc-nitrates_maxP90_bassin_versant_telecharger.R b/R/data_doc-nitrates_maxP90_bassin_versant_telecharger.R
index 4d9354323e47e57e21e0fa62cdecee77b2afad37..80ba9d0fb3345db5a1392a4d83b7f6ed9012d0a0 100644
--- a/R/data_doc-nitrates_maxP90_bassin_versant_telecharger.R
+++ b/R/data_doc-nitrates_maxP90_bassin_versant_telecharger.R
@@ -2,7 +2,7 @@
 #'
 #' données à télécharger des maxima des percentiles 90 par bassin versant par année.
 #'
-#' @format A data frame with 5937 rows and 8 variables:
+#' @format A data frame with 6231 rows and 8 variables:
 #' \describe{
   #'   \item{ annee }{  numeric }
   #'   \item{ code_nature_eau }{  factor }
diff --git a/R/data_doc-nitrates_moyP90_bassin_versant.R b/R/data_doc-nitrates_moyP90_bassin_versant.R
index 9a1a5dc5e204cfdb9704578be2a21fb552939d12..9dd4f38740ac089ca691b555b64ea722cc31915e 100644
--- a/R/data_doc-nitrates_moyP90_bassin_versant.R
+++ b/R/data_doc-nitrates_moyP90_bassin_versant.R
@@ -2,7 +2,7 @@
 #'
 #' moyennes des  percentiles 90 par bassin versant par année.
 #'
-#' @format A data frame with 5797 rows and 5 variables:
+#' @format A data frame with 6231 rows and 5 variables:
 #' \describe{
   #'   \item{ code_bassin_versant }{  character }
   #'   \item{ annee }{  numeric }
diff --git a/R/data_doc-nitrates_moyP90_bassin_versant_telecharger.R b/R/data_doc-nitrates_moyP90_bassin_versant_telecharger.R
index c7d9b835c14411688eef8115d58343fbe0c09a2d..631a7fa543e0c69e6a057cae56b88ab8e87755a5 100644
--- a/R/data_doc-nitrates_moyP90_bassin_versant_telecharger.R
+++ b/R/data_doc-nitrates_moyP90_bassin_versant_telecharger.R
@@ -2,7 +2,7 @@
 #'
 #' données à télécharger des moyennes des  percentiles 90 par bassin versant par année.
 #'
-#' @format A data frame with 5937 rows and 8 variables:
+#' @format A data frame with 6231 rows and 8 variables:
 #' \describe{
   #'   \item{ annee }{  numeric }
   #'   \item{ code_nature_eau }{  factor }
diff --git a/R/data_doc-nitrates_telecharger.R b/R/data_doc-nitrates_telecharger.R
index 54f0b46bdf04d1451afb424f9c40c060553c83f7..fa0faa867a1c64d2023265bc7078126a7c9ec1b9 100644
--- a/R/data_doc-nitrates_telecharger.R
+++ b/R/data_doc-nitrates_telecharger.R
@@ -2,7 +2,7 @@
 #'
 #' données à télécharger des mesures de nitrates par station.
 #'
-#' @format A data frame with 63239 rows and 15 variables:
+#' @format A data frame with 81525 rows and 15 variables:
 #' \describe{
   #'   \item{ annee }{  numeric }
   #'   \item{ mois }{  numeric }
@@ -13,9 +13,9 @@
   #'   \item{ nom_bassin_versant }{  character }
   #'   \item{ code_station }{  character }
   #'   \item{ libelle_station }{  character }
-  #'   \item{ source_prelevement }{  character }
+  #'   \item{ source_prelevement_analyse }{  character }
   #'   \item{ resultat_analyse }{  numeric }
-  #'   \item{ code_remarque }{  integer }
+  #'   \item{ code_remarque }{  numeric }
   #'   \item{ insee_dep }{  factor }
   #'   \item{ code_commune }{  character }
   #'   \item{ captage_prioritaire }{  logical }
diff --git a/R/data_doc-nitrates_tendanceP90.R b/R/data_doc-nitrates_tendanceP90.R
index 9c671efc4c727c0df058791176d62901d19c316b..266bbd803682bff2472595d0480d3e74c7c0be83 100644
--- a/R/data_doc-nitrates_tendanceP90.R
+++ b/R/data_doc-nitrates_tendanceP90.R
@@ -2,7 +2,7 @@
 #'
 #' tendances des percentiles 90 par station par période de 10 ans.
 #'
-#' @format A data frame with 8754 rows and 5 variables:
+#' @format A data frame with 10135 rows and 5 variables:
 #' \describe{
   #'   \item{ code_station }{  factor }
   #'   \item{ libelle_station }{  factor }
diff --git a/R/data_doc-nitrates_tendanceP90_telecharger.R b/R/data_doc-nitrates_tendanceP90_telecharger.R
index 136423e48128d681d87441651a68aa31fd318a70..de32794aff15c33e5320b4880ead107dd4ab1345 100644
--- a/R/data_doc-nitrates_tendanceP90_telecharger.R
+++ b/R/data_doc-nitrates_tendanceP90_telecharger.R
@@ -2,7 +2,7 @@
 #'
 #' données à télécharger des tendances des percentiles 90 par station par période de 10 ans.
 #'
-#' @format A data frame with 6311 rows and 12 variables:
+#' @format A data frame with 10135 rows and 12 variables:
 #' \describe{
   #'   \item{ periode }{  factor }
   #'   \item{ code_nature_eau }{  factor }
diff --git a/R/data_doc-periode_nitrates_tendanceP90.R b/R/data_doc-periode_nitrates_tendanceP90.R
index 9fa48fb235c251c95e0285bb13256b74bde951c7..80303af825f6dda1e492e8cde81e36c6b15dc30f 100644
--- a/R/data_doc-periode_nitrates_tendanceP90.R
+++ b/R/data_doc-periode_nitrates_tendanceP90.R
@@ -2,7 +2,7 @@
 #'
 #' affectation d'une période de 10 ans à chaque année.
 #'
-#' @format A data frame with 16 rows and 2 variables:
+#' @format A data frame with 17 rows and 2 variables:
 #' \describe{
   #'   \item{ annee }{  factor }
   #'   \item{ periode }{  factor }
diff --git a/R/data_doc-repartition_P90.R b/R/data_doc-repartition_P90.R
index 54d0d29a9d12c6cf978714bd1a6c69adbdb0efff..db89325c5be3fbf1ae68b353e85ef1f532b63179 100644
--- a/R/data_doc-repartition_P90.R
+++ b/R/data_doc-repartition_P90.R
@@ -2,7 +2,7 @@
 #'
 #' répartition du nombre de stations par classe de percentiles 90 au niveau régional ou par SAGE ou par bassin versant, par annee.
 #'
-#' @format A data frame with 11750 rows and 8 variables:
+#' @format A data frame with 11505 rows and 8 variables:
 #' \describe{
   #'   \item{ code_nature_eau }{  factor }
   #'   \item{ annee }{  numeric }
diff --git a/R/data_doc-repartition_maxBV.R b/R/data_doc-repartition_maxBV.R
index 3bb3c894553a39e32724f4fc494720e42bbdc23b..ca6eb2885cd24f35ff1639d65a393f0694820b12 100644
--- a/R/data_doc-repartition_maxBV.R
+++ b/R/data_doc-repartition_maxBV.R
@@ -2,7 +2,7 @@
 #'
 #' répartition des maxima de percentiles 90 par classes de concentration au niveau régional ou par SAGE, par année.
 #'
-#' @format A data frame with 2854 rows and 7 variables:
+#' @format A data frame with 2580 rows and 7 variables:
 #' \describe{
   #'   \item{ code_nature_eau }{  factor }
   #'   \item{ annee }{  numeric }
diff --git a/R/data_doc-repartition_moyBV.R b/R/data_doc-repartition_moyBV.R
index 8ae1e140c762f3ce47cfbfa9f9c0a863ead676c2..e51152364589f506b73d390eab5eb67d182e6f04 100644
--- a/R/data_doc-repartition_moyBV.R
+++ b/R/data_doc-repartition_moyBV.R
@@ -2,7 +2,7 @@
 #'
 #' répartition des moyennes de percentiles 90 par classes de concentration au niveau régional ou par SAGE, par année.
 #'
-#' @format A data frame with 2733 rows and 7 variables:
+#' @format A data frame with 2431 rows and 7 variables:
 #' \describe{
   #'   \item{ code_nature_eau }{  factor }
   #'   \item{ annee }{  numeric }
diff --git a/R/data_doc-repartition_tendanceP90.R b/R/data_doc-repartition_tendanceP90.R
index 75dfc1711fcf73af3d3ae4d23b7cf0cd9199cf80..09f20181fb8e0b1bd4eaee8bec0dcd6fad7bfbb1 100644
--- a/R/data_doc-repartition_tendanceP90.R
+++ b/R/data_doc-repartition_tendanceP90.R
@@ -2,7 +2,7 @@
 #'
 #' répartition des tendances de percentiles 90 par classes de concentration au niveau régional ou par SAGE ou par bassin versant, par période de 10 ans.
 #'
-#' @format A data frame with 6412 rows and 8 variables:
+#' @format A data frame with 7254 rows and 8 variables:
 #' \describe{
   #'   \item{ code_nature_eau }{  factor }
   #'   \item{ periode }{  factor }
diff --git a/R/data_doc-station_pdl.R b/R/data_doc-station_pdl.R
index bde9eb65679f1c09f3d1148fd38731b8c8399638..858c4a09bc83b4740917403719ed15dcc341a360 100644
--- a/R/data_doc-station_pdl.R
+++ b/R/data_doc-station_pdl.R
@@ -2,21 +2,23 @@
 #'
 #' géodataframe des stations de mesures de la région des Pays de la Loire.
 #'
-#' @format A data frame with 3816 rows and 14 variables:
+#' @format A data frame with 3870 rows and 16 variables:
 #' \describe{
   #'   \item{ code_station }{  character }
+  #'   \item{ code_bss }{  character }
+  #'   \item{ code_sise_eaux }{  character }
+  #'   \item{ code_naiades }{  character }
   #'   \item{ libelle_station }{  character }
+  #'   \item{ code_nature_eau }{  character }
   #'   \item{ date_creation }{  Date }
-  #'   \item{ source }{  character }
+  #'   \item{ source_station }{  character }
   #'   \item{ code_masse_eau }{  character }
-  #'   \item{ code_eu_masse_eau }{  character }
+  #'   \item{ code_bassin_versant }{  character }
   #'   \item{ code_commune }{  character }
   #'   \item{ code_sage }{  character }
-  #'   \item{ code_bassin_versant }{  character }
   #'   \item{ captage_prioritaire }{  logical }
   #'   \item{ insee_dep }{  factor }
   #'   \item{ test }{  logical }
-  #'   \item{ code_nature_eau }{  character }
   #'   \item{ the_geom }{  sfc_POINT,sfc }
   #' }
   #' @source DREAL
diff --git a/R/fct_carte_P90.R b/R/fct_carte_P90.R
index 661220acde7baa715d99a03d5a7e78de221ae98e..d01a3dc8ae420fcdeca426f8ae197bff97cd2279 100644
--- a/R/fct_carte_P90.R
+++ b/R/fct_carte_P90.R
@@ -116,7 +116,8 @@ carte_P90_station_fct <- function(selectSage, selectBV, nitrates_P90_carte, sage
       lapply(htmltools::HTML)
     factpal_P90_station <- leaflet::colorFactor(
       # palette =  c("#33CCFF","#00CC33","#66FF99","#FFFF00","orange","red","#FF6633","#CC3300"),
-      palette = c("#33CCFF", "#00CC33", "#66FF99", "#FFFF00", "orange", "red", "#A10684", "#723E64"),
+      # palette = c("#33CCFF", "#00CC33", "#66FF99", "#FFFF00", "orange", "red", "#A10684", "#723E64"),
+      palette = c("#33CCFF", "#00CC33", "#FFFF00", "orange", "red", "#A10684", "#723E64"),
       nitrates_P90$classe
       )
     if(selectSage == "Tous" & selectBV == "Tous")({
diff --git a/R/fct_carte_moymaxP90.R b/R/fct_carte_moymaxP90.R
index 8ad8c199dbdf68fa40655ce79da35c5d24a554dc..99114d5be2a74f40b569485e6b457c551c747d14 100644
--- a/R/fct_carte_moymaxP90.R
+++ b/R/fct_carte_moymaxP90.R
@@ -88,7 +88,8 @@ carte_bassin_versant_fct <- function(modalite, selectSage, nitrates_bassin_versa
 
   titre_legende <- paste("Classe<br>", qualification_percentiles, "des P90<br>par bassin versant")
   factpal_bassin_versant <- leaflet::colorFactor(
-    palette = c("#33CCFF", "#00CC33", "#66FF99", "#FFFF00", "orange", "red", "#A10684", "#723E64"),
+    # palette = c("#33CCFF", "#00CC33", "#66FF99", "#FFFF00", "orange", "red", "#A10684", "#723E64"),
+    palette = c("#33CCFF", "#00CC33", "#FFFF00", "orange", "red", "#A10684", "#723E64"),
     nitrates_maxP90_bassin_versant$classe
     )
 
diff --git a/R/fct_preparation_data.R b/R/fct_preparation_data.R
index 559a9cbdb5691e57ab3939b5968035004681c6dd..f48d081f47befcd4abfb16ef2835134586f505d8 100644
--- a/R/fct_preparation_data.R
+++ b/R/fct_preparation_data.R
@@ -48,38 +48,6 @@ prepa_station_PDL_fct <- function(station){
     dplyr::filter(test == FALSE)
 }
 
-#' prepa_nitrates_fct
-#'
-#' @description Une fonction pour produire les tables des mesures nitrates en fonction du code_nature_eau (ESU ou ESO) des stations
-#' @param station_code_nature_eau choix dans la table des stations selon leur code_nature_eau (ESU ou ESO)
-#'
-#' @return Un dataframe.
-#'
-#' @export
-#' @importFrom dplyr filter inner_join rename select
-#' @importFrom sf st_drop_geometry
-prepa_nitrates_fct <- function(station_code_nature_eau){
-  nitrates_esu_ou_eso <- nitrates %>%
-    dplyr::inner_join(
-      station_pdl %>% dplyr::filter(code_nature_eau == station_code_nature_eau) %>% sf::st_drop_geometry(),
-      by = c("code_station")
-    ) %>%
-    dplyr::rename(
-      code_intervenant_prelevement = "code_intervenant.x",
-      code_intervenant_analyse = "code_intervenant.y",
-      source_prelevement = "source.x",
-      source_station = "source.y"
-    ) %>%
-    dplyr::select(
-      code_station, libelle_station, source_station,
-      code_prelevement, source_prelevement,
-      date_prelevement, heure_prelevement, annee, mois,
-      id_usage,
-      code_analyse, date_analyse,
-      resultat_analyse, code_remarque
-    )
-}
-
 #' prepa_correspondance_sage_bassin_versant_fct
 #'
 #' @description Une fonction pour produire la table de correspondance entre bassin_versant et SAGE
@@ -196,6 +164,7 @@ prepa_NatureEau_fct <- function(station_pdl){
 #' @importFrom dplyr group_by arrange mutate n filter rename select ungroup case_when mutate_if
 prepa_nitrates_P90_fct <- function(nitrates){
   nitrates_P90 <- nitrates %>%
+    dplyr::left_join(station_pdl %>% sf::st_drop_geometry()) %>%
     dplyr::group_by(code_station, annee) %>%
     dplyr::arrange(code_station, annee, desc(resultat_analyse)) %>%
     dplyr::mutate(
@@ -211,8 +180,9 @@ prepa_nitrates_P90_fct <- function(nitrates){
     dplyr::mutate(classe = dplyr::case_when(
       P90 < 10 ~ "0-10 mg/l",
       P90 < 18 ~ "10-18 mg/l",
-      P90 < 30 ~ "18-30 mg/l",
-      P90 < 40 ~ "30-40 mg/l",
+      # P90 < 30 ~ "18-30 mg/l",
+      # P90 < 40 ~ "30-40 mg/l",
+      P90 < 40 ~ "18-40 mg/l",
       P90 < 50 ~ "40-50 mg/l",
       P90 < 60 ~ "50-60 mg/l",
       P90 < 70 ~ "60-70 mg/l",
@@ -220,8 +190,10 @@ prepa_nitrates_P90_fct <- function(nitrates){
     )
     ) %>%
     dplyr::select(
-      -c(code_prelevement, source_prelevement, date_prelevement, heure_prelevement, mois,
-         code_analyse, date_analyse, code_remarque)
+      # -c(code_prelevement_analyse, source_prelevement_analyse, date_prelevement, heure_prelevement, mois,
+      #    date_analyse, code_remarque)
+      c(code_station, code_bss, code_sise_eaux, code_naiades, libelle_station,
+        annee, source_station, id_usage, P90, code_nature_eau, pos_a_garder, classe)
     ) %>%
     dplyr::filter(!is.na(classe)) %>%
     dplyr::mutate_if(is.character,as.factor)
@@ -358,7 +330,9 @@ prepa_nitrates_bassin_versant_fct <- function(nitrates_P90){
   nitrates_bassin_versant <- nitrates_P90 %>%
     dplyr::semi_join(station_pdl) %>%
     dplyr::left_join(
-      station_pdl %>% dplyr::select(c(code_station, libelle_station, code_bassin_versant)) %>% sf::st_drop_geometry(),
+      station_pdl %>%
+        dplyr::select(c(code_station, libelle_station, code_bassin_versant)) %>%
+        sf::st_drop_geometry(),
       by = c("code_station")
     ) %>%
     dplyr::group_by(code_bassin_versant, annee, code_nature_eau) %>%
@@ -385,12 +359,13 @@ prepa_nitrates_moyP90_bassin_versant_fct <- function(nitrates_bassin_versant){
       classe = dplyr::case_when(
         moyP90 < 10 ~ "0-10 mg/l",
         moyP90 < 18 ~ "10-18 mg/l",
-        moyP90 < 30 ~ "18-30 mg/l",
-        moyP90 < 40 ~ "30-40 mg/l",
+        # moyP90 < 30 ~ "18-30 mg/l",
+        # moyP90 < 40 ~ "30-40 mg/l",
+        moyP90 < 40 ~ "18-40 mg/l",
         moyP90 < 50 ~ "40-50 mg/l",
         moyP90 < 60 ~ "50-60 mg/l",
         moyP90 < 70 ~ "60-70 mg/l",
-        moyP90 >=70 ~ "70 mg/l et plus"
+        moyP90 >= 70 ~ "70 mg/l et plus"
       )
     )
 }
@@ -411,12 +386,13 @@ prepa_nitrates_maxP90_bassin_versant_fct <- function(nitrates_bassin_versant){
       classe = dplyr::case_when(
         maxP90 < 10 ~ "0-10 mg/l",
         maxP90 < 18 ~ "10-18 mg/l",
-        maxP90 < 30 ~ "18-30 mg/l",
-        maxP90 < 40 ~ "30-40 mg/l",
+        # maxP90 < 30 ~ "18-30 mg/l",
+        # maxP90 < 40 ~ "30-40 mg/l",
+        maxP90 < 40 ~ "18-40 mg/l",
         maxP90 < 50 ~ "40-50 mg/l",
         maxP90 < 60 ~ "50-60 mg/l",
-        maxP90 <  70 ~ "60-70 mg/l",
-        maxP90 >=70 ~ "70 mg/l et plus"
+        maxP90 < 70 ~ "60-70 mg/l",
+        maxP90 >= 70 ~ "70 mg/l et plus"
       )
     )
 }
@@ -435,15 +411,19 @@ prepa_nitrates_maxP90_bassin_versant_fct <- function(nitrates_bassin_versant){
 #' @export
 #' @importFrom dplyr semi_join left_join select group_by summarise n mutate ungroup bind_rows rename
 #' @importFrom sf st_drop_geometry
-prepa_repartition_P90_fct <- function(nitrates_P90,
-                                      station_pdl,
-                                      n_sage_r52,
-                                      masse_eau_bassin_versant_PDL,
-                                      correspondance_sage_bassin_versant){
+prepa_repartition_P90_fct <- function(
+    nitrates_P90,
+    station_pdl,
+    n_sage_r52,
+    masse_eau_bassin_versant_PDL,
+    correspondance_sage_bassin_versant
+    ){
   repartition_P90_region <- nitrates_P90 %>%
     dplyr::semi_join(station_pdl) %>%
     dplyr::left_join(
-      station_pdl %>% dplyr::select(code_station, code_sage, code_bassin_versant, insee_dep) %>% sf::st_drop_geometry()
+      station_pdl %>%
+        dplyr::select(code_station, code_sage, code_bassin_versant, insee_dep) %>%
+        sf::st_drop_geometry()
     ) %>%
     dplyr::group_by(code_nature_eau, annee, classe) %>%
     dplyr::summarise(n = dplyr::n()) %>%
@@ -457,7 +437,9 @@ prepa_repartition_P90_fct <- function(nitrates_P90,
   repartition_P90_sage <- nitrates_P90 %>%
     dplyr::semi_join(station_pdl) %>%
     dplyr::left_join(
-      station_pdl %>% dplyr::select(code_station, code_sage, code_bassin_versant, insee_dep) %>% sf::st_drop_geometry()
+      station_pdl %>%
+        dplyr::select(code_station, code_sage, code_bassin_versant, insee_dep) %>%
+        sf::st_drop_geometry()
     ) %>%
     dplyr::group_by(code_nature_eau, code_sage, annee, classe) %>%
     dplyr::summarise(n = dplyr::n()) %>%
@@ -476,7 +458,9 @@ prepa_repartition_P90_fct <- function(nitrates_P90,
   repartition_P90_bassin_versant <- nitrates_P90 %>%
     dplyr::semi_join(station_pdl) %>%
     dplyr::left_join(
-      station_pdl %>% dplyr::select(code_station, code_sage, code_bassin_versant, insee_dep) %>% sf::st_drop_geometry()
+      station_pdl %>%
+        dplyr::select(code_station, code_sage, code_bassin_versant, insee_dep) %>%
+        sf::st_drop_geometry()
     ) %>%
     dplyr::group_by(code_nature_eau, code_bassin_versant, annee, classe) %>%
     dplyr::summarise(n = dplyr::n()) %>%
@@ -518,11 +502,13 @@ prepa_repartition_P90_fct <- function(nitrates_P90,
 #' @export
 #' @importFrom dplyr semi_join group_by summarise n mutate ungroup rename left_join select bind_rows
 #' @importFrom sf st_drop_geometry
-prepa_repartition_tendanceP90_fct <- function(nitrates_tendanceP90,
-                                              station_pdl,
-                                              n_sage_r52,
-                                              masse_eau_bassin_versant_PDL,
-                                              correspondance_sage_bassin_versant){
+prepa_repartition_tendanceP90_fct <- function(
+    nitrates_tendanceP90,
+    station_pdl,
+    n_sage_r52,
+    masse_eau_bassin_versant_PDL,
+    correspondance_sage_bassin_versant
+    ){
   repartition_tendanceP90_region <- nitrates_tendanceP90 %>%
     dplyr::semi_join(station_pdl) %>%
     dplyr::group_by(code_nature_eau, periode, tendance) %>%
@@ -541,7 +527,9 @@ prepa_repartition_tendanceP90_fct <- function(nitrates_tendanceP90,
   repartition_tendanceP90_sage <- nitrates_tendanceP90 %>%
     dplyr::semi_join(station_pdl) %>%
     dplyr::left_join(
-      station_pdl %>% dplyr::select(code_station, code_sage, code_bassin_versant) %>% sf::st_drop_geometry()
+      station_pdl %>%
+        dplyr::select(code_station, code_sage, code_bassin_versant) %>%
+        sf::st_drop_geometry()
     ) %>%
     dplyr::group_by(code_nature_eau, code_sage, periode,tendance) %>%
     dplyr::summarise(n = dplyr::n()) %>%
@@ -562,7 +550,9 @@ prepa_repartition_tendanceP90_fct <- function(nitrates_tendanceP90,
   repartition_tendanceP90_bassin_versant <- nitrates_tendanceP90 %>%
     dplyr::semi_join(station_pdl) %>%
     dplyr::left_join(
-      station_pdl %>% dplyr::select(code_station, code_sage, code_bassin_versant) %>% sf::st_drop_geometry()
+      station_pdl %>%
+        dplyr::select(code_station, code_sage, code_bassin_versant) %>%
+        sf::st_drop_geometry()
     ) %>%
     dplyr::group_by(code_nature_eau, code_bassin_versant, periode, tendance) %>%
     dplyr::summarise(n = dplyr::n()) %>%
@@ -603,9 +593,11 @@ prepa_repartition_tendanceP90_fct <- function(nitrates_tendanceP90,
 #' @export
 #' @importFrom dplyr group_by summarise n mutate ungroup rename left_join filter select bind_rows
 #' @importFrom sf st_drop_geometry
-prepa_repartition_moymaxBV_fct <- function(nitrates_moymaxP90_bassin_versant,
-                                           correspondance_sage_bassin_versant,
-                                           n_sage_r52){
+prepa_repartition_moymaxBV_fct <- function(
+    nitrates_moymaxP90_bassin_versant,
+    correspondance_sage_bassin_versant,
+    n_sage_r52
+    ){
   repartition_region <- nitrates_moymaxP90_bassin_versant %>%
     dplyr::group_by(code_nature_eau, annee, classe) %>%
     dplyr::summarise(n = dplyr::n()) %>%
@@ -678,11 +670,13 @@ prepa_nitrates_nature_eau_fct <- function(station_pdl, nature_eau){
 #' @export
 #' @importFrom dplyr group_by mutate n ungroup filter select left_join bind_rows mutate_if arrange
 #' @importFrom sf st_drop_geometry
-prepa_depassement_seuil_nature_eau_fct <- function(nitrates_nature_eau,
-                                                   seuil,
-                                                   station_pdl,
-                                                   n_sage_r52,
-                                                   masse_eau_bassin_versant_PDL){
+prepa_depassement_seuil_nature_eau_fct <- function(
+    nitrates_nature_eau,
+    seuil,
+    station_pdl,
+    n_sage_r52,
+    masse_eau_bassin_versant_PDL
+    ){
   depassement_seuil_region <- nitrates_nature_eau %>%
     dplyr::group_by(annee) %>%
     unique() %>%
@@ -701,10 +695,6 @@ prepa_depassement_seuil_nature_eau_fct <- function(nitrates_nature_eau,
     dplyr::mutate(nom_sage = "Tous", nom_bassin_versant = "Tous")
 
   depassement_seuil_sage <- nitrates_nature_eau %>%
-    dplyr::left_join(
-      station_pdl %>% dplyr::select(code_station, code_sage) %>%  sf::st_drop_geometry(),
-      by = "code_station"
-    ) %>%
     dplyr::group_by(code_sage, annee) %>%
     unique() %>%
     dplyr::mutate(nbre_mesure = dplyr::n()) %>%
@@ -728,10 +718,6 @@ prepa_depassement_seuil_nature_eau_fct <- function(nitrates_nature_eau,
     dplyr::select(-c(code_sage))
 
   depassement_seuil_bassin_versant <- nitrates_nature_eau %>%
-    dplyr::left_join(
-      station_pdl %>% dplyr::select(code_station, code_sage, code_bassin_versant) %>% sf::st_drop_geometry(),
-      by = "code_station"
-    ) %>%
     dplyr::group_by(code_bassin_versant, code_sage, annee) %>%
     unique() %>%
     dplyr::mutate(nbre_mesure = dplyr::n()) %>%
@@ -779,9 +765,11 @@ prepa_depassement_seuil_nature_eau_fct <- function(nitrates_nature_eau,
 #' @export
 #' @importFrom dplyr semi_join select left_join arrange
 #' @importFrom sf st_drop_geometry
-prepa_nitrates_telecharger_fct <- function(nitrates,
-                                           station_pdl,
-                                           correspondance_sage_bassin_versant){
+prepa_nitrates_telecharger_fct <- function(
+    nitrates,
+    station_pdl,
+    correspondance_sage_bassin_versant
+    ){
   nitrates_telecharger <- nitrates %>%
     dplyr::semi_join(station_pdl, by = c("code_station")) %>%
     dplyr::select(
@@ -792,10 +780,11 @@ prepa_nitrates_telecharger_fct <- function(nitrates,
       libelle_station,
       resultat_analyse,
       code_remarque,
-      source_prelevement
+      source_prelevement_analyse
     ) %>%
     dplyr::left_join(
-      station_pdl %>% sf::st_drop_geometry() %>%
+      station_pdl %>%
+        sf::st_drop_geometry() %>%
         dplyr::select(
           code_station,
           code_sage,
@@ -812,7 +801,7 @@ prepa_nitrates_telecharger_fct <- function(nitrates,
       code_sage, nom_sage,
       code_bassin_versant, nom_bassin_versant,
       code_station, libelle_station,
-      source_prelevement, resultat_analyse, code_remarque,
+      source_prelevement_analyse, resultat_analyse, code_remarque,
       insee_dep, code_commune,
       captage_prioritaire
     ) %>%
@@ -831,9 +820,11 @@ prepa_nitrates_telecharger_fct <- function(nitrates,
 #' @export
 #' @importFrom dplyr semi_join select left_join arrange
 #' @importFrom sf st_drop_geometry
-prepa_nitrates_P90_telecharger_fct <- function(nitrates_P90,
-                                               station_pdl,
-                                               correspondance_sage_bassin_versant){
+prepa_nitrates_P90_telecharger_fct <- function(
+    nitrates_P90,
+    station_pdl,
+    correspondance_sage_bassin_versant
+    ){
   nitrates_P90_telecharger <- nitrates_P90 %>%
     dplyr::semi_join(station_pdl, by = c("code_station")) %>%
     dplyr::select(
@@ -845,7 +836,8 @@ prepa_nitrates_P90_telecharger_fct <- function(nitrates_P90,
       classe
     ) %>%
     dplyr::left_join(
-      station_pdl %>% sf::st_drop_geometry() %>%
+      station_pdl %>%
+        sf::st_drop_geometry() %>%
         dplyr::select(
           code_station,
           code_sage,
@@ -881,13 +873,16 @@ prepa_nitrates_P90_telecharger_fct <- function(nitrates_P90,
 #' @export
 #' @importFrom dplyr semi_join select left_join arrange
 #' @importFrom sf st_drop_geometry
-prepa_nitrates_tendanceP90_telecharger_fct <- function(nitrates_tendanceP90,
-                                                       station_pdl,
-                                                       correspondance_sage_bassin_versant){
+prepa_nitrates_tendanceP90_telecharger_fct <- function(
+    nitrates_tendanceP90,
+    station_pdl,
+    correspondance_sage_bassin_versant
+    ){
   nitrates_tendanceP90_telecharger <- nitrates_tendanceP90 %>%
     dplyr::semi_join(station_pdl, by = c("code_station")) %>%
     dplyr::left_join(
-      station_pdl %>% sf::st_drop_geometry() %>%
+      station_pdl %>%
+        sf::st_drop_geometry() %>%
         dplyr::select(
           code_station,
           code_sage,
@@ -921,8 +916,10 @@ prepa_nitrates_tendanceP90_telecharger_fct <- function(nitrates_tendanceP90,
 #'
 #' @export
 #' @importFrom dplyr left_join select arrange
-prepa_nitrates_moyP90_bassin_versant_telecharger_fct <- function(nitrates_moyP90_bassin_versant,
-                                                                 correspondance_sage_bassin_versant){
+prepa_nitrates_moyP90_bassin_versant_telecharger_fct <- function(
+    nitrates_moyP90_bassin_versant,
+    correspondance_sage_bassin_versant
+    ){
   nitrates_moyP90_bassin_versant_telecharger <- nitrates_moyP90_bassin_versant %>%
     dplyr::left_join(correspondance_sage_bassin_versant) %>%
     dplyr::select(
@@ -946,8 +943,10 @@ prepa_nitrates_moyP90_bassin_versant_telecharger_fct <- function(nitrates_moyP90
 #'
 #' @export
 #' @importFrom dplyr left_join select arrange
-prepa_nitrates_maxP90_bassin_versant_telecharger_fct <- function(nitrates_maxP90_bassin_versant,
-                                                                 correspondance_sage_bassin_versant){
+prepa_nitrates_maxP90_bassin_versant_telecharger_fct <- function(
+    nitrates_maxP90_bassin_versant,
+    correspondance_sage_bassin_versant
+    ){
   nitrates_maxP90_bassin_versant_telecharger <- nitrates_maxP90_bassin_versant %>%
     dplyr::left_join(correspondance_sage_bassin_versant) %>%
     dplyr::select(
diff --git a/R/globals.R b/R/globals.R
index 9dd8d4b0fc624d28abe79f55ec4e64973be1cab7..6465efa0c343704f816e0ff1657ad47fa0ff3c34 100644
--- a/R/globals.R
+++ b/R/globals.R
@@ -40,7 +40,8 @@ utils::globalVariables(
 
     "bassin_versant_pa_telecharger", "departement_pa_telecharger",
     "depassement", "nbre_depassement", "non_depassement",
-    "captage_prioritaire"
-
+    "captage_prioritaire",
+    "code_prelevement_analyse", "source_prelevement_analyse",
+    "code_bss", "code_naiades", "code_sise_eaux"
   )
 )
diff --git a/data-raw/explo_prepa_data.R b/data-raw/explo_prepa_data.R
index c08024727a6c448f41b29df89d2b8c828a635d4d..f78fcf333d9f990e1eee20f5731b63205608c92a 100644
--- a/data-raw/explo_prepa_data.R
+++ b/data-raw/explo_prepa_data.R
@@ -6,7 +6,7 @@ rm(list=ls())
 nitrate_prelevement_analyse <- datalibaba::importer_data(
   db = "si_eau",
   schema = "nitrates",
-  table = "nitrate_prelevement_analyse_v0_20"
+  table = "nitrate_prelevement_analyse_v0_21"
 ) # 83882 obs
 # names(nitrate_prelevement_analyse)
 # [1] "code_prelevement_analyse" "code_intervenant"         "source"                   "code_reseau"
@@ -77,29 +77,27 @@ nitrates_station <- nitrates_station %>%
   ) %>%
   dplyr::select(code_station, tidyr::everything()) # 81721 obs
 
-# dplyr::filter(nitrates_station, captage_prioritaire == "TRUE") %>%
-#   dplyr::pull(code_station) %>%
-#   unique()
-# [1] "035000242"  "BSS000TRNA" "BSS000VVKA" "BSS000VXRH" "BSS000VXSR" "BSS000XWUM" "BSS000XWVU" "BSS000XWVX" "BSS000XXAR"
-# [10] "BSS000XXLZ" "BSS000XXXJ" "BSS000XYQY" "BSS000ZRMP" "BSS000ZRWX" "BSS000ZSMH" "BSS000ZSMK" "BSS000ZSZS" "BSS000ZTAB"
-# [19] "BSS000ZTJM" "BSS000ZTKU" "BSS000ZWLX" "BSS001BNWY" "BSS001DMXF" "BSS001EUDY" "BSS001EUMJ" "BSS001EYVU" "BSS001FDCD"
-# [28] "BSS001HKWP" "BSS001HLXD" "BSS001HMKY" "BSS001JPLC" "BSS001JZRT" "BSS001LFKF" "BSS001NLEZ" "BSS001NLXS" "044000102"
-# [37] "044000449"  "049000024"  "049000388"  "049000402"  "053000091"  "053000106"  "085000047"  "085000094"  "085000410"
-# [46] "085000446"
+dplyr::filter(nitrates_station, captage_prioritaire == "TRUE") %>%
+  dplyr::pull(code_station) %>%
+  unique()
+# [1] "035000242"  "BSS000TRNA" "BSS000VVKA" "BSS000VXRH" "BSS000VXSR" "BSS000XWUM" "BSS000XWVU" "BSS000XWVX" "BSS000XXAR" "BSS000XXLZ" "BSS000XXXJ"
+# [12] "BSS000XYQY" "BSS000ZRMP" "BSS000ZRWX" "BSS000ZSMH" "BSS000ZSMK" "BSS000ZSZS" "BSS000ZTAB" "BSS000ZTJM" "BSS000ZTKU" "BSS000ZWLX" "BSS001BNWY"
+# [23] "BSS001DLVY" "BSS001DMXF" "BSS001EUDY" "BSS001EUMJ" "BSS001EYVU" "BSS001FDCD" "BSS001HKWP" "BSS001HLXD" "BSS001HMKY" "BSS001JPLC" "BSS001JZRT"
+# [34] "BSS001LFKF" "BSS001NLEZ" "BSS001NLXS" "044000102"  "044000449"  "049000024"  "049000388"  "049000402"  "053000091"  "053000106"  "085000047"
+# [45] "085000094"  "085000410"  "085000446"
 
-# dplyr::filter(station_mesure, captage_prioritaire == "TRUE") %>%
-#   dplyr::mutate(
-#     code_station = dplyr::case_when(
-#       nature_eau == "ESU" ~ code_sise_eaux,
-#       nature_eau == "ESO" & is.na(code_bss) | code_bss == "00000X0000" | code_bss == "XXX" ~ code_sise_eaux,
-#       nature_eau == "ESO" & !is.na(code_bss) ~ code_bss
-#     )
-#   ) %>%
-#   dplyr::pull(code_station) %>%
-#   unique()
-# [1] "BSS001JPLC" "BSS001EUMJ" "044000449"  "BSS001EUDY" "044000102"  "BSS001DMXF" "BSS001HLXD" "BSS001FDCD" "BSS001LFKF"
-# [10] "049000388"  "BSS001HMKY" "BSS001EYVU" "BSS001JZRT" "BSS001HKWP" "049000402"  "049000024"  "BSS000ZSZS" "BSS000ZSMH"
-# [19] "BSS001BNWY" "BSS000TRNA" "053000091"  "053000106"  "035000242"  "BSS000ZRWX" "BSS000VVKA" "BSS000ZRMP" "BSS000XWUM"
-# [28] "BSS000XWVX" "BSS000ZTAB" "BSS000XXAR" "BSS000ZSMK" "BSS000XXXJ" "BSS000ZTKU" "BSS000XXLZ" "BSS000XWVU" "BSS000VXRH"
-# [37] "BSS000XYQY" "BSS000ZTJM" "BSS000VXSR" "BSS000ZWLX" "085000047"  "085000410"  "085000446"  "085000094"  "BSS001NLEZ"
-# [46] "BSS001NLXS"
+dplyr::filter(station_mesure, captage_prioritaire == "TRUE") %>%
+  dplyr::mutate(
+    code_station = dplyr::case_when(
+      nature_eau == "ESU" ~ code_sise_eaux,
+      nature_eau == "ESO" & is.na(code_bss) | code_bss == "00000X0000" | code_bss == "XXX" ~ code_sise_eaux,
+      nature_eau == "ESO" & !is.na(code_bss) ~ code_bss
+    )
+  ) %>%
+  dplyr::pull(code_station) %>%
+  unique()
+# [1] "BSS001JPLC" "BSS001EUMJ" "044000449"  "BSS001EUDY" "044000102"  "BSS001DMXF" "BSS001HLXD" "BSS001FDCD" "BSS001LFKF" "049000388"  "BSS001HMKY"
+# [12] "BSS001EYVU" "BSS001JZRT" "BSS001HKWP" "049000402"  "049000024"  "BSS000ZSZS" "BSS000ZSMH" "BSS001BNWY" "053000091"  "053000106"  "BSS000ZRWX"
+# [23] "BSS000VVKA" "BSS000ZRMP" "BSS000XWUM" "BSS000XWVX" "BSS000ZTAB" "BSS000XXAR" "BSS000ZSMK" "BSS000XXXJ" "BSS000ZTKU" "BSS000XXLZ" "BSS000XWVU"
+# [34] "BSS000VXRH" "BSS000XYQY" "BSS000ZTJM" "BSS000VXSR" "BSS000ZWLX" "085000047"  "085000410"  "085000446"  "085000094"  "BSS001NLEZ" "BSS001NLXS"
+# [45] "BSS000TRNA" "035000242"  "BSS001DLVY"
diff --git a/data-raw/prepa_data.R b/data-raw/prepa_data.R
index 20deabb764967d42ca9e16c2f2778f05df4fdc12..aeb4a5da533f5ad71117761a85cdeaa377db29a6 100644
--- a/data-raw/prepa_data.R
+++ b/data-raw/prepa_data.R
@@ -5,88 +5,83 @@ rm(list=ls())
 
 
 # chargement des données ------------
-nitrate_analyse <- datalibaba::importer_data(
+nitrate_prelevement_analyse <- datalibaba::importer_data(
   db = "si_eau",
   schema = "nitrates",
-  table = "nitrate_analyse_v0_17"
-  ) # 82725 obs
-# names(nitrate_analyse)
-# [1] "code_analyse"           "code_intervenant"       "code_prelevement"       "code_parametre"         "code_fraction_analysee"
-# [6] "date_analyse"           "resultat_analyse"       "code_remarque"          "limite_detection"       "limite_quantification"
-
-nitrate_prelevement <- datalibaba::importer_data(
-  db = "si_eau",
-  schema = "nitrates",
-  table = "nitrate_prelevement_v0_17"
-  ) # 82343 obs
-# names(nitrate_prelevement)
-# 1] "code_prelevement"     "code_intervenant"     "source"               "code_reseau"          "code_station"         "date_prelevement"
-# [7] "heure_prelevement"    "code_support"         "id_usage"             "id_prelevement_motif" "commentaire"
+  table = "nitrate_prelevement_analyse_v0_21"
+) # 83882 obs
+# names(nitrate_prelevement_analyse)
+# [1] "code_prelevement_analyse" "code_intervenant"         "source"                   "code_reseau"
+# [5] "code_station"             "date_prelevement"         "heure_prelevement"        "code_support"
+# [9] "nature_eau"               "id_usage"                 "id_prelevement_motif"     "date_analyse"
+# [13] "resultat_analyse"         "code_parametre"           "code_fraction_analysee"   "code_remarque"
+# [17] "limite_detection"         "limite_quantification"
+
+nitrate_prelevement_analyse <- dplyr::rename(
+  nitrate_prelevement_analyse,
+  source_prelevement_analyse = source
+  )
 
 
 # création de la table nitrates ---------
-nitrates <- dplyr::inner_join(
-  nitrate_prelevement,
-  nitrate_analyse,
-  by = c("code_prelevement")
-) %>%
-  dplyr::mutate(
-    annee = lubridate::year(date_prelevement),
-    mois = lubridate::month(date_prelevement)
-  ) # 82693 obs
-
-nitrates_esu <- prepa_nitrates_fct(station_code_nature_eau = "ESU") # 58610 obs
-nitrates_eso <- prepa_nitrates_fct(station_code_nature_eau = "ESO") # 21010 obs
-
-nitrates <- dplyr::bind_rows(
-  nitrates_esu %>% dplyr::mutate(code_nature_eau = "ESU"),
-  nitrates_eso %>% dplyr::mutate(code_nature_eau = "ESO")
-) # 79620 obs
-
-# unique(nitrates$annee)
-# [1] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
 
-
-# un peu de ménage intermédiaire ---------
-rm(
-   nitrate_analyse,
-   nitrate_prelevement,
-   nitrates_esu,
-   nitrates_eso
-   )
+nitrates <- nitrate_prelevement_analyse %>%
+    dplyr::mutate(
+      annee = lubridate::year(date_prelevement),
+      mois = lubridate::month(date_prelevement)
+      ) %>%
+  dplyr::rename(code_nature_eau = nature_eau)
+
+nitrates_code_bss <- station_pdl %>%
+  sf::st_drop_geometry() %>%
+  dplyr::inner_join(
+    nitrates,
+    by = c("code_bss" = "code_station", "code_nature_eau")
+  ) # 4771 obs
+
+nitrates_code_sise_eaux <- station_pdl %>%
+  sf::st_drop_geometry() %>%
+  dplyr::inner_join(
+    nitrates,
+    by = c("code_sise_eaux" = "code_station", "code_nature_eau")
+  ) # 76397 obs
+
+nitrates_code_naiades <- station_pdl %>%
+  sf::st_drop_geometry() %>%
+  dplyr::inner_join(
+    nitrates,
+    by = c("code_naiades" = "code_station", "code_nature_eau")
+  ) # 357 obs
+
+nitrates_station <- dplyr::bind_rows(
+  nitrates_code_bss,
+  nitrates_code_sise_eaux,
+  nitrates_code_naiades
+  ) %>% # 81525 obs
+  dplyr::group_by(
+    code_bss, code_sise_eaux, code_naiades, libelle_station, code_nature_eau, source_station,
+    code_masse_eau, code_bassin_versant, code_commune, code_sage,
+    captage_prioritaire,
+    source_prelevement_analyse, code_prelevement_analyse, code_intervenant, date_analyse, resultat_analyse
+    ) %>%
+  dplyr::slice(1) %>%
+  dplyr::ungroup() # 81525 obs
+
+nitrates <- nitrates_station
+
+# unique(nitrates$annee) %>% sort()
+# [1] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
 
 
 # calcul P90 ----------------
 nitrates_P90 <- prepa_nitrates_P90_fct(nitrates)
-# 12766 obs dont 6104 esu et 6662 eso
+# 12848 obs dont 6101 esu et 6747 eso
 
 
 # calcul de la tendance P90 -----------------------------
 nitrates_tendanceP90 <- purrr::map(c(2016:2023), ~prepa_creer_tendanceP90(.x)) %>%
   dplyr::bind_rows()
-# 10078 obs dont 5268 esu et 4810 eso
-
-# Warning messages:
-#   1: There was 1 warning in `dplyr::mutate()`.
-# ℹ In argument: `determination = as.numeric(summary(Mods)[8])`.
-# ℹ In row 435.
-# Caused by warning in `summary.lm()`:
-#   ! ajustement pratiquement parfait : le résumé n’est peut-être pas fiable
-# 2: There was 1 warning in `dplyr::mutate()`.
-# ℹ In argument: `determination = as.numeric(summary(Mods)[8])`.
-# ℹ In row 439.
-# Caused by warning in `summary.lm()`:
-#   ! ajustement pratiquement parfait : le résumé n’est peut-être pas fiable
-# 3: There was 1 warning in `dplyr::mutate()`.
-# ℹ In argument: `determination = as.numeric(summary(Mods)[8])`.
-# ℹ In row 453.
-# Caused by warning in `summary.lm()`:
-#   ! ajustement pratiquement parfait : le résumé n’est peut-être pas fiable
-# 4: There was 1 warning in `dplyr::mutate()`.
-# ℹ In argument: `determination = as.numeric(summary(Mods)[8])`.
-# ℹ In row 490.
-# Caused by warning in `summary.lm()`:
-#   ! ajustement pratiquement parfait : le résumé n’est peut-être pas fiable
+# 23450 obs dont 12686 esu et 10764 eso
 
 nitrates_tendanceP90 <- nitrates_tendanceP90 %>%
   dplyr::select(code_station, libelle_station, code_nature_eau, tendance, periode) %>%
@@ -100,24 +95,13 @@ periode_nitrates_tendanceP90 <- prepa_periode_nitrates_tendanceP90_fct(2023)
 # calcul de la moyenne et du maximum des P90 sur un bassin versant ----------
 nitrates_bassin_versant <- prepa_nitrates_bassin_versant_fct(nitrates_P90) %>%
   dplyr::filter(!is.na(code_bassin_versant))
-# 6202 obs dont 3749 obs esu et 2453 eso
-
-# 0 station dont le code_bassin_versant n'est pas renseigné
-# nitrates_P90 %>%
-#   dplyr::semi_join(station_pdl) %>%
-#   dplyr::left_join(
-#     station_pdl %>% dplyr::select(c(code_station, libelle_station, code_bassin_versant)) %>% sf::st_drop_geometry(),
-#     by = c("code_station", "libelle_station")
-#   ) %>%
-#   dplyr::filter(is.na(code_bassin_versant)) %>%
-#   dplyr::pull(code_station) %>%
-#   unique()
-# character(0)
+# 6231 obs dont 3765 obs esu et 2466 eso
 
 nitrates_moyP90_bassin_versant <- prepa_nitrates_moyP90_bassin_versant_fct(nitrates_bassin_versant)
-# 6202 obs
+# 6231 obs
+
 nitrates_maxP90_bassin_versant <- prepa_nitrates_maxP90_bassin_versant_fct(nitrates_bassin_versant)
-# 6202 obs
+# 6231 obs
 
 
 # répartition P90 ------------
@@ -127,7 +111,7 @@ repartition_P90 <- prepa_repartition_P90_fct(
   n_sage_r52,
   masse_eau_bassin_versant_PDL,
   correspondance_sage_bassin_versant
-  ) # 12259 obs
+  ) # 11505 obs
 
 
 # répartition tendance P90 -----------
@@ -137,7 +121,7 @@ repartition_tendanceP90 <- prepa_repartition_tendanceP90_fct(
   n_sage_r52,
   masse_eau_bassin_versant_PDL,
   correspondance_sage_bassin_versant
-  ) # 7224 obs
+  ) # 7254 obs
 
 
 # répartition moyenne et maximum BV --------
@@ -145,20 +129,21 @@ repartition_moyBV <- prepa_repartition_moymaxBV_fct(
   nitrates_moyP90_bassin_versant,
   correspondance_sage_bassin_versant,
   n_sage_r52
-  ) # 2829 obs
+  ) # 2431 obs
 
 repartition_maxBV <- prepa_repartition_moymaxBV_fct(
   nitrates_maxP90_bassin_versant,
   correspondance_sage_bassin_versant,
   n_sage_r52
-  ) # 2963 obs
+  ) # 2580 obs
 
 
 # concentration -----------------
 nitrates_esu <- prepa_nitrates_nature_eau_fct(station_pdl, "ESU")
-# 58610 obs
+# 59645 obs
+
 nitrates_eso <- prepa_nitrates_nature_eau_fct(station_pdl, "ESO")
-# 21010 obs
+# 21880 obs
 
 depassement_seuil_esu <- prepa_depassement_seuil_nature_eau_fct(
   nitrates_esu,
@@ -167,7 +152,7 @@ depassement_seuil_esu <- prepa_depassement_seuil_nature_eau_fct(
   n_sage_r52,
   masse_eau_bassin_versant_PDL
   )
-# 3662 obs
+# 3694 obs
 
 depassement_seuil_eso <- prepa_depassement_seuil_nature_eau_fct(
   nitrates_eso,
@@ -176,11 +161,12 @@ depassement_seuil_eso <- prepa_depassement_seuil_nature_eau_fct(
   n_sage_r52,
   masse_eau_bassin_versant_PDL
   )
-# 920 obs
+# 921 obs
 
 
 # choix des couleurs -----------
-couleur <- c("#33CCFF", "#00CC33", "#66FF99", "#FFFF00", "orange", "red", "#A10684", "#723E64")
+# couleur <- c("#33CCFF", "#00CC33", "#66FF99", "#FFFF00", "orange", "red", "#A10684", "#723E64")
+couleur <- c("#33CCFF", "#00CC33", "#FFFF00", "orange", "red", "#A10684", "#723E64")
 facteur <- nitrates_P90 %>%
   dplyr::arrange(classe) %>%
   dplyr::pull(classe) %>%
@@ -208,20 +194,24 @@ nitrates_telecharger <- prepa_nitrates_telecharger_fct(
   station_pdl,
   correspondance_sage_bassin_versant
   )
+
 nitrates_P90_telecharger <- prepa_nitrates_P90_telecharger_fct(
   nitrates_P90,
   station_pdl,
   correspondance_sage_bassin_versant
   )
+
 nitrates_tendanceP90_telecharger <- prepa_nitrates_tendanceP90_telecharger_fct(
   nitrates_tendanceP90,
   station_pdl,
   correspondance_sage_bassin_versant
   )
+
 nitrates_moyP90_bassin_versant_telecharger <- prepa_nitrates_moyP90_bassin_versant_telecharger_fct(
   nitrates_moyP90_bassin_versant,
   correspondance_sage_bassin_versant
   )
+
 nitrates_maxP90_bassin_versant_telecharger <- prepa_nitrates_maxP90_bassin_versant_telecharger_fct(
   nitrates_maxP90_bassin_versant,
   correspondance_sage_bassin_versant
@@ -277,22 +267,24 @@ usethis::use_data(
 
 
 # # versement sur le sgbd/datamart/nitrates ------
+
 datalibaba::poster_data(
   data = nitrates_P90,
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_p90_v0_17",
+  table = "nitrates_p90_v0_21",
   pk = c("code_station", "libelle_station", "annee"),
   post_row_name = FALSE,
   overwrite = TRUE,
   droits_schema = TRUE,
   user = "does"
   )
+
 datalibaba::commenter_table(
   comment = "calcul du P90 par station",
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_p90_v0_17",
+  table = "nitrates_p90_v0_21",
   user = "does"
   )
 
@@ -300,18 +292,19 @@ datalibaba::poster_data(
   data = nitrates_tendanceP90,
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_tendancep90_v0_17",
+  table = "nitrates_tendancep90_v0_21",
   pk = c("code_station", "libelle_station", "periode"),
   post_row_name = FALSE,
   overwrite = TRUE,
   droits_schema = TRUE,
   user = "does"
   )
+
 datalibaba::commenter_table(
   comment = "calcul de la tendance P90 par station",
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_tendancep90_v0_17",
+  table = "nitrates_tendancep90_v0_21",
   user = "does"
   )
 
@@ -319,7 +312,7 @@ datalibaba::poster_data(
   data = nitrates_moyP90_bassin_versant,
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_moyp90_bassin_versant_v0_17",
+  table = "nitrates_moyp90_bassin_versant_v0_21",
   pk = c("code_bassin_versant", "annee", "code_nature_eau"),
   post_row_name = FALSE,
   overwrite = TRUE,
@@ -330,7 +323,7 @@ datalibaba::commenter_table(
   comment = "calcul de la moyenne P90 par bassin versant",
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_moyp90_bassin_versant_v0_17",
+  table = "nitrates_moyp90_bassin_versant_v0_21",
   user = "does"
   )
 
@@ -338,18 +331,19 @@ datalibaba::poster_data(
   data = nitrates_maxP90_bassin_versant,
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_maxp90_bassin_versant_v0_17",
+  table = "nitrates_maxp90_bassin_versant_v0_21",
   pk = c("code_bassin_versant", "annee", "code_nature_eau"),
   post_row_name = FALSE,
   overwrite = TRUE,
   droits_schema = TRUE,
   user = "does"
   )
+
 datalibaba::commenter_table(
   comment = "calcul du maximum P90 par bassin versant",
   db = "datamart",
   schema = "nitrates",
-  table = "nitrates_maxp90_bassin_versant_v0_17",
+  table = "nitrates_maxp90_bassin_versant_v0_21",
   user = "does"
   )
 
diff --git a/data-raw/prepa_pression_azotee.R b/data-raw/prepa_pression_azotee.R
index fe0b210dc4748d9897d97b0e6936ae31b3377528..0044db74ebadb089a703524ab3e5e73e97c1233f 100644
--- a/data-raw/prepa_pression_azotee.R
+++ b/data-raw/prepa_pression_azotee.R
@@ -4,13 +4,28 @@ library(nitrates.pdl)
 rm(list=ls())
 
 
-# importation des données de la DRAAF --------
+# importation des données de la DRAAF 2023 pm --------
 # Departement_2023 <- utils::read.csv2("inst/extdata/Departement_2023.csv")
 # names(Departement_2023)
 # [1] "ANNEE_DECLARATION"      "Nom"                    "Code"                   "SAU_recensee"           "QTE_ORG_MIN"
 # [6] "QTE_N_MIN_EPAN"         "ORG_EPAN_THEORIQ"       "Comptage"               "pression_azote_globale" "pression_azote_mineral"
 # [11] "pression_azote_orga"
 
+# ME_2023 <- utils::read.csv2("inst/extdata/ME_2023.csv")
+# names(ME_2023)
+# [1] "ANNEE_DECLARATION"      "Nom"                    "Code"                   "SAU_recensee"           "QTE_ORG_MIN"
+# [6] "QTE_N_MIN_EPAN"         "ORG_EPAN_THEORIQ"       "Comptage"               "pression_azote_globale" "pression_azote_mineral"
+# [11] "pression_azote_orga"
+
+# SAGE_2023 <- utils::read.csv2("inst/extdata/SAGE_2023.csv") %>%
+# dplyr::mutate(Code = as.character(Code))
+# names(SAGE_2023)
+# [1] "ANNEE_DECLARATION"      "Nom"                    "Code"                   "SAU_recensee"           "QTE_ORG_MIN"
+# [6] "QTE_N_MIN_EPAN"         "ORG_EPAN_THEORIQ"       "Comptage"               "pression_azote_globale" "pression_azote_mineral"
+# [11] "pression_azote_orga"
+
+
+# importation des données de la DRAAF 2024 --------
 Departement_2024 <- utils::read.csv2("inst/extdata/Departement_2024.csv")
 # names(Departement_2024)
 # [1] "ANNEE_DECLARATION"              "DEPARTEMENT"                    "Code"                           "SAU_recensee"
@@ -18,12 +33,6 @@ Departement_2024 <- utils::read.csv2("inst/extdata/Departement_2024.csv")
 # [9] "nb_occurences"                  "pression_azote_globale"         "pression_azote_mineral"         "pression_azote_orga"
 # [13] "pression_azote_orga_non_maitri"
 
-# ME_2023 <- utils::read.csv2("inst/extdata/ME_2023.csv")
-# names(ME_2023)
-# [1] "ANNEE_DECLARATION"      "Nom"                    "Code"                   "SAU_recensee"           "QTE_ORG_MIN"
-# [6] "QTE_N_MIN_EPAN"         "ORG_EPAN_THEORIQ"       "Comptage"               "pression_azote_globale" "pression_azote_mineral"
-# [11] "pression_azote_orga"
-
 ME_2024 <- utils::read.csv2("inst/extdata/ME_2024.csv")
 # names(ME_2024)
 # [1] "ANNEE_DECLARATION"              "CODE_ME"                        "Nom"                            "SAU_recensee"
@@ -31,13 +40,6 @@ ME_2024 <- utils::read.csv2("inst/extdata/ME_2024.csv")
 # [9] "Comptage"                       "pression_azote_globale"         "pression_azote_mineral"         "pression_azote_orga"
 # [13] "pression_azote_orga_non_maitri"
 
-# SAGE_2023 <- utils::read.csv2("inst/extdata/SAGE_2023.csv") %>%
-  dplyr::mutate(Code = as.character(Code))
-# names(SAGE_2023)
-# [1] "ANNEE_DECLARATION"      "Nom"                    "Code"                   "SAU_recensee"           "QTE_ORG_MIN"
-# [6] "QTE_N_MIN_EPAN"         "ORG_EPAN_THEORIQ"       "Comptage"               "pression_azote_globale" "pression_azote_mineral"
-# [11] "pression_azote_orga"
-
 SAGE_2024 <- utils::read.csv2("inst/extdata/SAGE_2024.csv") %>%
   dplyr::mutate(Code = as.character(Code))
 # names(SAGE_2024)
@@ -158,10 +160,15 @@ palette_pa <- gouvdown::gouv_colors("q0", "o0", "o1", "o2", "n2", "l2")
 bassin_versant_col_pa <- bassin_versant_carte_pa %>%
   sf::st_drop_geometry() %>%
   dplyr::select(-pression_azote_globale, -classe) %>%
+  dplyr::rename(
+    minerale = pression_azote_mineral,
+    organique = pression_azote_orga
+  ) %>%
   tidyr::gather(
     key = "pression",
     value = "valeur",
-    pression_azote_mineral, pression_azote_orga
+    # pression_azote_mineral, pression_azote_orga
+    minerale, organique
     ) %>%
   dplyr::mutate(valeur = round(valeur, digits = 0)) %>%
   dplyr::arrange(
@@ -169,17 +176,21 @@ bassin_versant_col_pa <- bassin_versant_carte_pa %>%
     nom_bassin_versant,
     annee_declaration
     )
-usethis::use_data(bassin_versant_col_pa, choix_couleur_col_pa, overwrite = TRUE)
+
+usethis::use_data(bassin_versant_col_pa, overwrite = TRUE)
 # utilitaires.ju::use_data_doc(
 #   "bassin_versant_col_pa",
 #   description = "pression azotée (minérale, organique) par bassin versant par campagne culturale", source = "DREAL"
 # )
 
 choix_couleur_col_pa <- data.frame(
-  pression = c("pression_azote_mineral", "pression_azote_orga"),
+  # pression = c("pression_azote_mineral", "pression_azote_orga"),
+  pression = c("minerale", "organique"),
   couleur = c(gouvdown::gouv_colors("o0"), gouvdown::gouv_colors("l1"))
 ) %>%
   dplyr::pull(couleur)
+
+usethis::use_data(choix_couleur_col_pa, overwrite = TRUE)
 # utilitaires.ju::use_data_doc(
 #   "choix_couleur_col_pa",
 #   description = "choix des couleurs pour diagramme des pressions azotées (minérale, organique) par bassin versant par campagne culturale",
@@ -187,8 +198,8 @@ choix_couleur_col_pa <- data.frame(
 # )
 
 # exemple pour voir
-plot_bv <- plot_bv_col_pa_fct(nom = "Baie de Bourgneuf")
-plot_bv
+# plot_bv <- plot_bv_col_pa_fct(nom = "Baie de Bourgneuf")
+# plot_bv
 
 
 # diagramme colonnes départements --------------
@@ -199,14 +210,15 @@ departement_col_pa <- Departement %>%
     annee_declaration = ANNEE_DECLARATION,
     Nom = DEPARTEMENT,
     Code,
-    pression_azote_mineral,
-    pression_azote_orga
-  ) %>%
+    minerale = pression_azote_mineral,
+    organique = pression_azote_orga
+    ) %>%
   tidyr::gather(
     key = "pression",
     value = "valeur",
-    pression_azote_mineral, pression_azote_orga
-  ) %>%
+    # pression_azote_mineral, pression_azote_orga
+    minerale, organique
+    ) %>%
   dplyr::mutate(valeur = round(valeur, digits = 0)) %>%
   dplyr::mutate(Code = ifelse(Code == 303, 90, Code)) %>%
   dplyr::arrange(
diff --git a/data-raw/prepa_referentiel.R b/data-raw/prepa_referentiel.R
index 9316f5808a68bc5e30aa63e6cbd6a1c79d633b1e..1de598b9ffd33ff72334d5b640e4725e7ad188f2 100644
--- a/data-raw/prepa_referentiel.R
+++ b/data-raw/prepa_referentiel.R
@@ -179,37 +179,50 @@ bv_hors_correspondance_2 <- dplyr::anti_join(
 
 
 # stations de mesure -----------
-station_esu <- datalibaba::importer_data(
+station_mesure <- datalibaba::importer_data(
   db = "si_eau",
   schema = "stations",
-  table = "station_esu"
-  )  # 3675 obs
+  table = "r_station_mesure_p_2024_r52"
+) # 7047 obs
+# names(station_mesure)
+# [1] "id_station"          "code_bss"            "code_sise_eaux"      "code_naiades"        "libelle_station"
+# [6] "nature_eau"          "date_creation"       "source"              "code_masse_eau"      "code_bassin_versant"
+# [11] "code_commune"        "code_sage"           "captage_prioritaire" "the_geom"
 
-station_eso <- datalibaba::importer_data(
-  db = "si_eau",
-  schema = "stations",
-  table = "station_eso"
-  ) # 2034 obs
+station_mesure <- dplyr::rename(station_mesure, source_station = source)
+
+station_esu <- dplyr::filter(station_mesure, nature_eau == "ESU") # 3673 obs
+station_eso <- dplyr::filter(station_mesure, nature_eau == "ESO") # 3374 obs
 
 # mention du département, et restriction au périmètre régional et aux géométries définies
-station_esu_PDL <- prepa_station_PDL_fct(station_esu) # 1835 obs
-station_eso_PDL <- prepa_station_PDL_fct(station_eso) # 2027 obs
+station_esu_PDL <- prepa_station_PDL_fct(station_esu) # 1833 obs
+station_eso_PDL <- prepa_station_PDL_fct(station_eso) # 2049 obs
 
 station <- dplyr::bind_rows(
   station_esu,
-  station_eso %>% dplyr::select(-code_sise_eaux)
-  ) # 5709 obs
+  station_eso
+  ) # 7047 obs
 
 station_pdl <- dplyr::bind_rows(
   station_esu_PDL %>%
-    dplyr::mutate(code_nature_eau = "ESU") %>%
+    dplyr::rename(code_nature_eau = nature_eau) %>%
     dplyr::mutate(
       etoile = ifelse(startsWith(libelle_station,"* A"), "oui", "non")
     ) %>%
     dplyr::filter(etoile == "non") %>% # élimination de stations qui ne se rattachent à aucun prélèvement
     dplyr::select(-etoile),
-  station_eso_PDL %>% dplyr::mutate(code_nature_eau = "ESO") %>% dplyr::select(-code_sise_eaux)
-  ) # 3850 obs
+  station_eso_PDL %>%
+    dplyr::rename(code_nature_eau = nature_eau)
+  ) %>%
+  dplyr::mutate(
+    code_station = dplyr::case_when(
+      code_nature_eau == "ESU" ~ code_sise_eaux,
+      code_nature_eau == "ESO" & is.na(code_bss) | code_bss == "00000X0000" | code_bss == "XXX" ~ code_sise_eaux,
+      code_nature_eau == "ESO" & !is.na(code_bss) ~ code_bss
+    )
+  ) %>%
+  dplyr::select(-id_station) %>%
+  dplyr::select(code_station, tidyr::everything()) # 3870 obs
 
 
 # un peu de ménage ---------
@@ -228,6 +241,9 @@ NatureEau <- prepa_NatureEau_fct(station_pdl)
 
 
 # sauvegarde et documentation ----------
+
+# remotes::install_gitlab('dreal-pdl/csd/utilitaires.ju', host = "gitlab-forge.din.developpement-durable.gouv.fr")
+
 usethis::use_data(
   Sage,
   BassinVersant,
diff --git a/data/BassinVersant.rda b/data/BassinVersant.rda
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diff --git a/data/Campagne_pa.rda b/data/Campagne_pa.rda
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diff --git a/data/bassin_versant_col_pa.rda b/data/bassin_versant_col_pa.rda
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diff --git a/data/choix_couleur_P90.rda b/data/choix_couleur_P90.rda
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diff --git a/data/choix_couleur_tendance.rda b/data/choix_couleur_tendance.rda
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diff --git a/data/couleur_seuil50.rda b/data/couleur_seuil50.rda
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diff --git a/data/repartition_tendanceP90.rda b/data/repartition_tendanceP90.rda
index ee7357feb7a9800240d0dd61cd1d1bc076188ff2..5b50aa075dc2d6c8db1adcc14b442406955a9179 100644
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diff --git a/data/station_pdl.rda b/data/station_pdl.rda
index c3570015ead41c385fe952cb758ca2c942c9d88c..6a2fae8de5cc7804346b6c83e57fe362b1a3f8d3 100644
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diff --git a/inst/app/www/News.md b/inst/app/www/News.md
index 7cff532015fcc0b2a4bc78c13fcdd4170ae3e98a..d8156d8855e4c23854c288386c47e85ec2d73014 100644
--- a/inst/app/www/News.md
+++ b/inst/app/www/News.md
@@ -2,9 +2,9 @@
 
 Annonce des évolutions de l'application et des données qu'elle valorise.
 
-### Nitrates v3_1
+### Nitrates v3.1
 
-Publiée le xx septembre 2024
+Publiée le xx avril 2025
 
 ##### Evolution des données
 
diff --git a/man/bassin_versant_col_pa.Rd b/man/bassin_versant_col_pa.Rd
index 264a3160fa8dc13ec2fb12ff329a20ff5a15a1c5..cf07153852d64df4fb0bc47c5fc28805e6e201f5 100644
--- a/man/bassin_versant_col_pa.Rd
+++ b/man/bassin_versant_col_pa.Rd
@@ -5,7 +5,7 @@
 \alias{bassin_versant_col_pa}
 \title{bassin_versant_col_pa}
 \format{
-A data frame with 4616 rows and 5 variables:
+A data frame with 5538 rows and 5 variables:
 \describe{
   \item{ code_bassin_versant }{  character }
   \item{ nom_bassin_versant }{  character }
diff --git a/man/bassin_versant_pa_telecharger.Rd b/man/bassin_versant_pa_telecharger.Rd
index ea9cc5d08cd21e25a727c13e3bfc06facfcc2c92..a35e386dba1942d092c8f653a2a606c31f0ac18b 100644
--- a/man/bassin_versant_pa_telecharger.Rd
+++ b/man/bassin_versant_pa_telecharger.Rd
@@ -5,7 +5,7 @@
 \alias{bassin_versant_pa_telecharger}
 \title{bassin_versant_pa_telecharger}
 \format{
-A data frame with 2308 rows and 7 variables:
+A data frame with 2769 rows and 7 variables:
 \describe{
   \item{ code_bassin_versant }{  character }
   \item{ nom_bassin_versant }{  character }
diff --git a/man/choix_couleur_P90.Rd b/man/choix_couleur_P90.Rd
index 92724f9b99958bda5809cd52a82fd867e116bf08..a423e676412397ffb7c89c77c94843809e45587a 100644
--- a/man/choix_couleur_P90.Rd
+++ b/man/choix_couleur_P90.Rd
@@ -5,7 +5,7 @@
 \alias{choix_couleur_P90}
 \title{choix_couleur_P90}
 \format{
-A data frame with 8 rows and 2 variables:
+A data frame with 7 rows and 2 variables:
 \describe{
   \item{ classe }{  character }
   \item{ couleur }{  character }
diff --git a/man/correspondance_sage_bassin_versant.Rd b/man/correspondance_sage_bassin_versant.Rd
index 5c193f80fcc943789f2f483622f8fabed91a0dc8..61144b3d4b4e471e920e1cff35c9af2dd76d44ef 100644
--- a/man/correspondance_sage_bassin_versant.Rd
+++ b/man/correspondance_sage_bassin_versant.Rd
@@ -5,11 +5,11 @@
 \alias{correspondance_sage_bassin_versant}
 \title{correspondance_sage_bassin_versant}
 \format{
-A data frame with 457 rows and 4 variables:
+A data frame with 463 rows and 4 variables:
 \describe{
   \item{ code_sage }{  character }
-  \item{ code_bassin_versant }{  character }
   \item{ nom_sage }{  character }
+  \item{ code_bassin_versant }{  character }
   \item{ nom_bassin_versant }{  character }
 }
 }
diff --git a/man/departement_col_pa.Rd b/man/departement_col_pa.Rd
index 6d3e4d03be8a61178d190c993432aaee83bf44ad..5feb31245ab3b0b05b90041b6e73c88cc7a4270c 100644
--- a/man/departement_col_pa.Rd
+++ b/man/departement_col_pa.Rd
@@ -5,7 +5,7 @@
 \alias{departement_col_pa}
 \title{departement_col_pa}
 \format{
-A data frame with 60 rows and 5 variables:
+A data frame with 72 rows and 5 variables:
 \describe{
   \item{ annee_declaration }{  character }
   \item{ Nom }{  character }
diff --git a/man/departement_pa_telecharger.Rd b/man/departement_pa_telecharger.Rd
index 6c9a71d5597f3864179252bf6d946cc75666a9c1..b097ef7ea008d1d8c0bf8dac5952c68872795494 100644
--- a/man/departement_pa_telecharger.Rd
+++ b/man/departement_pa_telecharger.Rd
@@ -5,7 +5,7 @@
 \alias{departement_pa_telecharger}
 \title{departement_pa_telecharger}
 \format{
-A data frame with 30 rows and 5 variables:
+A data frame with 36 rows and 5 variables:
 \describe{
   \item{ Nom }{  character }
   \item{ annee_declaration }{  character }
diff --git a/man/depassement_seuil_eso.Rd b/man/depassement_seuil_eso.Rd
index b619c005b6da8302773c9afd4884a2cfa4a02d56..6834cb9e34da20bf402b21a27443aed216af8574 100644
--- a/man/depassement_seuil_eso.Rd
+++ b/man/depassement_seuil_eso.Rd
@@ -5,7 +5,7 @@
 \alias{depassement_seuil_eso}
 \title{depassement_seuil_eso}
 \format{
-A data frame with 867 rows and 5 variables:
+A data frame with 921 rows and 5 variables:
 \describe{
   \item{ annee }{  factor }
   \item{ depassement }{  numeric }
diff --git a/man/depassement_seuil_esu.Rd b/man/depassement_seuil_esu.Rd
index fa01ef81c701125e499f5f79d5f469c4dca19fbd..375ae9b71e1f54294d646d3e095042f879275af2 100644
--- a/man/depassement_seuil_esu.Rd
+++ b/man/depassement_seuil_esu.Rd
@@ -5,7 +5,7 @@
 \alias{depassement_seuil_esu}
 \title{depassement_seuil_esu}
 \format{
-A data frame with 3444 rows and 5 variables:
+A data frame with 3694 rows and 5 variables:
 \describe{
   \item{ annee }{  factor }
   \item{ depassement }{  numeric }
diff --git a/man/masse_eau_bassin_versant_PDL.Rd b/man/masse_eau_bassin_versant_PDL.Rd
index 3736391e39ee954115157a91e954a9345193b960..b9f16edd24d827dee77ce57e9881e147db246e33 100644
--- a/man/masse_eau_bassin_versant_PDL.Rd
+++ b/man/masse_eau_bassin_versant_PDL.Rd
@@ -5,7 +5,7 @@
 \alias{masse_eau_bassin_versant_PDL}
 \title{masse_eau_bassin_versant_PDL}
 \format{
-A data frame with 457 rows and 3 variables:
+A data frame with 463 rows and 3 variables:
 \describe{
   \item{ code_bassin_versant }{  character }
   \item{ nom_bassin_versant }{  character }
diff --git a/man/n_sage_r52.Rd b/man/n_sage_r52.Rd
index dabe9416c86a8b1f6161bada0bd3e51d141083e2..a5bc6a8f3c4e84fb23b1abefca7097aa865347cf 100644
--- a/man/n_sage_r52.Rd
+++ b/man/n_sage_r52.Rd
@@ -5,10 +5,9 @@
 \alias{n_sage_r52}
 \title{n_sage_r52}
 \format{
-A data frame with 24 rows and 16 variables:
+A data frame with 24 rows and 15 variables:
 \describe{
   \item{ nid }{  character }
-  \item{ vid }{  character }
   \item{ nom_sage }{  character }
   \item{ type }{  character }
   \item{ code_sage }{  character }
diff --git a/man/nitrates_P90.Rd b/man/nitrates_P90.Rd
index 88ae4adb5f9c5fc65a9868ed7d3c6d45a30e60eb..676edd85d5d05a6d7f79809d2dbdf0c1c86ec375 100644
--- a/man/nitrates_P90.Rd
+++ b/man/nitrates_P90.Rd
@@ -5,12 +5,15 @@
 \alias{nitrates_P90}
 \title{nitrates_P90}
 \format{
-A data frame with 11972 rows and 9 variables:
+A data frame with 12848 rows and 12 variables:
 \describe{
   \item{ code_station }{  factor }
+  \item{ code_bss }{  factor }
+  \item{ code_sise_eaux }{  factor }
+  \item{ code_naiades }{  factor }
   \item{ libelle_station }{  factor }
-  \item{ source_station }{  factor }
   \item{ annee }{  numeric }
+  \item{ source_station }{  factor }
   \item{ id_usage }{  factor }
   \item{ P90 }{  numeric }
   \item{ code_nature_eau }{  factor }
diff --git a/man/nitrates_P90_telecharger.Rd b/man/nitrates_P90_telecharger.Rd
index 3d1135c91341feea10cdaa51221c2e04b9b6c9a1..da4e500a93e2a16e42582ac42fb45cfd572eebab 100644
--- a/man/nitrates_P90_telecharger.Rd
+++ b/man/nitrates_P90_telecharger.Rd
@@ -5,7 +5,7 @@
 \alias{nitrates_P90_telecharger}
 \title{nitrates_P90_telecharger}
 \format{
-A data frame with 7781 rows and 13 variables:
+A data frame with 12848 rows and 13 variables:
 \describe{
   \item{ annee }{  numeric }
   \item{ code_nature_eau }{  factor }
diff --git a/man/nitrates_maxP90_bassin_versant.Rd b/man/nitrates_maxP90_bassin_versant.Rd
index 8547d95eec9daa2419ac7351e990b53d27b243db..2a7e36a06a5cfb22ee85341b8609e81d2aa6e99d 100644
--- a/man/nitrates_maxP90_bassin_versant.Rd
+++ b/man/nitrates_maxP90_bassin_versant.Rd
@@ -5,7 +5,7 @@
 \alias{nitrates_maxP90_bassin_versant}
 \title{nitrates_maxP90_bassin_versant}
 \format{
-A data frame with 5797 rows and 5 variables:
+A data frame with 6231 rows and 5 variables:
 \describe{
   \item{ code_bassin_versant }{  character }
   \item{ annee }{  numeric }
diff --git a/man/nitrates_maxP90_bassin_versant_telecharger.Rd b/man/nitrates_maxP90_bassin_versant_telecharger.Rd
index 89ba636b5494e2852696efddd00c324d7027ca0d..4483db83f9d972ba0c0243be6159b8f9b59da301 100644
--- a/man/nitrates_maxP90_bassin_versant_telecharger.Rd
+++ b/man/nitrates_maxP90_bassin_versant_telecharger.Rd
@@ -6,7 +6,7 @@
 \alias{nitrates_maxP90_bassin_versant_telecharger}
 \title{nitrates_maxP90_bassin_versant_telecharger}
 \format{
-A data frame with 5937 rows and 8 variables:
+A data frame with 6231 rows and 8 variables:
 \describe{
   \item{ annee }{  numeric }
   \item{ code_nature_eau }{  factor }
diff --git a/man/nitrates_moyP90_bassin_versant.Rd b/man/nitrates_moyP90_bassin_versant.Rd
index 1dfd5330e3b9ed8269b01854fd7a515dc1a771fc..9d6d4abfafd850a6e460def7c3acd0879096e536 100644
--- a/man/nitrates_moyP90_bassin_versant.Rd
+++ b/man/nitrates_moyP90_bassin_versant.Rd
@@ -5,7 +5,7 @@
 \alias{nitrates_moyP90_bassin_versant}
 \title{nitrates_moyP90_bassin_versant}
 \format{
-A data frame with 5797 rows and 5 variables:
+A data frame with 6231 rows and 5 variables:
 \describe{
   \item{ code_bassin_versant }{  character }
   \item{ annee }{  numeric }
diff --git a/man/nitrates_moyP90_bassin_versant_telecharger.Rd b/man/nitrates_moyP90_bassin_versant_telecharger.Rd
index 79ed6fb357eac04ee296d8824424123f8715267a..25231eff94e3ee414eb27d7259d76b054e712a24 100644
--- a/man/nitrates_moyP90_bassin_versant_telecharger.Rd
+++ b/man/nitrates_moyP90_bassin_versant_telecharger.Rd
@@ -6,7 +6,7 @@
 \alias{nitrates_moyP90_bassin_versant_telecharger}
 \title{nitrates_moyP90_bassin_versant_telecharger}
 \format{
-A data frame with 5937 rows and 8 variables:
+A data frame with 6231 rows and 8 variables:
 \describe{
   \item{ annee }{  numeric }
   \item{ code_nature_eau }{  factor }
diff --git a/man/nitrates_telecharger.Rd b/man/nitrates_telecharger.Rd
index 0a521800315eb5d5d13c97db0a163484f43ab899..4b4baa0ba324c5990d9774ef5d614a3814b079da 100644
--- a/man/nitrates_telecharger.Rd
+++ b/man/nitrates_telecharger.Rd
@@ -5,7 +5,7 @@
 \alias{nitrates_telecharger}
 \title{nitrates_telecharger}
 \format{
-A data frame with 63239 rows and 15 variables:
+A data frame with 81525 rows and 15 variables:
 \describe{
   \item{ annee }{  numeric }
   \item{ mois }{  numeric }
@@ -16,9 +16,9 @@ A data frame with 63239 rows and 15 variables:
   \item{ nom_bassin_versant }{  character }
   \item{ code_station }{  character }
   \item{ libelle_station }{  character }
-  \item{ source_prelevement }{  character }
+  \item{ source_prelevement_analyse }{  character }
   \item{ resultat_analyse }{  numeric }
-  \item{ code_remarque }{  integer }
+  \item{ code_remarque }{  numeric }
   \item{ insee_dep }{  factor }
   \item{ code_commune }{  character }
   \item{ captage_prioritaire }{  logical }
diff --git a/man/nitrates_tendanceP90.Rd b/man/nitrates_tendanceP90.Rd
index d4cd7868654eb6607fdd3a373777afd8d7d2ec63..847283ef3b68b5abc4a1635892d542742a91e784 100644
--- a/man/nitrates_tendanceP90.Rd
+++ b/man/nitrates_tendanceP90.Rd
@@ -5,7 +5,7 @@
 \alias{nitrates_tendanceP90}
 \title{nitrates_tendanceP90}
 \format{
-A data frame with 8754 rows and 5 variables:
+A data frame with 10135 rows and 5 variables:
 \describe{
   \item{ code_station }{  factor }
   \item{ libelle_station }{  factor }
diff --git a/man/nitrates_tendanceP90_telecharger.Rd b/man/nitrates_tendanceP90_telecharger.Rd
index 07a4ad921b10d8dbd65b7ced1cac47b18bf47bd1..6d3e52bffee82f0ea8af2a0722228f462adc91c4 100644
--- a/man/nitrates_tendanceP90_telecharger.Rd
+++ b/man/nitrates_tendanceP90_telecharger.Rd
@@ -5,7 +5,7 @@
 \alias{nitrates_tendanceP90_telecharger}
 \title{nitrates_tendanceP90_telecharger}
 \format{
-A data frame with 6311 rows and 12 variables:
+A data frame with 10135 rows and 12 variables:
 \describe{
   \item{ periode }{  factor }
   \item{ code_nature_eau }{  factor }
diff --git a/man/periode_nitrates_tendanceP90.Rd b/man/periode_nitrates_tendanceP90.Rd
index aefe4b242ebe65cae6c4d7e099537d40eda4f3b1..61d7b381d3c159e63c0eb915c6435a96fc22a615 100644
--- a/man/periode_nitrates_tendanceP90.Rd
+++ b/man/periode_nitrates_tendanceP90.Rd
@@ -5,7 +5,7 @@
 \alias{periode_nitrates_tendanceP90}
 \title{periode_nitrates_tendanceP90}
 \format{
-A data frame with 16 rows and 2 variables:
+A data frame with 17 rows and 2 variables:
 \describe{
   \item{ annee }{  factor }
   \item{ periode }{  factor }
diff --git a/man/prepa_nitrates_fct.Rd b/man/prepa_nitrates_fct.Rd
deleted file mode 100644
index 21d21d5ccffe631d982c39ca410f84b11d952e50..0000000000000000000000000000000000000000
--- a/man/prepa_nitrates_fct.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/fct_preparation_data.R
-\name{prepa_nitrates_fct}
-\alias{prepa_nitrates_fct}
-\title{prepa_nitrates_fct}
-\usage{
-prepa_nitrates_fct(station_code_nature_eau)
-}
-\arguments{
-\item{station_code_nature_eau}{choix dans la table des stations selon leur code_nature_eau (ESU ou ESO)}
-}
-\value{
-Un dataframe.
-}
-\description{
-Une fonction pour produire les tables des mesures nitrates en fonction du code_nature_eau (ESU ou ESO) des stations
-}
diff --git a/man/repartition_P90.Rd b/man/repartition_P90.Rd
index 7e892cc6024cbe2059bd2724fdb26dc46d91ae07..d1c213698177f8fc7b80b3ba88f19fcd485fd470 100644
--- a/man/repartition_P90.Rd
+++ b/man/repartition_P90.Rd
@@ -5,7 +5,7 @@
 \alias{repartition_P90}
 \title{repartition_P90}
 \format{
-A data frame with 11750 rows and 8 variables:
+A data frame with 11505 rows and 8 variables:
 \describe{
   \item{ code_nature_eau }{  factor }
   \item{ annee }{  numeric }
diff --git a/man/repartition_maxBV.Rd b/man/repartition_maxBV.Rd
index 416d16791e6b036438c3911cf9c4d2bfc8206d96..5f7cb95d5b5b9881ee85dc234c3009595bfcd2fc 100644
--- a/man/repartition_maxBV.Rd
+++ b/man/repartition_maxBV.Rd
@@ -5,7 +5,7 @@
 \alias{repartition_maxBV}
 \title{repartition_maxBV}
 \format{
-A data frame with 2854 rows and 7 variables:
+A data frame with 2580 rows and 7 variables:
 \describe{
   \item{ code_nature_eau }{  factor }
   \item{ annee }{  numeric }
diff --git a/man/repartition_moyBV.Rd b/man/repartition_moyBV.Rd
index 66867170b46faff0f7bb99c2ec9cc0864ded808f..d99b285ac883ca3c4b64c9fde155ba0552cba478 100644
--- a/man/repartition_moyBV.Rd
+++ b/man/repartition_moyBV.Rd
@@ -5,7 +5,7 @@
 \alias{repartition_moyBV}
 \title{repartition_moyBV}
 \format{
-A data frame with 2733 rows and 7 variables:
+A data frame with 2431 rows and 7 variables:
 \describe{
   \item{ code_nature_eau }{  factor }
   \item{ annee }{  numeric }
diff --git a/man/repartition_tendanceP90.Rd b/man/repartition_tendanceP90.Rd
index 380ec564e54119e1128484f579abeacefaf6ded8..9bfd9fb9340386cd1d8ec7137d1eff3b8fde9447 100644
--- a/man/repartition_tendanceP90.Rd
+++ b/man/repartition_tendanceP90.Rd
@@ -5,7 +5,7 @@
 \alias{repartition_tendanceP90}
 \title{repartition_tendanceP90}
 \format{
-A data frame with 6412 rows and 8 variables:
+A data frame with 7254 rows and 8 variables:
 \describe{
   \item{ code_nature_eau }{  factor }
   \item{ periode }{  factor }
diff --git a/man/station_pdl.Rd b/man/station_pdl.Rd
index 6aeeaba2dff3b4f10b5c68220c189984649b3b01..011f551102fe196a05ecb71177b405c80a36a431 100644
--- a/man/station_pdl.Rd
+++ b/man/station_pdl.Rd
@@ -5,21 +5,23 @@
 \alias{station_pdl}
 \title{station_pdl}
 \format{
-A data frame with 3816 rows and 14 variables:
+A data frame with 3870 rows and 16 variables:
 \describe{
   \item{ code_station }{  character }
+  \item{ code_bss }{  character }
+  \item{ code_sise_eaux }{  character }
+  \item{ code_naiades }{  character }
   \item{ libelle_station }{  character }
+  \item{ code_nature_eau }{  character }
   \item{ date_creation }{  Date }
-  \item{ source }{  character }
+  \item{ source_station }{  character }
   \item{ code_masse_eau }{  character }
-  \item{ code_eu_masse_eau }{  character }
+  \item{ code_bassin_versant }{  character }
   \item{ code_commune }{  character }
   \item{ code_sage }{  character }
-  \item{ code_bassin_versant }{  character }
   \item{ captage_prioritaire }{  logical }
   \item{ insee_dep }{  factor }
   \item{ test }{  logical }
-  \item{ code_nature_eau }{  character }
   \item{ the_geom }{  sfc_POINT,sfc }
 }
 }