Correction d'approximations se révélant fausses pour le mois de janvier

cloture.Rmd

@@ -414,7 +414,7 @@ rss %>%
 ```{r fig8}
 gam %>%
   count(mois_sortie) %>%
-  full_join(data.frame(mois_sortie = setdiff(1:mois, .$mois_sortie), n = 0)) %>%
+  full_join(data.frame(mois_sortie = setdiff(1:mois, .$mois_sortie), n = rep(0, length(setdiff(1:mois, .$mois_sortie))))) %>%
 
   ggplot +
   aes(x = mois_sortie,
@@ -573,14 +573,14 @@ Ovalide$RAV[-(1:2), c(1,4,6)] %>%
 ### <a href="#tab14" data-toggle="collapse" class="panel-heading">Valorisation des IVG, ATU, SE, actes et consultations</a> {.panel .panel-default}
 #### {.panel-body .collapse #tab14}
 ```{r tab14}
-data_frame(A = c(Ovalide$VATU[nrow(Ovalide$VATU), 3:5] %>% unlist,
-                 Ovalide$VSE[nrow(Ovalide$VSE), 3:5] %>% unlist,
-                 Ovalide$VCCAM[nrow(Ovalide$VCCAM), 2:4] %>% unlist,
-                 Ovalide$VNGAP[nrow(Ovalide$VNGAP), 3:5] %>% unlist),
-           B = c(OvalideP$VATU[nrow(OvalideP$VATU), 3:5] %>% unlist,
-                 OvalideP$VSE[nrow(OvalideP$VSE), 3:5] %>% unlist,
-                 OvalideP$VCCAM[nrow(OvalideP$VCCAM), 2:4] %>% unlist,
-                 OvalideP$VNGAP[nrow(OvalideP$VNGAP), 3:5] %>% unlist)) %>%
+data_frame(A = c(Ovalide$VATU[is.na(Ovalide$VATU$A), 3:5] %>% unlist,
+                 Ovalide$VSE[Ovalide$VSE$A == "", 3:5] %>% unlist,
+                 Ovalide$VCCAM[Ovalide$VCCAM$A == "", 2:4] %>% unlist,
+                 Ovalide$VNGAP[Ovalide$VNGAP$A == "", 3:5] %>% unlist),
+           B = c(OvalideP$VATU[is.na(OvalideP$VATU$A), 3:5] %>% unlist,
+                 OvalideP$VSE[OvalideP$VSE$A == "", 3:5] %>% unlist,
+                 OvalideP$VCCAM[OvalideP$VCCAM$A == "", 2:4] %>% unlist,
+                 OvalideP$VNGAP[OvalideP$VNGAP$A == "", 3:5] %>% unlist)) %>%
   mutate(C = A - B,
          D = (A - B) / B) %>%
 
@@ -659,23 +659,21 @@ _\* Prix Moyen du Cas Traité = Total valorisation / nombre de RSA valorisés_
 ### <a href="#tab18" data-toggle="collapse" class="panel-heading">Indice de performance de la durée moyenne de séjours (IP-DMS) de l'année en cours et de l'année précédente</a> {.panel .panel-default}
 #### {.panel-body .collapse #tab18}
 ```{r tab18}
-Ovalide$EDMS %<>%
-  filter(A != ".") %>%
-  mutate(B = B %>% str_replace(",",".")) %>%
-  mutate(B = B %>% as.numeric)
-
-OvalideP$EDMS %<>%
-  filter(A != ".") %>%
-  mutate(B = B %>% str_replace(",",".")) %>%
-  mutate(B = B %>% as.numeric)
-
-IP_current <- (Ovalide$EDMS[[1, 2]] + Ovalide$EDMS[[4, 2]]) / (Ovalide$EDMS[[2, 2]] + Ovalide$EDMS[[5, 2]])
-IP_previous <- (OvalideP$EDMS[[1, 2]] + OvalideP$EDMS[[4, 2]]) / (OvalideP$EDMS[[2, 2]] + OvalideP$EDMS[[5, 2]])
+if (nrow(Ovalide$EDMS) == 6)
+{
+  IP_current <- (Ovalide$EDMS[[1, 2]] + Ovalide$EDMS[[4, 2]]) / (Ovalide$EDMS[[2, 2]] + Ovalide$EDMS[[5, 2]])
+  IP_previous <- (OvalideP$EDMS[[1, 2]] + OvalideP$EDMS[[4, 2]]) / (OvalideP$EDMS[[2, 2]] + OvalideP$EDMS[[5, 2]])
+} else {
+  IP_current <- Ovalide$EDMS[[1, 2]] / Ovalide$EDMS[[2, 2]]
+  IP_previous <- OvalideP$EDMS[[1, 2]] / OvalideP$EDMS[[2, 2]]
+}
 
 data_frame(IP_current, IP_previous) %>%
   datatable(rownames = "Indice de performance",
             colnames = c(current, previous)) %>%
   formatCurrency(1:2, currency = "", digits = 3, dec.mark = ",")
+
+rm(IP_previous, IP_current)
 ```
 *Données issues des tableaux OVALIDE [1.D2.EDMS] de `r periode`*  
 _\* Nb de journées / Nb de journées standardisées sur la DMS théorique_