Data Preparation

00_Mesobromion_DataPreparation.R

@@ -100,42 +100,55 @@ env.all <- env
 ### 3. Import species data #### 
 # columns in species correspond to those in env
 # there is no PlotObservationID (yet)
-species0 <- read.table("_data/Mesobromion/GVRD_Mes2_veg1.csv", sep = ",", header=T)
-dim(species0) #6868 obs. of  907 variables:
-rownames(species0) <- env0$RELEVE_NR
+#species0 <- read.table("_data/Mesobromion/GVRD_Mes2_veg1.csv", sep = ",", header=T)
+species.proz <- read_csv("_data/Mesobromion/GVRD_Mes2_proz.csv", locale = locale(encoding = 'latin1'))
+species.proz$RELEVE_NR <- env0$RELEVE_NR
+#dim(species0) #6868 obs. of  907 variables:
+#rownames(species0) <- env0$RELEVE_NR
+dim(species.proz) #6868 obs. of  903 variables:
+
 
 ## select only plots already selected in env
 species <- env %>% 
   dplyr::select(RELEVE_NR) %>% 
-  left_join(species0 %>%
-              mutate(RELEVE_NR=env0$RELEVE_NR), 
+  left_join(species.proz %>%
+              #rownames_to_column("RELEVE_NR") %>% 
+              mutate(RELEVE_NR=as.numeric(RELEVE_NR)),
             by="RELEVE_NR") %>% 
-  column_to_rownames("RELEVE_NR") %>% 
+  filter_all(any_vars(!is.na(.))) %>% 
   ## delete species not appearing in any plot
-  dplyr::select(colnames(.)[which(colSums(.)!=0)])
+  dplyr::select(colnames(.)[which(colSums(., na.rm=T)!=0)])
 #dplyr::select(traits$species0)
+dim(species) # [1]  5810  878
 
-dim(species) # [1] 5810 881
 
 releve08trait <- species %>% 
-  rownames_to_column("RELEVE_NR") %>% 
-  reshape2::melt(.id="RELEVE_NR") %>% 
-  rename(species0=variable, pres=value) %>% 
-  as.tbl() %>% 
-  filter(pres>0) %>% 
-  arrange(RELEVE_NR)  %>% 
+  #transform percentage cover to relative.cover
+  mutate(sumVar = rowSums(.[-1])) %>% 
+  mutate_at(.vars=vars(-RELEVE_NR), 
+            .funs=~./sumVar) %>% 
+  dplyr::select(-sumVar) %>% 
+  # transform to long format and attach traits
+  #rownames_to_column("RELEVE_NR") %>% 
+  #reshape2::melt(.id="RELEVE_NR") %>% 
+  #rename(species0=variable, pres=value) %>% 
+  #as.tbl() %>% 
+  #arrange(RELEVE_NR)  %>% 
+  pivot_longer(names_to = "species0", values_to = "abund", -RELEVE_NR) %>% 
+  filter(abund>0) %>% 
   ## attach traits 
   left_join(traits %>% 
               dplyr::select(-species), by="species0") %>% 
   mutate_at(.vars = vars(LEB_F_Makrophanerophyt:Disp.unit.leng), 
-            .funs = list(~if_else(is.na(.),0,1) * pres)) %>%
+            .funs = list(~if_else(is.na(.),0,1) * abund)) %>%
   group_by(RELEVE_NR) %>%
   summarize_at(.vars= vars(LEB_F_Makrophanerophyt:Disp.unit.leng),
-               .funs = list(~mean(.))) %>%
+               .funs = list(~sum(.))) %>%
   dplyr::select(RELEVE_NR, order(colnames(.))) %>%
-  reshape2::melt(id.vars="RELEVE_NR", value.name="trait.coverage") %>% 
+  pivot_longer(values_to="trait.coverage", names_to="trait", -RELEVE_NR) %>% 
+  #reshape2::melt(id.vars="RELEVE_NR", value.name="trait.coverage") %>% 
   group_by(RELEVE_NR) %>% 
-  summarize(ntraits08=mean(trait.coverage>=0.8)) %>% 
+  summarize(ntraits08=mean(trait.coverage>=.8)) %>% 
   #select only those releves where we have a coverage of >0.8 for all traits
   filter(ntraits08==1) %>% 
   pull(RELEVE_NR)
@@ -143,11 +156,12 @@ releve08trait <- species %>%
 set.seed(1984)
 releve08trait.samp <- sample(releve08trait, round(length(releve08trait)/10), replace=F)
 species <- species %>% 
-  rownames_to_column("RELEVE_NR") %>% 
+ #  rownames_to_column("RELEVE_NR") %>% 
   filter(RELEVE_NR %in% releve08trait.samp) %>% 
   #column_to_rownames("RELEVE_NR") %>% 
   #as.tbl() %>% 
-  dplyr::select(RELEVE_NR, one_of(traits$species0))
+  dplyr::select(RELEVE_NR, one_of(traits$species0)) %>% 
+  dplyr::select(colnames(.)[which(colSums(.)!=0)])
 
 
 env <- env %>% 
@@ -228,9 +242,9 @@ traits <- traits %>%
 ### ordered factors
 
 
-dim(species) #558 783
-dim(traits) #783 53
-dim(env) #558 8
+dim(species) #558 783 -> 531 461
+dim(traits) #783 53 -> 460  53
+dim(env) #558 8 -> 531   8
 
 
 
@@ -278,22 +292,13 @@ env <- env %>%
 
 
 ##export for Valerio
-write_delim(species, path="_data/Mesobromion/species.out.10perc.txt", delim="\t")
-write_delim(traits, path="_data/Mesobromion/traits.out.10perc.txt", delim="\t")
-write_delim(env, path="_data/Mesobromion/env.10perc.txt", delim="\t")
-
-## version without missing species
-empty <- which(colSums(species[,-1])==0)
-traits_nozero <- traits[-empty,]
-species_nozero <- species[,-(empty+1)]
-
-write_delim(species_nozero , path="_data/Mesobromion/species.out.10perc_nozero.txt", delim="\t")
-write_delim(traits_nozero, path="_data/Mesobromion/traits.out.10perc_nozero.txt", delim="\t")
-
+write_delim(species, path="_data/Mesobromion/species.out.10perc.cov.txt", delim="\t")
+write_delim(traits, path="_data/Mesobromion/traits.out.10perc.cov.txt", delim="\t")
+write_delim(env, path="_data/Mesobromion/env.10perc.cov.txt", delim="\t")
 
 write_delim(species %>% 
               dplyr::select(RELEVE_NR), 
-            path="_derived/Mesobromion/ReleveList.txt", delim="\t")
+            path="_derived/Mesobromion/ReleveList.cov.txt", delim="\t")