Minor fixed to 02

02_Mesobromion_ExamineOutput.R

@@ -72,10 +72,12 @@ is.binary <- function(x){(all(na.omit(x) %in% 0:1))}
 
 ####1.1 Table S3 - Trait recap #####
 trait.recap <- traits %>% 
+  mutate_at(.vars=vars(Leaf_Scleroph, LifeSpan, Rosette), 
+            .funs=~as.ordered(.)) %>% 
   dplyr::select(-ends_with("1")) %>%  ## exclude two traits that shouldn't be there V_VER_present1 & V_VER_absent1
   mutate_if(.predicate = ~is.numeric(.), .funs=~round(.,3)) %>% 
   summarize_all(.funs = list(xxxType.of.variable=~ifelse(is.binary(.), "binary", 
-                                ifelse(is.ordered(.), "ordered", 
+                                ifelse(is.ordered(.), "ordinal", 
                                        ifelse(is.numeric(.), "quantitative", 
                                               ifelse(is.factor(.), "nominal", NA)))),
                              xxxLevels=~(
@@ -569,9 +571,7 @@ species.cov <- read_delim("_data/Mesobromion/species.v2.10perc.cov.txt", delim="
 
 #traits <- traits.backup
 categorical.traits <- colnames(traits)[which(sapply(traits, "is.factor"))]
-traits$LeafPersistence <- factor(traits$LeafPersistence, 
-                                 levels=c("immergrün","sommergrün","überwinternd_grün", "vorsommergrün"),
-                                 labels=c("eg", "sg", "wg", "se"))
+
 #traits$Pollination <- factor(traits$Pollination, 
 #                             levels=c("NEKTAR_HONIG_INSEKTEN","POLLEN","WIND" ), 
 #                             labels=c("insects", "pollen", "wind"))
@@ -764,15 +764,14 @@ ggsave("_pics/FigSXXXb_PCA_Fuzzy_2-3_wSpecies.png", width=8, height=8, dpi=300,
 
 #### 4.1 PCA of Y (Bealls) matrix + CWM ####
 W.beals <- as.data.frame(beals(species.cov %>% 
-                                 column_to_rownames("RELEVE_NR")  %>% 
-                                 mutate_all(~(.>0)*1),  #transform to p\a
-                         include=T, type=2))
+                                 column_to_rownames("RELEVE_NR"),  
+                               include=T, type=2))
 write.table(W.beals, sep="\t", file="_derived/Mesobromion/MatrixY_Beals.csv")
 pca.out <- rda(W.beals)
 varexpl <- round((pca.out$CA$eig)/sum(pca.out$CA$eig)*100,1)
 cwms.envfit <- envfit(pca.out, CWM.wide, na.rm = T, choices = 1:5)
 env.envfit <- envfit(pca.out, env %>% 
-                       dplyr::select(Temp, Prec, pH=PHIPHOX, C.org=ORCDRC), choices=1:5)
+                       dplyr::select(Temp, Prec, pH=PHIPHOX, C.org=ORCDRC), choices=1:4, na.rm = T)
 
 ### Transform to correlations and sink envfits
 ### see https://www.davidzeleny.net/anadat-r/doku.php/en:suppl_vars_examples for procedure
@@ -804,9 +803,9 @@ myvectors <- as.data.frame(env.cor) %>%
   bind_rows(as.data.frame(cwms.cor) %>% 
               rownames_to_column("mylab") %>%
               mutate(category="Trait")) %>% 
-  bind_rows(as.data.frame(fuzz.cor) %>% 
-              rownames_to_column("mylab") %>%
-              mutate(category="Fuzzy-Weighted")) %>% 
+  #bind_rows(as.data.frame(fuzz.cor) %>% 
+  #            rownames_to_column("mylab") %>%
+  #            mutate(category="Fuzzy-Weighted")) %>% 
   mutate(fontface0=ifelse(mylab %in% best.4traits, "bold", "italic")) %>% 
   mutate(category=as.factor(category)) %>% 
   mutate(mycol=ifelse(category=="Trait", oilgreen, orange)) %>%