diff --git a/00_Mesobromion_DataPreparation.R b/00_Mesobromion_DataPreparation.R
index 682828e711ce5f601744723a8798bc143da14763..391b9a4d33ff2847851acd95f6d1fd5fc9783c67 100644
--- a/00_Mesobromion_DataPreparation.R
+++ b/00_Mesobromion_DataPreparation.R
@@ -12,9 +12,10 @@ source("99_HIDDEN_functions.R")
##### PART 1 ####
#### 1. traits data ####
-traits0 <- read_delim("_data/Mesobromion/traits3_cov.txt", delim =";", ## manually corrected vowels with umlaut
+traits0 <- read_delim("_data/Mesobromion/traits4.csv", delim =",", ## manually corrected vowels with umlaut
col_names = T, locale = locale(encoding = 'UTF-8')) %>%
column_to_rownames("X1") %>%
+ dplyr::select(-starts_with("X")) %>%
dplyr::select(colnames(.)[which(colSums(., na.rm=T)!=0)]) %>%
dplyr::select(!starts_with("BLM")) %>%
dplyr::select(!starts_with("ZWT")) %>%
@@ -30,15 +31,22 @@ traits0 <- read_delim("_data/Mesobromion/traits3_cov.txt", delim =";", ## manua
mutate(species=gsub(pattern="_", replacement = " ", x = species)) %>%
mutate(species=gsub(pattern=" agg | x | spec$| agg$| s | Sec | ", replacement=" ", x=species)) %>%
mutate(species=gsub(pattern=" $", replacement = "", x = species)) %>%
- mutate(species=ifelse(is.na(word(species, 1, 2)), species, word(species, 1, 2)))
-dim(traits0) #907 obs. of 75 variables:
-
-
-
-#keep only traits with >=88 completeness
+ mutate(species=ifelse(is.na(word(species, 1, 2)), species, word(species, 1, 2))) %>%
+ ungroup()
+dim(traits0) #902 obs. of 67 variables:
+
+## remove species with NAs
+species.to.remove <- traits0 %>%
+ filter(all_of(is.na({.} %>% dplyr::select(-species, -species0)))) %>%
+ pull(species0)
traits0 <- traits0 %>%
- dplyr::select_if(~mean(!is.na(.)) >= 0.88)
-dim(traits0)# 902 x 67
+ filter(!species0 %in% species.to.remove)
+
+
+# #keep only traits with >=88 completeness
+# traits0 <- traits0 %>%
+# dplyr::select_if(~mean(!is.na(.)) >= 0.88)
+# dim(traits0)# 902 x 67
@@ -88,8 +96,8 @@ all.traits <- traits0 %>%
by="species")
traits <- all.traits %>%
filter(!is.na(LeafArea))
-dim(all.traits) #[1] 902 82
-dim(traits) #[1] 801 82
+dim(all.traits) #[1] 898 82
+dim(traits) #[1] 799 82
@@ -114,22 +122,23 @@ env.all <- env
### 3. Import species data ####
# columns in species correspond to those in env
# there is no PlotObservationID (yet)
-species0 <- read_csv("_data/Mesobromion/GVRD_Mes2_proz2.csv", locale = locale(encoding = 'latin1'))
-dim(species0) #6868 obs. of 903 variables:
-rownames(species0) <- env0$RELEVE_NR
+species0 <- read_csv("_data/Mesobromion/GVRD_Mes2_proz2.csv", locale = locale(encoding = 'latin1')) %>%
+ dplyr::select(-species.to.remove) %>%
+ mutate(RELEVE_NR=env0$RELEVE_NR)
+dim(species0) #6868 obs. of 899 variables:
+
## select only plots already selected in env
species <- env %>%
dplyr::select(RELEVE_NR) %>%
- left_join(species0 %>%
- mutate(RELEVE_NR=env0$RELEVE_NR),
+ left_join(species0,
by="RELEVE_NR") %>%
column_to_rownames("RELEVE_NR") %>%
## delete species not appearing in any plot
dplyr::select(colnames(.)[which(colSums(.)!=0)])
#dplyr::select(traits$species0)
-dim(species) # [1] 5810 877
+dim(species) # [1] 5810 873
releve08trait <- species %>%
rownames_to_column("RELEVE_NR") %>%
@@ -310,7 +319,6 @@ species.cov <- species.cov %>%
dplyr::select(-sumVar)
dim(species.cov) #[1] 581 510
-
## export
write_delim(species.pa, path="_data/Mesobromion/species.v2.10perc.pa.txt", delim="\t")
write_delim(species.cov, path="_data/Mesobromion/species.v2.10perc.cov.txt", delim="\t")
@@ -323,18 +331,16 @@ write_delim(species %>%
path="_data/Mesobromion/ReleveList.txt", delim="\t")
-
##check for species without trait info
traits %>%
filter_at(.vars=vars(-"species0"),
all_vars(is.na(.))) %>%
nrow() ## [1] no species with no trait info ##
-## simply because those 7 species without TRY data were excluded already
traits %>%
filter_at(.vars=vars(-"species0"),
any_vars(is.na(.))) %>%
- nrow() ## [1] 109 # species with at least 1 NA in traits
+ nrow() ## [1] 36 # species with at least 1 NA in traits
#### CORRELATION BETWEEN FUZZY WEIGHTED AND BEALS MATRICES
@@ -350,7 +356,7 @@ traits %>%
source("01b_MesobromionCluster.R")
#### 1. Traits individually significant for COVER data#### na.exclude=T ########
traits <- read_delim("_data/Mesobromion/traits.v2.10perc.txt", delim="\t")
-myfilelist <- list.files(path="_derived/Mesobromion/Cover", pattern="HIDDENcov_[0-9]+_.RData", full.names = T)
+myfilelist <- list.files(path="_derived/Mesobromion/Cover", pattern="HIDDENcov-nona2_[0-9]+_.RData", full.names = T)
dataFiles = purrr::map(myfilelist, function(x){get(load(x))})
corXY = bind_rows(dataFiles) %>%
as_tibble()
@@ -373,7 +379,7 @@ traits.sign <- traits %>%
dplyr::select(species0, any_of(traits.sign.alone))
#write_delim(traits.sign, path="_data/Mesobromion/traits.out.10perc.cov.sign.txt", delim="\t")
-### COV - NONAs
+### COV - NONAs - all species with at least 1 NAs in traits excluded BEFORE The analysis
" Trait.comb Coef.obs Coef.perm q025 q975 greater.than.perm n sign_plus sign_minus ntraits trait.name
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <lgl> <lgl> <int> <fct>
1 36 0.316 0.154 0.290 0.370 0.997 999 TRUE FALSE 1 PlantHeight
@@ -385,6 +391,18 @@ traits.sign <- traits %>%
7 32 0.251 0.127 0.226 0.303 0.968 999 TRUE FALSE 1 SLA
8 35 0.241 0.128 0.217 0.289 0.970 999 TRUE FALSE 1 LeafP "
+### COV - NONA2 - modified Matrix.x to deal with NAs INSIDE the analysis
+" Trait.comb Coef.obs Coef.perm q025 q975 greater.than.perm n sign_plus sign_minus ntraits trait.name
+ <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <lgl> <lgl> <int> <fct>
+ 1 36 0.304 0.134 0.281 0.356 0.990 199 TRUE FALSE 1 PlantHeight
+ 2 50 0.290 0.126 0.261 0.339 0.985 199 TRUE FALSE 1 BL_ANAT
+ 3 2 0.252 0.0621 0.214 0.303 0.985 199 TRUE FALSE 1 LEB_F_Nanophaneroph…
+ 4 20 0.251 0.145 0.206 0.320 0.975 199 TRUE FALSE 1 V_VER_Fragmentation
+ 5 30 0.250 0.137 0.228 0.298 0.970 199 TRUE FALSE 1 BL_DAU
+ 6 32 0.247 0.0932 0.225 0.292 0.965 199 TRUE FALSE 1 SLA
+ 7 49 0.236 0.0784 0.212 0.284 0.975 199 TRUE FALSE 1 STRAT_T
+ 8 35 0.241 0.127 0.218 0.288 0.940 199 FALSE FALSE 1 LeafP "
+
### COV - without deleting NAs
"# A tibble: 53 x 11
Trait.comb Coef.obs Coef.perm q025 q975 greater.than.perm n sign_plus sign_minus ntraits trait.name