Version v1.1 corrected

code/04_buildHeader.Rmd

@@ -151,7 +151,8 @@ Pfe-f-08 - 1707849
 Pfe-o-05- 1707854  
 ```{r, eval=T}
 header0 <- header0 %>% 
-  filter(!PlotObservationID %in% c(1707776, 1707779:1707782, 1707849, 1707854))
+  filter(!PlotObservationID %in% c(1707776, 1707779:1707782, 1707849, 1707854)) %>% 
+  filter(Dataset != "$Coastal_Borja")
 ```
 
 
@@ -394,7 +395,7 @@ header <- header %>%
             by="PlotObservationID") %>% 
   mutate(CONTINENT=factor(continent, 
                             levels=c("Africa", "Antarctica", "Australia", "Eurasia", "North America", "South America"),
-                            labels=c("AF", "AN", "AU", "EU", "NA", "SA"))) %>% 
+                            labels=c("AF", "AN", "AU", "EU", "N-A", "S-A"))) %>% 
   
   dplyr::select(-continent)
 ```

code/05_ExtractEnvironment.Rmd

@@ -295,7 +295,8 @@ elevation.out <- read_csv("../_derived/elevatr/elevation.out.csv")
 soilclim <- header %>% 
   dplyr::select(PlotObservationID) %>% 
   left_join(elevation.out %>% 
-              dplyr::select(PlotObservationID, Elevation_median, Elevation_q2.5, Elevation_q97.5, Elevation_DEM.res=DEM.res), 
+              dplyr::select(PlotObservationID, Elevation_median, Elevation_q2.5, Elevation_q97.5, Elevation_DEM.res=DEM.res) %>% 
+              filter(!is.na(Elevation_median)), 
             by="PlotObservationID") %>% 
   left_join(header.shp@data %>% 
               dplyr::select(PlotObservationID) %>% 
@@ -305,6 +306,8 @@ soilclim <- header %>%
               bind_cols(isric.sd.out) %>% 
               distinct(), 
             by="PlotObservationID")
+dim(soilclim)
+nrow(soilclim)==nrow(header)
 ```
 
 ```{r, echo=F}

code/07_buildCWMs.Rmd

@@ -15,7 +15,7 @@ output: html_document
 **Revised:**  
 **version:** 1.0
   
-This reports documents 1) the construction of Community Weighted Means (CWMs) and Variance (CWVs); and 2) the classification of plots into forest\\non-forest based on species growth forms. It complements species composition data from sPlot 3.0 and gap-filled plant functional traits from TRY 5.0, as received by [Jens Kattge](jkattge@bgc-jena.mpg.de) on Jan 21, 2020. 
+This report documents 1) the construction of Community Weighted Means (CWMs) and Variance (CWVs); and 2) the classification of plots into forest\\non-forest based on species growth forms. It complements species composition data from sPlot 3.0 and gap-filled plant functional traits from TRY 5.0, as received by [Jens Kattge](jkattge@bgc-jena.mpg.de) on Jan 21, 2020. 
 
 *Changes in version 1.1* - Standardized Growth form names in sPlot_traits.  
 
@@ -202,7 +202,7 @@ This results in the exclusion of `r length(unique(c(toexclude, toexclude2, toexc
 
 
 ## 1.4 Calculate species and genus level trait means and sd
-```{r}
+```{r, cache=T,  cache.lazy=F, warning=F}
 ## Calculate species level trait means and sd. 
 try.species.means <- try.individuals %>% 
   group_by(Name_short) %>% 
@@ -335,7 +335,10 @@ CWM0 <- DT2.comb %>%
               dplyr::rename(Species=Taxon_name) %>% 
               dplyr::select(Species, Rank_correct, ends_with("_mean")), 
             by=c("Species", "Rank_correct"))
+```
 
+
+```{r, cache=T,  cache.lazy=F, warning=F}
 # Calculate CWM for each trait in each plot
 CWM1 <- CWM0 %>%
   group_by(PlotObservationID) %>%
@@ -343,7 +346,10 @@ CWM1 <- CWM0 %>%
                .funs = list(~weighted.mean(., Relative_cover, na.rm=T))) %>%
   dplyr::select(PlotObservationID, order(colnames(.))) %>%
   gather(key=variable, value=CWM, -PlotObservationID)
+```
+
 
+```{r, cache=T,  cache.lazy=F, warning=F}
 # Calculate coverage for each trait in each plot
 CWM2 <- CWM0 %>%
   mutate_at(.funs = list(~if_else(is.na(.),0,1) * Relative_cover), 
@@ -353,7 +359,10 @@ CWM2 <- CWM0 %>%
                .funs = list(~sum(., na.rm=T))) %>%
   dplyr::select(PlotObservationID, order(colnames(.))) %>%
   gather(key=variable, value=trait.coverage, -PlotObservationID)
+```
 
+
+```{r, cache=T,  cache.lazy=F, warning=F}
 # Calculate CWV
 # Ancillary function
 variance2.fun <- function(trait, abu){
@@ -377,7 +386,10 @@ CWM3 <- CWM0 %>%
                .funs = list(~variance2.fun(., Relative_cover))) %>%
   dplyr::select(PlotObservationID, order(colnames(.))) %>%
   gather(key=variable, value=CWV, -PlotObservationID)
+```
 
+
+```{r, cache=T,  cache.lazy=F, warning=F}
 ## Calculate proportion of species having traits
 CWM4 <- CWM0 %>%
   group_by(PlotObservationID) %>%
@@ -479,12 +491,6 @@ knitr::kable(CWM.summary,
                   full_width = F, position = "center")
 ```
 
-### 2.2 Export CWM and species mean trait values
-```{r}
-save(try.combined.means, CWM, file="../_output/Traits_CWMs_sPlot3.RData")
-```
-
-
 # 3 Classify plots in `is.forest` or `is.non.forest` based on species traits
 sPlot has two independent systems for classifying plots to vegetation types. The first relies on the expert opinion of data contributors and classifies plots into broad habitat types. These broad habitat types are coded using 5, non-mutually exclusive dummy variables:  
 1) Forest