biodivExplorer_BRA_GRAMUTE.Rmd

---
title: "P2P Rockyshore Biodiversity Explorer"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(reshape2)
library(leaflet)
library(ggplot2)
library(vegan)
library(plotly)
library(lubridate)
library(dplyr)

library(RColorBrewer)
palette(brewer.pal(8, "Set2"))


```

```{r dataread}

siteDF = read_csv(file = "../data/rocky/BRA/ACMazzuco/GRAMUT_siteDF.csv")
Occurrence = read_csv(file = "../data/rocky/BRA/ACMazzuco/GRAMUT_occurrence.csv")

## reorder strata factor
Occurrence$strata = factor(Occurrence$strata, levels = c("lowtide", "midtide", "hightide"))

```


General Information
======================================================================

Row
-----------------------------------------------------------------------

### Locality: `r siteDF$locality[1]` 

```{r map}
## get lat lon
siteCoords = siteDF %>% dplyr::group_by(locality, site, strata) %>% 
  dplyr::summarise(lng = mean(decimalLongitude, na.rm=T), 
            lat = mean(decimalLatitude, na.rm=T))
## get total abund and cover
siteAbund = Occurrence %>% dplyr::filter(!is.na(AphiaID))  %>% 
  dplyr::group_by(locality, site, strata) %>% 
  dplyr::summarise(sumAbund = sum(abundance, na.rm=T), 
            sumCover = sum(Cover, na.rm=T), 
            richness = n())

## add abund and cover to coords
siteCoords = full_join(siteCoords, siteAbund)

## create a color palette
pal <- colorFactor(brewer.pal(3, "Set2"), domain = c("lowtide", "midtide", "hightide"))

## make leaflet map
leaflet(siteCoords) %>% addTiles() %>% 
  addCircleMarkers(label = ~paste0(site, "-", strata, " / N Taxa: ", richness), 
                   radius = ~100 * richness/sum(richness) + 4,
                   color = ~pal(strata), 
                   fillOpacity = 0.8,
                   stroke = F,
                   clusterOptions = markerClusterOptions()) %>% 
  addMiniMap(toggleDisplay = T)

```


### Number of taxa per site and stratum

```{r taxasite}
taxaquadrat = Occurrence %>% filter(scientificName!="Bare Rock", scientificName!="Without Substrate") %>% 
  group_by(site, strata, replicateID) %>% 
  summarise(taxa.n = n())
taxaquadrat$strata = factor(taxaquadrat$strata, levels = c("lowtide", "midtide", "hightide"))

pp = ggplot(taxaquadrat, aes(strata, taxa.n, fill=strata))
pp = pp + geom_boxplot() + ylab("Number of taxa per quadrat") + 
  facet_grid(~site) + 
  theme_bw(base_size = 9) + theme(legend.position = "none")


ggplotly(pp) %>% plotly::config(displayModeBar = F) 


```

### Abundance of taxa per site and stratum

```{r taxaabund}

pp = ggplot(Occurrence, aes(strata, abundance, group=strata, fill=strata))
pp = pp + geom_boxplot() + ylab("Abundance of all taxa per quadrat") + 
  facet_grid(~site) + 
  theme_bw(base_size = 9) + theme(legend.position = "none")

ggplotly(pp) %>% plotly::config(displayModeBar = F) 

```

### Average live cover per strata

```{r taxacover}
taxacover = Occurrence %>% dplyr::filter(!is.na(AphiaID)) %>% 
  dplyr::group_by(site, strata, replicateID) %>% 
  dplyr::summarise(sumcover = sum(Cover, na.rm=T))

pp = ggplot(taxacover, aes(strata, sumcover, fill=strata))
pp = pp + geom_boxplot() + ylab("% Cover of all taxa per quadrat") + 
  facet_grid(~site) + 
  theme_bw(base_size = 9) + theme(legend.position = "none")

ggplotly(pp) %>% plotly::config(displayModeBar = F) 

```


Row
-----------------------------------------------------------------------



### taxonomic rank distribution

```{r taxonrank}
library(worms)
scNames = unique(Occurrence$AphiaID)
scNames = scNames[!is.na(scNames)]

## get taxon rank from WoRMS
scNames.rank = wormsbyid(x = scNames, verbose = F)

## summarise the results
taxranks = as.data.frame(table(scNames.rank$rank))

## make a donut
p = taxranks %>% plot_ly(labels = ~Var1, values=~Freq) %>% 
  add_pie(hole=0.6) %>% 
  layout(title = ~paste0("Total number of Taxa: ", length(scNames))) 

plotly::config(p,displayModeBar = F) 



```


### Species frequency

```{r}
taxafreq = Occurrence %>% dplyr::filter(!is.na(AphiaID)) %>% 
  dplyr::group_by(site, strata, ScientificName_accepted) %>% 
  dplyr::summarise(sppfreq = n()) %>% 
  dplyr::arrange(sppfreq) %>% 
  dplyr::mutate(sppacum = cumsum(sppfreq))

pp = ggplot(taxafreq, aes(ScientificName_accepted, sppfreq, fill=strata))
pp = pp + geom_bar(stat="identity") + coord_flip() + facet_grid(~site) + 
  theme_bw(base_size = 9) + xlab("") + ylab("number of quadrats present")

ggplotly(pp) %>% plotly::config(displayModeBar = F) 

```


### Taxa list: number of quadrats where found 

```{r taxalist}
spRanks = scNames.rank[,c("AphiaID", "rank")]

## add taxon rank to occurrence table
Occurrence = full_join(Occurrence, spRanks)

taxaTable = Occurrence %>% 
  dplyr::group_by(AphiaID) %>% dplyr::filter(!is.na(AphiaID)) %>% 
  dplyr::summarise(Taxon = unique(ScientificName_accepted), 
            rank = unique(rank), 
            lowtide = sum(strata=="lowtide", na.rm=T), 
            midtide = sum(strata=="midtide", na.rm=T), 
            hightide = sum(strata=="hightide", na.rm=T))

knitr::kable(taxaTable)

```


Indicators
=======================================================

Row
-------------------------------------------------------

### %Cover Brachidontes: High tide
```{r}
brachiCover = Occurrence %>% dplyr::filter(AphiaID==138215) %>% 
  dplyr::group_by(strata) %>% 
  dplyr::summarise(cover = format(mean(Cover, na.rm=T), digits = 3))

gauge(brachiCover$cover[brachiCover$strata=="hightide"], min=0, max=100, symbol="%")

```


### %Cover Brachidontes: Mid tide
```{r}
gauge(brachiCover$cover[brachiCover$strata=="midtide"], min=0, max=100, symbol="%")

```


### %Cover Brachidontes: Low tide

```{r}
gauge(brachiCover$cover[brachiCover$strata=="lowtide"], min=0, max=100, symbol="%")

```



Row
-------------------------------------------------------

### Cluster: abundance

```{r}
library(vegan)

## cast the div data
ecoMat = dcast(eventID~AphiaID, data=subset(Occurrence, !is.na(abundance)), value.var = "abundance", sum)


ecoMat.dist = vegdist(ecoMat[,-1], method = "bray")
plot(hclust(ecoMat.dist), hang=-1, labels = paste0(substr(ecoMat$eventID,9,17), substr(ecoMat$eventID, 27,32)), cex=0.4, ylab="B-C Distance", main="", xlab="", sub="")
```


### Cluster: cover


```{r}

## cast the div data
ecoMat = dcast(eventID~AphiaID, data=subset(Occurrence, !is.na(Cover)), value.var = "Cover", sum)

ecoMat.dist = vegdist(ecoMat[,-1], method = "bray")
plot(hclust(ecoMat.dist), hang=-1, labels = paste0(substr(ecoMat$eventID,9,17), substr(ecoMat$eventID, 27,32)), cex=0.4, ylab="B-C Distance", main="", xlab="", sub = "")
```