biodivExplorer_rocky.Rmd

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

```{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}


## add here the names of the file to analyse, with the correct path

## add the file name here, with eh correct path
## rocky or beach
baseDataDir =  "../data2"
baseEcosystem = "rocky"
fileName = "ARGENTINA_MARDELPLATA_dashboard_"

datafileName = file.path(baseDataDir, baseEcosystem, fileName)


siteDF = read_csv(file = file.path(baseDataDir, "DataAnalysisFiles", baseEcosystem, paste0(fileName, "siteDF.csv")))
Occurrence = read_csv(file = file.path(baseDataDir, "DataAnalysisFiles",baseEcosystem, paste0(fileName, "occurrence.csv")))

Occurrence$abundance = as.numeric(Occurrence$abundance)
Occurrence$Cover = as.numeric(Occurrence$Cover)


## reorder strata factor
Occurrence$strata = factor(Occurrence$strata, levels = c("LOWTIDE", "MIDTIDE", "HIGHTIDE"))

## remove spp without AphiaID
Occurrence = Occurrence[!is.na(Occurrence$AphiaID),]


```


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

Column
-----------------------------------------------------------------------

### 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) 

```


Column
-----------------------------------------------------------------------



### taxonomic rank distribution

```{r taxonrank}
## library(worms)
## get taxonomy file
taxaDictFileName = file.path(baseDataDir, "taxalist", 
                             baseEcosystem, "RS-taxonlistALL_nonduplicated_taxonomy.csv")
taxaDict = read_tsv(taxaDictFileName)


scNames = unique(Occurrence$AphiaID)
scNames = left_join(data.frame(AphiaID=scNames), taxaDict)

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

## summarise the results
taxranks = as.data.frame(table(scNames$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[,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
-------------------------------------------------------

### Number of species
```{r}
nspp = nrow(taxafreq)
valueBox(nspp)
```


### Number of Mollusc species
```{r}
nMolluscs = sum(scNames$Phylum=="Mollusca", na.rm=T)
valueBox(nMolluscs)
```


### Number of Algal species

```{r}
nPlants = sum(scNames$Kingdom=="Plantae", na.rm=T)
valueBox(nPlants)

```



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

### Number of Threatened species


### Number of Invasive Species


### Number of XXXX Species