biodivExplorer_rocky_v2.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(xts)
library(dygraphs)
library(plotly)
library(lubridate)
library(dplyr)

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


```


```{r}
## functions

## remove all spaces from string
NoSpaces = function(x){
  return(gsub(" ", "", x))
}
```


```{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"
SSTSiteName = "Arraial do Cabo"

fileName = "BRAZIL_ARRAIALDOCABO_FORTALEZA_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]` {data-width=250}

```{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)

```


### SST 

```{r SST}
## plot SST from stored values

## get sampling event dates
samplingDates = unique(siteDF$eventDate)


## read SST values
SSTDir = "sst"
SST = read_csv(file.path(baseDataDir, SSTDir, paste0(NoSpaces(SSTSiteName), "_SST.csv")))
SST.clim = read_csv(file.path(baseDataDir, SSTDir, paste0(NoSpaces(SSTSiteName), "_Climatology.csv")))

SST.xts = as.xts(SST$SST, SST$time)
dygraph(SST.xts, 
        ylab = "Sea Surface Temperature °C") %>% 
  dySeries("V1", label ="SST °C", color = "steelblue") %>%
  dyHighlight(highlightCircleSize = 5, 
              highlightSeriesBackgroundAlpha = 0.2,
              hideOnMouseOut = FALSE) %>% 
  dyOptions(fillGraph = FALSE, fillAlpha = 0.4) %>% 
  dyEvent(samplingDates,  color="red") %>% 
  dyRangeSelector(dateWindow = c(max(SST$time) - years(5), max(SST$time)))


```



### SST Last year with smoothed Climatology {data-width=250}

```{r}
## subset SST for last year
SST.lastyear = SST %>% filter(year(time)==max(year(time)))

## make the plot
pp = ggplot(SST.clim, aes(yDay, SST.mean))
pp = pp + geom_line() + geom_smooth(span=0.25, se=FALSE, colour="steelblue") +  
  geom_ribbon(aes(ymin=SST.q25, ymax=SST.q75), fill="steelblue", alpha=0.5) +
  geom_line(data=SST.lastyear, aes(yday(time), SST), colour="red") + 
  ylab("Sea Surface Temperature °C") + xlab("Day of the Year") + 
  theme_bw(base_size = 12) 
ggplotly(pp) %>% plotly::config(displayModeBar = F) 

```



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



### taxonomic rank distribution

```{r taxonrank}

## summarise the results
taxranks = as.data.frame(table(Occurrence$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(unique(Occurrence$AphiaID)))) 

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}
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
=======================================================

```{r taxonomy}
## get taxonomy for AphiaID
## read taxonomy file
taxonomyFileName = file.path(baseDataDir, "taxalist", baseEcosystem, "RS-taxonlistALL_nonduplicated_taxonomy.csv")
taxonomy = read_tsv(taxonomyFileName)

## add taxonomy to Occurrence DF
Occurrence.taxonomy = left_join(Occurrence[,c("AphiaID", "site")], taxonomy, by="AphiaID")

```



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

### Number of species
```{r}
nspp = length(unique(Occurrence.taxonomy$AphiaID))
gauge(nspp, min=0, max=50)
```


### Number of Mollusc species
```{r}
nMolluscs = length(unique(Occurrence.taxonomy$AphiaID[Occurrence.taxonomy$Phylum=="Mollusca"]))
gauge(nMolluscs, min=0, max=20)
```


### Number of Algae species

```{r}
nPlants = length(unique(Occurrence.taxonomy$AphiaID[Occurrence.taxonomy$Kingdom=="Plantae"]))
gauge(nPlants, min=0, max=20)

```

### Number of Echinoderm species

```{r}
nEchino = length(unique(Occurrence.taxonomy$AphiaID[Occurrence.taxonomy$Phylum=="Echinodermata"]))
gauge(nEchino, min=0, max=20)

```

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


### 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
-------------------------------------------------------

### Distribution of Classes

```{r taxaclassdist}

tt = table(Occurrence.taxonomy$Class, Occurrence.taxonomy$site)

taxaclass = as.data.frame(prop.table(tt,2))
names(taxaclass) = c("Class", "Site", "Freq")

pp = ggplot(taxaclass, aes(Class, Freq, fill=Site))
pp = pp + geom_bar(stat="identity") + facet_grid(~Site) + coord_flip() + 
  xlab("") + ylab("Proportion of individuals") + ylim(0,1) + 
  theme_bw(base_size = 12) + theme(legend.position = "none")

ggplotly(pp)
```


### % of Bare Rock Cover

```{r barerock}
barerock = Occurrence %>% 
  filter(scientificName=="Bare rock") %>% 
  group_by(site, strata) %>% 
  summarise(brock = mean(Cover), rest = 100-brock)

pp = ggplot(barerock, aes(strata, brock, fill=site))
pp = pp + geom_bar(stat = "identity") + coord_flip() + 
  xlab("") + ylab("% Bare Rock") + ylim(0,100) +
  theme_bw(base_size = 12) + theme(legend.position = 'none') + 
  facet_grid(~site)

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

```


### % Live Cover

```{r livecover}

pp = ggplot(barerock, aes(strata, rest, fill=site))
pp = pp + geom_bar(stat = "identity") + coord_flip() + 
  xlab("") + ylab("% Live Cover") + ylim(0,100) +
  theme_bw(base_size = 12) + theme(legend.position = 'none') + 
  facet_grid(~site)

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

```