EMODnet Chemistry [Points]_sl_plasticbags_760.rmd

---
title: "Dataset: Seabed litter - Plastic bags density (Nb. Items/km^2)"
author: Document produced by http://www.marine-analyst.eu
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document:
    df_print: paged
    number_section: yes
    theme: default
    toc: yes
    toc_depth: 2
    toc_float:
      collapsed: no
      smooth_scroll: yes
---

```{r setup, include=FALSE}
library(knitr)
library(kableExtra)
knitr::opts_chunk$set(
  eval = TRUE,
	echo = TRUE,
	fig.align = "center",
	message = FALSE,
	warning = FALSE,
	fig.width=5.5,
	out.width = "100%"
)
# clean environment
rm(list=ls())
gc()
```
```{r, include=FALSE, results='hide'}
# Edit the longitude and latitude coordinates to define the geographical area:
minlon=11.3 #minimum longitude
minlat=53.6 #minimum latitude
maxlon=15.5 #maximum longitude
maxlat=55.9 #maximum latitude
wdpaid=paste(minlon,minlat,maxlon,maxlat,sep="_")
Sessionid <- 'test'
wdpaidsplit <- unlist(strsplit(wdpaid, "[_]"))
xmin <- as.numeric(wdpaidsplit[1])
ymin <- as.numeric(wdpaidsplit[2])
xmax <- as.numeric(wdpaidsplit[3])
ymax <- as.numeric(wdpaidsplit[4])
```
```{r, include=FALSE, results='hide'}
source_provider <- "EMODnet Chemistry"
source_provider_url <- "https://www.emodnet.eu"
layer_title<-"Seabed litter - Plastic bags density"
layer="sl_plasticbags"
wfs_url <- "https://www.ifremer.fr/services/wfs/emodnet_chemistry2?"
wms_url <- "https://www.ifremer.fr/services/wms/emodnet_chemistry2?"
wms_layer="sl_plasticbags"
layer_id<-760
map_legend <- "littercount_by_squarekm"
map_label<-"surveyname"
link_csv<-paste0("./Report-", layer_id, "_", Sessionid, "_", wdpaid, "-csvfile.csv",sep="")
csvfile_name = paste("Report-", layer_id, "_", Sessionid, "_", wdpaid, "-csvfile.csv",sep="")
link_geojson<-paste0("./Report-", layer_id, "_", Sessionid, "_", wdpaid, "-geojsonfile.geojson",sep="")
geojsonfile_name = paste("Report-", layer_id, "_", Sessionid, "_", wdpaid, "-geojsonfile.geojson",sep="")
temp_path<- "."
```

```{r, include=FALSE, results='hide'}
library(rgdal)
library(downloader)
library(ggplot2)
library(mapdata)
library(ggmap)
library(ggrepel)
library(httr)
library(sf)
library(rasterVis)
library(rgeos)
library(sp)
library(raster)
library(dplyr)
library(XML)
```

# Data information

<TABLE BORDER=0 CELLSPACING=0 CELLPADDING=0 WIDTH=100&#37;><TR><TD WIDTH=100&#37; VALIGN=TOP><DIV ALIGN=JUSTIFY class=><DIV id=standard>This visualisation product displays plastic bags density per trawl per year. EMODnet Chemistry included the gathering of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. Densities have been calculated on each trawl and year using the following computation: Density of plastic bags (number of items per Km^2) = (total number of fishing related items)/(Swept area) Percentiles 50, 75 &amp; 95 have been calculated taking into account data from all years. Plastic bags reference codes taken into account for this product and information on data processing and calculation are detailed in the following document p22.: https://www.emodnet-chemistry.eu/repository/20190321_EMODnet_Beach_Seabed_ML_Products.pdf</DIV></DIV></TD></TR></TABLE>

```{r, include=FALSE, results='hide'}
# Script for Wekeo environment
sr=SpatialPolygons(list(Polygons(list(Polygon(cbind(c(xmin, xmin, xmax, xmax),c(ymax, ymin, ymin, ymax)))),"1")))
mpa=SpatialPolygonsDataFrame(sr, data.frame(cbind(1:1), row.names=c("1")))
proj4string(mpa)<-CRS("+proj=longlat +datum=WGS84")
bbox<-paste(xmin,ymin,xmax,ymax,sep=",")
```

```{r, include=FALSE, results='hide'}
# Link to Marine Analyst dataset page
link_marineanalyst <- paste0("http://marine-analyst.eu/dev.py?N=simple&O=",layer_id,"&maxlat=",ymax,"&maxlon=",xmax,"&minlon=",xmin,"&minlat=",ymin)
# Link to open the openlayer page for EMODnet HA
openlayer<-paste0("http://www.marine-analyst.eu/openlayers3/openlayer.py?wms_url=",wms_url,"/wms&wms_layer=",layer,"&bbox=",bbox)
```

## Metadata

Access <A HREF=`r toString(link_marineanalyst)` TARGET=_blank>metadata</A> from landing page<br>

# Geographical extent

## Coordinates

<A HREF=`r toString(link_marineanalyst)` TARGET=_blank>

```{r,echo=FALSE}
print (paste("West-Longitude:",round(xmin,2)))
print (paste("South-Latitude:",round(ymin,2)))
print (paste("East-Longitude:",round(xmax,2)))
print (paste("North-Latitude:",round(ymax,2)))
```

</a>

## Defined area

```{r ,echo=FALSE}
value<-(xmax-xmin)*(ymax-ymin)
if (value > 100) {
      zoom_value<-6
} else if (value > 1) {
      zoom_value<-7
} else {
      zoom_value<-8
}
base<-get_map(location=c(xmin-1,ymin-1,xmax+1,ymax+1), zoom=zoom_value, maptype="terrain-background", source = "stamen")
terrain <- ggmap(base)
map <- terrain + geom_polygon(data=mpa,aes(x=long,y=lat,group=group,fill="mpa"),colour="green",fill="blue",alpha=.1) +
ggtitle("")+xlab("Longitude")+ylab("Latitude")
plot(map)
```

Map tiles by <a href="http://stamen.com">Stamen Design</a>, under <a href="http://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>. Data by <a href="http://openstreetmap.org">OpenStreetMap</a>, under <a href="http://www.openstreetmap.org/copyright">ODbL</a>.

# `r toString(layer_title)`

## Access data

The Web Feature Service (WFS) of the `r toString(source_provider)` portal allows collecting the data:<BR><A HREF=
`r toString(wfs_url)`service=WFS&request=GetCapabilities&version=1.1.0 TARGET=_blank>`r toString(wfs_url)`service=WFS&request=GetCapabilities&version=1.1.0</A>
<BR><BR>**Available labels:**

```{r,echo=FALSE}
# DescribeFeatureType request function
DescribeFeatureType<-function(layer){
layer<-as.character(layer)

con<-paste0(wfs_url,"service=WFS&version=1.1.0&request=DescribeFeatureType&typeName=",layer,"&outpuformat=XMLSCHEMA")

xml <- "file.xml"
xml <- tempfile(xml)
httr::GET(con,write_disk(xml))
xmldoc <- XML::xmlParse(xml)
xml_data <- XML::xmlToList(xmldoc)
data.catalog <- data.frame(t(xml_data$complexType$complexContent$extension$sequence),row.names=NULL)

return(data.catalog)
}

WFS_DescribeFeatureType <- DescribeFeatureType(layer)

WFS_Colnames<-c()

for (i in 1:ncol(WFS_DescribeFeatureType)) {
WFS_Colnames<-append(WFS_Colnames, WFS_DescribeFeatureType[i]$element$element[1][["name"]])
}

WFS_Colnames

rez_nblist<- c(1:length(WFS_Colnames))
```

```{r, include=FALSE, results='hide'}
getWFSgml3<-function(layer){
layer<-as.character(layer)
con<-paste0(wfs_url,"service=WFS&version=1.0.0&request=GetFeature&typeName=",layer,"&OUTPUTFORMAT=gml3&srsName=EPSG%3A4326")
pipo<-sf::st_read(con)
return(pipo)
}

wfs_data<-getWFSgml3(layer)

#Transform mpa in Simple feature collection to perform the subsetting because wfs_data contains the whole info - use bbox and tyname are exclusive (Mapserver)
mpaSP <- as(mpa, "SpatialPolygonsDataFrame")
wfs_data<-wfs_data[st_as_sf(mpaSP),]
# get the geometry as lat and lon cols
wfs_data <- wfs_data %>% dplyr::mutate(lat = sf::st_coordinates(.)[,1],lon = sf::st_coordinates(.)[,2])
```

```{r, include=FALSE}
st_write(wfs_data, file.path(temp_path,csvfile_name), layer = csvfile_name, driver = "csv", delete_dsn = TRUE)
st_write(wfs_data, file.path(temp_path,geojsonfile_name), layer = geojsonfile_name, driver = "GeoJSON", delete_dsn = TRUE)
```

<br>**Download data for the defined geographical extent:**<br>

<TABLE BORDER=1 WIDTH=100%>
<TR>
<TD ALIGN=CENTER>Excel file</TD>
<TD ALIGN=CENTER>Geographic information</TD>
</TR>
<TR>
<TD ALIGN=CENTER><A HREF=`r toString(link_csv)`>**csv**</A></TD>
<TD ALIGN=CENTER><A HREF=`r toString(link_geojson)` TARGET=_blank>**geojson**</A></TD>
</TR>
</TABLE>

## Table

Browse table's columns by using the left and right arrows. Turn the table's pages with help of the previous/next buttons.<br><br>

```{r ,echo=FALSE}
if(nrow(wfs_data) > 0) {
wfs_data
} else {
print("No data available for the defined geographical extent")
}
```

## Map

```{r,echo=FALSE}
if(nrow(wfs_data) > 0) {
map <- ggplot() +
	borders("worldHires", fill = "gray", colour = "black", xlim = range(xmin,xmax), ylim = range(ymin,ymax), size = .25) +
theme(legend.position = "bottom") +
    theme(panel.grid.minor.y= element_blank(), panel.grid.minor.x = element_blank()) +
    geom_polygon(data=mpa,aes(x=long,y=lat,group=group,fill="mpa"),colour="green",fill="blue",alpha=.1) +
	geom_sf() +
	geom_point(data = wfs_data, aes(x = lat, y = lon, size =.data[[map_legend]]), fill = "red", color = "red", alpha = .4) +
	coord_sf(xlim = c(xmin, xmax),ylim = c(ymin, ymax))+
	ggtitle(layer_title)+xlab("Longitude (x)")+ylab("Latitude (y)")
map
} else {
print("No data available for the defined geographical extent")
}
```

## Map with id

```{r,echo=FALSE}
if(nrow(wfs_data) > 0) {
centroid<- st_centroid(wfs_data)
centroid<- cbind(wfs_data, st_coordinates(st_centroid(wfs_data$geometry)))
map <- ggplot() +
    borders("worldHires", fill = "gray", colour = "black", xlim = range(xmin,xmax), ylim = range(ymin,ymax), size = .25) +
theme(legend.position = "bottom") +
    theme(panel.grid.minor.y= element_blank(), panel.grid.minor.x = element_blank()) +
    geom_polygon(data=mpa,aes(x=long,y=lat,group=group,fill="mpa"),colour="green",fill="blue",alpha=.1) +
    geom_sf() +
    geom_point(data = wfs_data, aes(x = lat, y = lon, size = .data[[map_legend]]), fill = "red", color = "red", alpha = .4) +
    geom_text(data=centroid,aes(x=lat, y=lon, label=.data[[map_label]]), color = "black", fontface = "bold", size=2, hjust= 0, vjust=2, check_overlap = TRUE) +
    coord_sf(xlim = c(xmin, xmax),ylim = c(ymin, ymax))+
    ggtitle(layer_title)+xlab("Longitude (x)")+ylab("Latitude (y)")
map
} else {
print("No data available for the defined geographical extent")
}
```

## Interactive map

Visualise and access data with <A HREF=`r toString(openlayer)` TARGET=_blank>Openlayer (click on the map)<BR>

```{r, include=FALSE}
if(nrow(wfs_data) > 0) {
getWMSmap<-function (wms_layer,xmin,xmax,ymin,ymax)
{
width <- 960
height <- as.integer(width * (ymax-ymin) / (xmax-xmin))
wms_layer<-as.character(wms_layer)
bbox <- paste(xmin, ymin, xmax, ymax, sep = ",")
con<-paste0(wms_url,"/wms?SERVICE=WMS&VERSION=1.1.0&request=GetMap&layers=",wms_layer,"&format=image/jpeg&srs=EPSG:4326&bbox=",bbox,"&height=",height,"&width=",width,"")
        wms <- "img.png"
        wms <- tempfile(wms)
        download(con, wms, quiet = TRUE, mode = "wb")
        img <- brick(wms)
names(img) <- c("img.1", "img.2", "img.3")
img[img$img.1 == 255 & img$img.2 == 255 & img$img.3 == 255] <- NA
wms_basemap_url="http://www.gebco.net/data_and_products/gebco_web_services/web_map_service/mapserv"
wms_basemap_layer="gebco_latest"
con<-paste0(wms_basemap_url,"?SERVICE=WMS&VERSION=1.1.0&request=GetMap&layers=",wms_basemap_layer,"&format=image/png&srs=EPSG:4326&bbox=",bbox,"&height=",height,"&width=",width,"")
        wms <- "img.png"
        wms <- tempfile(wms)
        download(con, wms, quiet = TRUE, mode = "wb")
        basemap <- brick(wms)
names(basemap) <- c("img.1", "img.2", "img.3")
img <- merge(basemap,img)
img@extent@xmin <- xmin
img@extent@ymin <- ymin
img@extent@xmax <- xmax
img@extent@ymax <- ymax
proj4string(img)<-CRS("+proj=longlat +datum=WGS84")
return(img)
}
wms_img<-getWMSmap(wms_layer,xmin,xmax,ymin,ymax)
rggbplot <- function(inRGBRst,npix=NA,scale = 'lin'){
  rgblinstretch <- function(rgbDf){
    maxList <- apply(rgbDf,2,max)
    minList <- apply(rgbDf,2,min)
    temp<-rgbDf
    for(i in c(1:3)){
      temp[,i] <- (temp[,i]-minList[i])/(maxList[i]-minList[i])
    }
    return(temp)
  }
  rgbeqstretch<-function(rgbDf){
    temp<-rgbDf
    for(i in c(1:3)){
      unique <- na.omit(temp[,i])
      if (length(unique>0)){
        ecdf<-ecdf(unique)
        temp[,i] <- apply(temp[,i,drop=FALSE],2,FUN=function(x) ecdf(x))
      }
    }
    return(temp)
  }
      npix <- ncell(inRGBRst)
  x <- sampleRegular(inRGBRst, size=npix, asRaster = TRUE)
  dat <- as.data.frame(x, xy=TRUE)
  colnames(dat)[3:5]<-c('r','g','b')
  if(scale=='lin'){
    dat[,3:5]<- rgblinstretch(dat[,3:5])
  } else if(scale=='stretch'){
    dat[,3:5]<- rgbeqstretch(dat[,3:5])
  }
  p <- ggplot()+ geom_tile(data=dat, aes(x=x, y=y, fill=rgb(r,g,b))) + scale_fill_identity()
}
}
```

```{r, echo=FALSE}
if(nrow(wfs_data) > 0) {
map <- rggbplot(wms_img)+
#borders("worldHires", fill = "gray", colour = "black", xlim = range(xmin,xmax), ylim = range(ymin,ymax), size = .25) +
coord_quickmap(xlim=range(xmin,xmax),ylim=range(ymin,ymax))+
ggtitle(layer_title)+xlab("Longitude")+ylab("Latitude")
plot(map)
} else {
print("No data available for the defined geographical extent")
}
```

</a>

# Litter abundance per year

```{r, echo=FALSE}
if(nrow(wfs_data) > 0) {
abundance <- ggplot() +
geom_point(data = wfs_data, aes(x = year, y = littercount_by_squarekm, shape=surveyname, group=surveyname ,color=as.character(survey_id)), size=4) +
ggtitle("Density per year and location")+xlab("Year")+ylab("Nb. items/km^2")
plot(abundance)
} else {
print("No data available for the defined geographical extent")
}
```