In juldebar/IRDTunaAtlas: IRD (www.ird.fr) - UMR EME - Tuna atlas

library(flexdashboard)
library(shiny)
library(dplyr)
library(XML)
library(DBI)
library(RPostgres)
library(RPostgreSQL)
library(dplyr)
library(leaflet)
library(leaflet.extras)
library(leafpm)
library(mapedit)
library(pacman)
library(sf)
library(sp)
library(plotly)
library(ggplot2)
library(plyr)

#######################
##Database Identifiers:
DRV=RPostgres::Postgres()
source(file = "~/Desktop/CODES/IRDTunaAtlas/credentials.R")
source(file = "~/Bureau/CODES/IRDTunaAtlas/credentials.R")
####################################################################################################################################################################################################################################
source("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/R/TunaAtlas_i1_SpeciesByOcean.R")
source("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/R/TunaAtlas_i2_SpeciesByGear.R")
source("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/R/TunaAtlas_i3_SpeciesYearByGearMonth.R")
source("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/R/TunaAtlas_i4_SpeciesMonthByOcean.R")
source("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/R/TunaAtlas_i6_SpeciesMap.R")
source("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/R/TunaAtlas_i7_SpeciesMapRelativeCatches.R")
source("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/R/TunaAtlas_i8_SpeciesMapRelativeCatchesOtherSpecies.R")
####################################################################################################################################################################################################################################

Column {data-width=350}

Select parameters to customize plots 1, 2, 3 and 4 (see Github wiki)

new_wkt <- 'POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'
wkt <- reactiveVal(new_wkt) 

# Check http://jeffgoldsmith.com/p8105_f2017/shiny.html
#Loading related dataset
query_i1i2 <- ("select * from i1i2 ;")
df_nominal <- dbGetQuery(con, query_i1i2)
# head(df_nominal)
# colnames(df_nominal)
species = unique(df_nominal$species)
gear_group = unique(df_nominal$gear_group)
ocean = unique(df_nominal$ocean)

query_i3 <- ("select year, month,  c_esp AS species,  c_g_engin AS gear_type, value, mean_prev_5_years, stddev_prev_5_years from i3 ;")
df_i3 <- dbGetQuery(con, query_i3)
gear_group_i3 = unique(df_i3$gear_type)
year_i3 = unique(df_i3$year)
# unique(df_i3$species)
# unique(df_i3$year)
# colnames(df_i3)
# unique(df_i3$gear_type)

# selectInput widget

query_i4 <- ("select species, year, month, ocean, value from i4 ;")
df_i4 <- dbGetQuery(con, query_i4)
year_i4 = unique(df_i4$year)



query_i6i7i8 <- ("select ogc_fid, geom_id, geom, year, species, country, value, count from i6i7i8;")
df_i6i7i8 = st_read(con, query = query_i6i7i8)
year_i6i7i8 = unique(df_i6i7i8$year)
species_i6i7i8 = unique(df_i6i7i8$species)




# selectInput widget

selectInput("species_choice", label = h3("Select species for i1, i2 and i3 "), choices = species, selected = "SKJ")
selectInput("species_choice_i8", label = h3("Select species for i8"), choices = species_i6i7i8, selected = c("SKJ","YFT"), multiple = TRUE)

selectInput("ocean_choice", label = h3("Select ocean for i1"), choices = ocean, selected = "ICCAT",multiple = TRUE)
selectInput("gear_choice", label = h3("Select gear(s) for i2"), choices = gear_group, selected = "PS",multiple = TRUE)

selectInput("gear_choice_i3", label = h3("Select gear(s) for i3"), choices = gear_group_i3, selected = "PS",multiple = TRUE)
selectInput("year_i3", label = h3("Select year for i3"), choices = year_i3, selected = "2000",multiple = FALSE)
selectInput("year_choice_i6i7i8", label = h3("Select year for i3"), choices = year_i6i7i8, selected = c("2000","2001"),multiple = TRUE)


# textOutput(wkt())

renderPrint({ 
  paste0("Your current selection: ", input$species_choice," and ", input$gear_choice," and ", input$ocean_choice)
  paste0("Your current selection for i3 : ", input$species_choice," and ", input$gear_choice_i3," and ", input$year_i3)
  paste0("The area you have selected is (WKT format): ", wkt())
})

Select WKT on the map

To be done : add all WMS layers of input datasets

output$mymap <- renderLeaflet(
leaflet() %>%
addTiles() %>%
# addPmToolbar(
#   toolbarOptions = pmToolbarOptions(drawMarker = FALSE, position = "topright"),
#   drawOptions = pmDrawOptions(snappable = FALSE, allowSelfIntersection = FALSE),
#   editOptions = pmEditOptions(preventMarkerRemoval = TRUE, draggable = FALSE),
#   cutOptions = pmCutOptions(snappable = FALSE, allowSelfIntersection = FALSE)
# ) %>%
    addDrawToolbar(
        targetGroup = "draw",
        editOptions = editToolbarOptions(
            selectedPathOptions = selectedPathOptions()
        )
    )   %>% setView(lng =48, lat =-8, zoom = 5
        ) %>% addWMSTiles(
    # "http://thredds.oreme.org:8080/thredds/wms/MARBEC/Seychelles/global-reanalysis-phy-001-030-monthly_SEY_t55_199301-201906.nc",
    "https://geoserver-sdi-lab.d4science.org/geoserver/sdilab_geoflow/ows?service=WMS",
    layers = "global_catch_5deg_1m_ird_level0",
        # layers = "thetao",
        options = WMSTileOptions(format = "image/png", transparent = TRUE), group ="Seatizen", attribution = "Seatizen WMS"
  )  %>%
    addLayersControl(
        overlayGroups = c("draw"),
        options = layersControlOptions(collapsed = FALSE)
    ) 

)
    leafletOutput('mymap')  


        observe({
              #use the draw_stop event to detect when users finished drawing
            feature <- input$mymap_draw_new_feature
            req(input$mymap_draw_stop)
            print(feature)
            polygon_coordinates <- input$mymap_draw_new_feature$geometry$coordinates[[1]]
            # see  https://rstudio.github.io/leaflet/shiny.html
            bb <- input$mymap_bounds 
            geom_polygon <- input$mymap_draw_new_feature$geometry
            # drawn_polygon <- Polygon(do.call(rbind,lapply(polygon_coordinates,function(x){c(x[[1]][1],x[[2]][1])})))
            geoJson <- geojsonio::as.json(feature)
            # spdf <- geojsonio::geojson_sp(feature)
            geom <- st_read(geoJson)
            wkt(st_as_text(st_geometry(geom[1,])))
            coord <- st_as_text(st_geometry(geom[1,]))

            north <- polygon_coordinates[[1]][[1]]
            south <- polygon_coordinates[[2]][[1]]
            east <- polygon_coordinates[[1]][[2]]
            west <- polygon_coordinates[[2]][[2]]


            if(is.null(polygon_coordinates))
                return()
            text<-paste("North ", north, "South ", east)

            mymap_proxy = leafletProxy("mymap") %>% clearPopups() %>% addPopups(south,west,coord)
            textOutput("wkt")
        })

Column {data-width=650}

Plot 1 Indicator I1 : Annual catches by ocean

#unique(df_nominal$species)
# nrow(df_nominal)
# colnames(df_nominal)
# unique(df_nominal$ocean)

# Setting parameters (hard coded here) with selectInput widget

output$plot1 <- renderPlotly({ 
#unique(df_nominal$species)
# nrow(df_nominal)
# colnames(df_nominal)
# unique(df_nominal$ocean)

# Setting parameters (hard coded here) with selectInput widget

if(wkt()!=new_wkt){
  df_nominal = st_read(con, query = paste0("SELECT sum(value) as value, unit, ocean, gear_group, year, species, st_collect(geom) FROM public.i1i2_spatial WHERE ST_Within(geom,ST_GeomFromText('",wkt(),"',4326)) GROUP BY  unit, ocean, gear_group, year, species ;"))
}
  #Filtering input dataset with selected parameters

df_i1 <- df_nominal %>% filter(species %in% input$species_choice,ocean %in% input$ocean_choice) 


i1 <- Atlas_i1_SpeciesByOcean(as.data.frame(df_i1), 
                         yearAttributeName="year", 
                         oceanAttributeName="ocean", 
                         speciesAttributeName="species", 
                         valueAttributeName="value")
})

plotlyOutput("plot1")

Plot 2 Indicator I2 : Annual catches by gear

output$plot2 <- renderPlotly({ 
# renderPlot({ 

target_species <- c("SKJ")
target_gear <- c("PS")

#Filtering input dataset with selected parameters
colnames(df_nominal)
# df_i2 <- df_nominal %>% filter(gear_group %in% target_gear,species %in% target_species) %>% dplyr::rename(gear_type=gear_group)
df_i2 <- df_nominal %>% filter(gear_group %in% input$gear_choice,species %in% input$species_choice) %>% dplyr::rename(gear_type=gear_group)

i2 <- Atlas_i2_SpeciesByGear(df_i2,yearAttributeName="year", 
                                    speciesAttributeName="species",
                                    valueAttributeName="value",
                                   gearTypeAttributeName="gear_type",
                                    withSparql=FALSE)
# i2
})
plotlyOutput("plot2")

Plot 8 IIndicator I8 : Catches relative contribution over other species

# https://plotly.com/r/maps-sf/
# https://ggplot2.tidyverse.org/reference/ggsf.html
output$plot8 <- renderPlotly({ 

# target_species <- c("BET","YFT","SKJ")
# target_year <- c("2003","2004")
# df_i6i7i8_filtered <- df_i6i7i8 %>% filter(year %in% target_year, species %in% target_species)
df_i8_filtered <- df_i6i7i8 %>% filter(year %in% input$year_choice_i6i7i8, species %in% input$species_choice_i8)
# colnames(df_i6i7i8)

# df_i8_filtered <- as(df_i6i7i8_filtered, "Spatial")
i8 <-  plot_geo(df_i8_filtered)
# i8 <- Atlas_i8_SpeciesMapRelativeCatchesOtherSpecies(df=df_i8_filtered,
#                                              targetedSpecies="YFT",
#                                              geomIdAttributeName="geom_id",
#                                              yearAttributeName="year",
#                                              speciesAttributeName="species",
#                                              valueAttributeName="value",
#                           withSparql=FALSE
#                           )


})
plotlyOutput("plot8")

Plot 7 Indicator I7 : 5x5 degrees relative contribution to catches

output$plot7 <- renderPlotly({ 

df_i6i7i8_filtered <- df_i6i7i8 %>% filter(year %in% input$year_i3, species %in% input$species_choice) 
df_i7_filtered <- as(df_i6i7i8_filtered, "Spatial")

i7 <-  Atlas_i7_SpeciesMapRelativeCatches(df=df_i7_filtered, 
                              geomIdAttributeName="geom_id",
                              yearAttributeName="year", 
                              speciesAttributeName="species",                                         
                              valueAttributeName="value",
                          withSparql=FALSE
                          )

})

plotlyOutput("plot7")

Plot 6 Indicator I6 : Catches by 5x5 degrees

output$plot6 <- renderPlotly({ 

df_i6i7i8_filtered <- df_i6i7i8 %>% filter(year %in% input$year_i3, species %in% input$species_choice) 
df_i6_filtered <- as(df_i6i7i8_filtered, "Spatial")

i6 <- Atlas_i6_SpeciesMap(df=df_i6_filtered,
                          geomIdAttributeName="geom_id",
                          yearAttributeName="year",
                          speciesAttributeName="species",
                          valueAttributeName="value",
                          withSparql=FALSE
                          )

})
plotlyOutput("plot6")

Plot 4 Indicator I4 : Catches by month and by ocean

# renderPlot({
output$plot4 <- renderPlotly({ 

df_i4_filtered <- df_i4 %>% filter(year %in% input$year_i3, ocean %in% input$ocean_choice, species %in% input$species_choice) 

i4 <- Atlas_i4_SpeciesMonthByOcean(df=df_i4_filtered, 
                                          oceanAttributeName="ocean", 
                                          yearAttributeName="year", 
                                          monthAttributeName="month",
                                          speciesAttributeName="species", 
                                          valueAttributeName="value",
                                   withSparql=FALSE
                                      )

# i4

})

plotlyOutput("plot4")

Plot 3 Indicator I3 : Catches by gear and by month

# target_species <- c("SKJ")
# target_gear <- c("BB","PS","LL","Baitboat")
# target_year <- c("2004")

# Setting parameters (hard coded here) with selectInput widget

# renderPlot({
output$plot3 <- renderPlotly({ 

# df_i3_filtered <- df_i3 %>% filter(gear_group %in% input$gear_choice,species %in% input$species_choice) %>% rename(gear_type=gear_group)
# df_i3_filtered <- df_i3 %>% filter(year %in% target_year, gear_type %in% target_gear, species %in% target_species) 
df_i3_filtered <- df_i3 %>% filter(year %in% input$year_i3, gear_type %in% input$gear_choice_i3, species %in% input$species_choice) 

i3 <- Atlas_i3_SpeciesYearByGearMonth(df=df_i3_filtered,
                                      yearAttributeName="year",
                                      monthAttributeName="month",
                                      speciesAttributeName="species",
                                      gearTypeAttributeName="gear_type",
                                      valueAttributeName="value",
                                      meanPrev5YearsAttributeName="mean_prev_5_years",
                                      stddevPrev5YearsAttributeName="stddev_prev_5_years",
                                      withSparql=FALSE
                                      )
i3
})

plotlyOutput("plot3")