shiny/sardara_shiny_i1i2_all_datasets/app.R
In juldebar/IRDTunaAtlas: IRD (www.ird.fr) - UMR EME - Tuna atlas
library(shiny)
library(geojsonio)
library(plyr)
library(dplyr)
library(DBI)
library(leaflet)
library(leaflet.extras)
library(leafpm)
library(mapedit)
library(sf)
library(sp)
library(plotly)
library(DT)
library(ncdf4)
library(raster)
library(maps)
library(ggplot2)
library(XML)
library(streamgraph)
library(viridis)
library(xts)
library(dygraphs)
library(tidyr)
####################################################################################################################################################################################################################################
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")
####################################################################################################################################################################################################################################
DRV=RPostgres::Postgres()
# source(file = "~/Desktop/CODES/IRDTunaAtlas/credentials.R")
source(file = "~/Bureau/CODES/IRDTunaAtlas/credentials.R")
###################################################################################################################################################################################################################################
new_wkt <- 'POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'
wkt <- reactiveVal(new_wkt)
switch_unit <- reactiveVal(TRUE)
query_metadata <- reactiveVal()
query_all_datasets <- reactiveVal()
# target_species<- c("YFT","BFT")
# target_year <- c(seq(1:10)+1994)
target_dataset <- dbGetQuery(con, "SELECT DISTINCT(dataset) FROM public.i1i2_spatial_all_datasets ORDER BY dataset;")
target_species <- dbGetQuery(con, "SELECT DISTINCT(species) FROM public.i1i2_spatial_all_datasets ORDER BY species;")
target_year <- dbGetQuery(con, "SELECT DISTINCT(year) FROM public.i1i2_spatial_all_datasets ORDER BY year;")
target_gear <- dbGetQuery(con, "SELECT DISTINCT(gear_group) as gear FROM public.i1i2_spatial_all_datasets ORDER BY gear_group;")
target_ocean <- dbGetQuery(con, "SELECT DISTINCT(ocean) as ocean FROM public.i1i2_spatial_all_datasets ORDER BY ocean;")
target_unit <- dbGetQuery(con, "SELECT DISTINCT(unit) AS unit FROM public.i1i2_spatial_all_datasets ORDER BY unit;")
target_area <- dbGetQuery(con, "SELECT DISTINCT(area)::varchar AS area FROM public.i1i2_spatial_all_datasets ORDER BY area DESC;")
default_species <- 'YFT'
default_year <- c(seq(min(target_year):max(target_year))+min(target_year)-1)
# default_gear <- c('BB','PS')
default_gear <- unique(target_gear)
# default_dataset <- 'global_catch_5deg_1m_firms_level0'
default_dataset <- unique(target_dataset$dataset)
default_unit <- c('MT','t','no')
# default_unit <- unique(target_unit$unit)
# default_area <- '25'
default_area <- unique(target_area$area)
#check what are existing / possible combinations between dimension values
filters_combinations <- dbGetQuery(con, "SELECT species, year, gear_group as gear FROM public.i1i2_spatial_all_datasets GROUP BY species, year,gear_group;")
ui <- fluidPage(
titlePanel("Tuna Atlas: spatial indicators (maps, charts, plots...)"),
navbarPage(title="TunaAtlas",
tabPanel("Interactive Indicator 11",
div(class="outer",
tags$head(includeCSS("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/styles.css")),
# leafletOutput('map_i11', width = "60%", height = 1500),
leafletOutput("mymap", width="100%", height="100%"),
absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 150, left = "3%", width = "21%", height = "auto",
selectInput(
inputId = "dataset",
label = "Dataset",
choices = target_dataset$dataset,
multiple = TRUE,
selected= default_dataset,
width = "99%"
),
selectInput(
inputId = "unit",
label = "Unit",
choices = target_unit$unit,
multiple = TRUE,
selected= default_unit,
width = "99%"
),
selectInput(
inputId = "area",
label = "Area",
choices = target_area$area,
multiple = TRUE,
selected= default_area,
width = "99%"
),
selectInput(
inputId = "species",
label = "Species",
choices = target_species$species,
multiple = TRUE,
selected= default_species,
width = "99%"
),
selectInput(
inputId = "year",
label = "Year",
choices = target_year$year,
multiple = TRUE,
selected= default_year,
width = "99%"
),
selectInput(
inputId = "gear",
label = "Gear",
choices = target_gear$gear,
multiple = TRUE,
selected= target_gear$gear,
width = "99%"
),
textInput("yourWKT","Paste you WKT",value=new_wkt),
verbatimTextOutput("value"),
actionButton(
inputId = "submit",
label = "Submit"
),
actionButton("resetWkt", "Reset WKT to global"),
tags$br()
),
absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 150, left = "auto", right="1%", width = "23%", height = "auto",
tags$br(),
plotlyOutput("pie_area_catch", width="100%"),
tags$br(),
actionButton(
inputId = "switched",
label = "Switch unit for pie chart"
),
plotlyOutput("barplot_datasets", width="100%"),
tags$br(),
plotlyOutput("pie_ratio_catch")
),
absolutePanel(id = "controls", class = "panel panel-default", bottom = "2%", left = "25%", width = "50%", fixed=TRUE, draggable = FALSE, height = "auto",
plotlyOutput("plotly_time_series_all_datasets")
# fluidRow(
# column(10,dygraphOutput("dygraph_all_datasets",height="400")),
# column(2,textOutput("legendDivID"))
# )
),
absolutePanel(id = "logo", class = "card", bottom = 15, left = 60, width = 80, fixed=TRUE, draggable = FALSE, height = "auto",
tags$a(href='https://www.ird.fr/', tags$img(src='https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/logo_IRD.svg',height='178',width='216'))
)
)
),
tabPanel(
title = "Plot indicator 2",
plotlyOutput("plot2")
),
tabPanel(
title = "Streamgraph indicator 2",
streamgraphOutput("plot2_streamgraph")
),
navbarMenu("Browse Data Tables",
tabPanel(
title = "Browse query_metadata",
DT::dataTableOutput("DT_query_metadata")
),
tabPanel(
title = "Browse data_all_datasets",
DT::dataTableOutput("DT_data_all_datasets")
),
tabPanel(
title = "Browse data_barplot_all_datasets",
DT::dataTableOutput("DT_data_barplot_all_datasets")
)
),
# tabPanel(
# title = "Your filters",
# textOutput("selected_var")
# ),
navbarMenu("Browse underlying SQL queries",
tabPanel(
title = "The main SQL query: sql_query: sql_query",
textOutput("sql_query")
),
tabPanel(
title = "SQL query: query_metadata",
textOutput("query_metadata")
),
tabPanel(
title = "SQL query: query_all_datasets",
textOutput("query_all_datasets")
)
),
tabPanel("About",
fluidRow(
column(6,
includeMarkdown("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/README.md")
),
column(6,
img(class="logo_IRD",
src=paste0("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/logo_IRD.svg")),
tags$small(
"Source: IRD",
"Julien Barde ",
"Funding : BlueCloud ",
a(href="https://www.documentation.ird.fr/hor/fdi:010012425",
"IRD Tuna Atlas (Alain Fontenau)"),
a(href="https://github.com/juldebar/IRDTunaAtlas/wiki/Indicator-I11-:-Catches-by-country",
"IRD Indicator 11"),
a(href="https://www.documentation.ird.fr/hor/fdi:010012425",
"IRD Tuna Atlas (Alain Fontenau)"),
a(href="https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers11-03/010012425.pdf",
"PDF")
)
)
)
)
)
)
server <- function(input, output, session) {
########################################################## Dynamic filters ##########################################################
change <- reactive({
unlist(strsplit(paste(c(input$species,input$year,input$gear),collapse="|"),"|",fixed=TRUE))
})
observeEvent(input$resetWkt, {
wkt(new_wkt)
})
observeEvent(input$species,{
temp <- filters_combinations %>% filter(species %in% change()[1])
updateSelectInput(session,"year",choices = unique(temp$year),selected=c(seq(min(temp$year):max(temp$year))+min(temp$year)-1))
updateSelectInput(session,"gear",choices = unique(temp$gear),selected=unique(temp$gear))
}
)
observeEvent(input$switched, {
if(switch_unit()){switch_unit(FALSE)}else{switch_unit(TRUE)}
})
# observeEvent(input$year,{
# temp <- filters_combinations %>% filter(species %in% change()[1], year %in% change()[2])
# updateSelectInput(session,"gear",choices = unique(temp$gear),selected=unique(temp$gear))
# }
# )
########################################################## DATA & SQL queries ##########################################################
#set the main parameterized query (options for geom might be st_collect(geom) or ST_ConvexHull(st_collect(geom)) as convexhull )
sql_query <- eventReactive(input$submit, {
if(is.null(input$year)){year_name=target_year$year}else{year_name=input$year}
# if(is.null(input$dataset)){dataset_name=target_dataset$dataset}else{year_name=input$dataset}
query <- glue::glue_sql(
"SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets
WHERE ST_Within(geom,ST_GeomFromText(({wkt*}),4326))
AND dataset IN ({dataset_name*})
AND species IN ({species_name*})
AND gear_group IN ({gear_group_name*})
AND year IN ({year_name*})
AND area::varchar IN ({area_name*})
AND unit IN ({unit_name*}) ",
wkt = wkt(),
dataset_name = input$dataset,
species_name = input$species,
gear_group_name = input$gear,
year_name = year_name,
unit_name = input$unit,
area_name = input$area,
.con = con)
},
ignoreNULL = FALSE)
metadata <- reactive({
query_metadata(paste0("SELECT geom, sum(value) AS value FROM(",sql_query(),") AS foo GROUP BY geom"))
st_read(con, query = query_metadata())
})
# unit, ocean, gear_group, year, species ;")
data_all_datasets <- eventReactive(input$submit, {
query_all_datasets(paste0("SELECT dataset, to_date(year::varchar(4),'YYYY') AS year, species, sum(value) as value, unit FROM (",sql_query(),") AS foo GROUP BY dataset, year, species, unit"))
st_read(con, query = query_all_datasets())
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_pie_all_datasets <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, sum(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_pie_area_catch <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, area, unit, count(*), SUM(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset, area, unit ORDER BY dataset"))
},
ignoreNULL = FALSE)
data_barplot_all_datasets <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, unit, count(*)::decimal AS count, SUM(value) AS value FROM (",sql_query(),") AS foo GROUP BY dataset, unit ORDER BY dataset"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_i1 <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, unit, ocean, to_date(year::varchar(4),'YYYY') AS year, species, sum(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset,unit, ocean, year, species"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_i2 <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT unit, gear_group AS gear_type, year, species, sum(value) as value FROM (",sql_query(),") AS foo GROUP BY unit, gear_group, year, species"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
########################################################## Outputs: text & Data tables ##########################################################
output$value <- renderText({
wkt()
# output$value <- renderText({ input$caption })
})
output$sql_query <- renderText({
paste("Your SQL Query is : \n", sql_query())
})
output$query_metadata <- renderText({
paste("Your SQL Query is : \n", query_metadata())
})
output$query_all_datasets <- renderText({
paste("Your SQL Query is : \n", query_all_datasets())
})
# output$DT_data <- renderDT({
# data()
# })
output$DT_query_metadata <- renderDT({
metadata() %>% st_drop_geometry()
})
output$DT_data_all_datasets <- renderDT({
data_all_datasets()
})
output$DT_data_barplot_all_datasets <- renderDT({
# data_barplot_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0)
data_barplot_all_datasets()
})
output$selected_var <- renderText({
paste("You have selected:\n", input$species, "and \n", input$year, "and \n", input$flag, "and \n", wkt())
})
########################################################## Outputs: maps / plots / charts ##########################################################
output$mymap <- renderLeaflet({
df <- metadata()
centroid <- st_convex_hull(df) %>% st_centroid()
lat_centroid <- st_coordinates(centroid)[2]
lon_centroid <- st_coordinates(centroid)[1]
pal <- colorNumeric(
palette = "YlGnBu",
domain = df$value
)
# brewer.pal(7, "OrRd")
pal_fun <- colorQuantile( "YlOrRd", NULL, n = 10)
qpal <- colorQuantile(rev(viridis::viridis(10)),
df$value, n=10)
# https://r-spatial.github.io/sf/articles/sf5.html
map_leaflet <- leaflet() %>%
setView(lng = lon_centroid, lat =lat_centroid, zoom = 3
) %>%
clearBounds() %>%
addProviderTiles("Esri.OceanBasemap") %>%
addPolygons(data = df,
label = ~value,
popup = ~paste0("Captures pour cette espece: ", round(value), " tonnes(t) et des brouettes"),
# fillColor = ~pal_fun(value),
fillColor = ~qpal(value),
fill = TRUE,
fillOpacity = 0.8,
smoothFactor = 0.5
# color = ~pal(value)
) %>%
addDrawToolbar(
targetGroup = "draw",
editOptions = editToolbarOptions(
selectedPathOptions = selectedPathOptions()
)
) %>%
addLayersControl(
overlayGroups = c("draw"),
options = layersControlOptions(collapsed = FALSE)
) %>%
leaflet::addLegend("bottomright", pal = qpal, values = df$value,
title = "Total catch per cell for selected criteria",
labFormat = labelFormat(prefix = "MT "),
opacity = 1
)
})
observe({
#use the draw_stop event to detect when users finished drawing
feature <- input$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$geometry
# drawn_polygon <- Polygon(do.call(rbind,lapply(polygon_coordinates,function(x){c(x[[1]][1],x[[2]][1])})))
geoJson <- geojsonio::as.json(feature)
# spdf <- geojsonio::geojson_sp(feature)
geom <- st_read(geoJson)
wkt(st_as_text(st_geometry(geom[1,])))
coord <- st_as_text(st_geometry(geom[1,]))
north <- polygon_coordinates[[1]][[1]]
south <- polygon_coordinates[[2]][[1]]
east <- polygon_coordinates[[1]][[2]]
west <- polygon_coordinates[[2]][[2]]
if(is.null(polygon_coordinates))
return()
text<-paste("North ", north, "South ", east)
mymap_proxy = leafletProxy("mymap") %>% clearPopups() %>% addPopups(south,west,coord)
textOutput("wkt")
})
output$plot2 <- renderPlotly({
# df_i2 = data() %>% filter(year %in% input$year) %>% filter(gear_group %in% input$gear) %>% group_by(year,gear_group,species) %>% summarise(value = sum(value)) %>% dplyr::rename(gear_type=gear_group)
df_i2 <- data_i2()
i2 <- Atlas_i2_SpeciesByGear(as.data.frame(df_i2),
yearAttributeName="year",
speciesAttributeName="species",
valueAttributeName="value",
gearTypeAttributeName="gear_type",
withSparql=FALSE)
i2
})
output$plot2_streamgraph<- renderStreamgraph({
df_i2 = data_i2() %>%
streamgraph("gear_type", "value", "year", offset="zero", interpolate="step") %>%
sg_axis_x(1, "year", "%Y") %>%
sg_fill_brewer("PuOr") %>%
sg_legend(show=TRUE, label="I=RFMO - names: ")
})
output$dygraph_all_datasets <- renderDygraph({
# df_i1 = data_all_datasets() %>% filter(unit %in% c('t','MT')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
# df_i1 = data_all_datasets() %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 = data_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 <- as_tibble(df_i1) # %>% top_n(3)
tuna_catches_timeSeries <-NULL
if(length(unique(df_i1$unit))>1){
df_i1_t <- df_i1 %>% filter(unit == 't')
df_i1_no <- df_i1 %>% filter(unit == 'no')
# if(switch_unit()){
# df_i1 <- df_i1_no
# }else{
# df_i1 <- df_i1_t
# }
# if(length(colnames(dplyr::select(df_i1_t,-c(species,year,unit))))>1){
for(d in 1:length(colnames(dplyr::select(df_i1_t,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1_t,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_t, this_dataset))>0){
df_i1_t <- df_i1_t %>% dplyr::rename(!!paste0(this_dataset,'_t') := !!this_dataset)
this_time_serie <- xts(x = dplyr::select(df_i1_t, c(paste0(this_dataset,'_t'))), order.by = df_i1_t$year)
if(d==1){tuna_catches_timeSeries <- this_time_serie}else{
tuna_catches_timeSeries <- cbind(tuna_catches_timeSeries, this_time_serie)
}
}
}
# if(length(colnames(dplyr::select(df_i1_no,-c(species,year,unit))))>1){
for(d in 1:length(colnames(dplyr::select(df_i1_no,-c(species,year,unit))))){
this_dataset <- colnames(dplyr::select(df_i1_no,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_no,this_dataset))>0){
df_i1_no <- df_i1_no %>% dplyr::rename(!!paste0(this_dataset,'_no') := !!this_dataset)
this_time_serie <- xts(x = dplyr::select(df_i1_no, c(paste0(this_dataset,'_no'))), order.by = df_i1_no$year)
tuna_catches_timeSeries <- cbind(tuna_catches_timeSeries, this_time_serie)
}
}
}else{
for(d in 1:length(colnames(dplyr::select(df_i1,-c(species,year,unit))))){
this_dataset <- colnames(dplyr::select(df_i1,-c(species,year,unit)))[d]
this_time_serie <- xts(x = dplyr::select(df_i1, c(this_dataset)), order.by = df_i1$year)
if(d==1){tuna_catches_timeSeries <- this_time_serie}else{
tuna_catches_timeSeries <- cbind(tuna_catches_timeSeries, this_time_serie)
}
}
# create the area chart
# g1 <- dygraph(tuna_catches_timeSeries) # %>% dyOptions( fillGraph=TRUE )
# g1 <- dygraph(tuna_catches_timeSeries, main = "Catches by ocean") %>% dyRangeSelector() %>% dyLegend(labelsDiv = "legendDivID")
# %>%
# dyStackedBarGroup(c('global_catch_5deg_1m_firms_level0', 'global_catch_1deg_1m_ps_bb_firms_level0','spatial','nominal'))
# >% dyOptions( fillGraph=TRUE) %>% # create bar chart with the passed dygraph
# dyOptions(stackedGraph = stack())
# dySeries(iotc, label = "iotc") %>%
# dySeries(iattc, label = "iattc") %>%
# dySeries(iccat, label = "iccat")
#
}
g1 <- dygraph(tuna_catches_timeSeries, main = "Times series of values with selected units (tons or numbers) for the selected datasets") %>% dyRangeSelector() %>% dyLegend(labelsDiv = "legendDivID", labelsSeparateLines = T)
})
output$plotly_time_series_all_datasets <- renderPlotly({
# df_i1 = st_read(con, query = "SELECT dataset, to_date(year::varchar(4),'YYYY') AS year, species, sum(value) as value, unit FROM (SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets WHERE ST_Within(geom,ST_GeomFromText(('POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'),4326)) AND dataset IN ('global_catch_1deg_1m_ps_bb_firms_level0', 'global_catch_5deg_1m_firms_level0', 'global_catch_firms_level0', 'global_nominal_catch_firms_level0') AND species IN ('YFT') AND gear_group IN ('BB', 'LL', 'OTHER', 'PS', 'UNK') AND year IN ('1918', '1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019') AND area::varchar IN ('NA', '9101.3790835111', '825', '775', '750', '6995.49529425369', '6014.62349518009', '600', '525', '50', '450', '400', '375', '325', '3125', '3021.33388249682', '2750', '2625', '2612.4315513254', '2550', '250', '25', '2375', '2325', '2275', '2100', '200', '1425', '1350', '1300', '1250', '1125', '10040.35433829', '100', '1') AND unit IN ('MT', 'no', 't') ) AS foo GROUP BY dataset, year, species, unit") %>%
# mutate(unit=replace(unit,unit=='MT', 't')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 = data_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 <- as_tibble(df_i1) # %>% top_n(3)
if(length(unique(df_i1$unit))>1){
df_i1_t <- df_i1 %>% filter(unit == 't')
df_i1_no <- df_i1 %>% filter(unit == 'no')
for(d in 1:length(colnames(dplyr::select(df_i1_t,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1_t,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_t, this_dataset))>0){
# data <- df_i1_t %>% dplyr::select(c(year,!!this_dataset))
if(d==1){
fig <- plot_ly(df_i1_t, x = df_i1_t$year, y =df_i1_t[,this_dataset][[1]], name = paste0(this_dataset,"_",d), type = 'scatter', mode = 'lines' )
}else{
fig <- fig %>% add_trace(y = df_i1_t[,this_dataset][[1]], name = paste0(this_dataset,"_t"), mode = 'lines')
}
}
}
for(d in 1:length(colnames(dplyr::select(df_i1_no,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1_no,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_no, this_dataset))>0){
fig <- fig %>% add_trace(x = df_i1_no$year, y = df_i1_no[,this_dataset][[1]], name = paste0(this_dataset,"_no"), mode = 'lines')
}
}
}else{
for(d in 1:length(colnames(dplyr::select(df_i1,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1, this_dataset))>0){
# data <- df_i1 %>% dplyr::select(c(year,!!this_dataset))
if(d==1){
fig <- plot_ly(df_i1, x = df_i1$year, y =df_i1[,this_dataset][[1]], name = this_dataset, type = 'scatter', mode = 'lines' )
}else{
fig <- fig %>% add_trace(y = df_i1[,this_dataset][[1]], name = paste0(this_dataset,"_",d), mode = 'lines')
}
}
}
}
fig
})
output$pie_ratio_catch<- renderPlotly({
# output$plot_species<- renderPlot({
df_i2 = data_pie_all_datasets() # %>% spread(dataset, value, fill=0)
if('global_nominal_catch_firms_level0' %in% unique(df_i2$dataset)){
total <- filter(df_i2, dataset=='global_nominal_catch_firms_level0')
total <- total$value
}else{total=1}
df_i2 = df_i2 %>% mutate(value = value/total) %>% subset(dataset!='global_nominal_catch_firms_level0')
fig <- plot_ly(df_i2, labels = ~dataset, values = ~value, type = 'pie',
# marker = list(colors = colors, line = list(color = '#FFFFFF', width = 1), sort = FALSE),
textposition = 'inside',
textinfo = 'label+percent',
showlegend = TRUE)
fig <- fig %>% layout(title = 'Ratio of all datasets for selected units',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig
})
output$pie_area_catch<- renderPlotly({
df_i2 = data_pie_area_catch() %>% mutate(unit=replace(unit,unit=='MT', 't')) # %>% filter(unit == 't') # %>% filter(dataset=='global_nominal_catch_firms_level0')
# df_i2 = st_read(con, query = paste0("SELECT dataset, area, unit, count(*), SUM(value) as value FROM (SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets WHERE ST_Within(geom,ST_GeomFromText(('POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'),4326)) AND species IN ('YFT') AND gear_group IN ('LL', 'PS', 'BB', 'OTHER', 'UNK') AND year IN ('1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019') AND area::varchar IN ('NA', '9101.3790835111', '825', '775', '750', '6995.49529425369', '6014.62349518009', '600', '525', '50', '450', '400', '375', '325', '3125', '3021.33388249682', '2750', '2625', '2612.4315513254', '2550', '250', '25', '2375', '2325', '2275', '2100', '200', '1425', '1350', '1300', '1250', '1125', '10040.35433829', '100', '1') ) AS foo GROUP BY dataset, area, unit ORDER BY dataset"))
# df_i2 <- df_i2 %>% mutate(unit=replace(unit,unit=='MT', 't')) # %>% filter(unit == 't')
if(length(unique(df_i2$unit))>1){
df_i2_t <- df_i2 %>% filter(unit == 't')
df_i2_no <- df_i2 %>% filter(unit == 'no')
if(switch_unit()){
df_i2 <- df_i2_no
}else{
df_i2 <- df_i2_t
}
}
row=c(0,0,1,1)
column=c(0,1,0,1)
if(length(unique(df_i2$dataset))>1){
fig <- plot_ly()
for(d in 1:length(unique(df_i2$dataset))){
cat(df_i2$dataset[d])
fig <- fig %>% add_pie(data = df_i2 %>% filter(dataset == unique(df_i2$dataset)[d]), labels = ~area, values = ~value,
name = paste0("Dataset : ",df_i2$dataset[d]), domain = list(row =row[d], column =column[d]))
}
fig <- fig %>% layout(title = "One pie chart showing the total catch by type of spatial objects (identifed by its area) for each dataset using selected unit(s)", showlegend = T,
grid=list(rows=2, columns=2),
xaxis = list(showgrid = TRUE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = TRUE, zeroline = FALSE, showticklabels = FALSE)) %>% add_annotations(x=row+0.5,
y=column+05,
text = unique(df_i2$dataset),
xref = "paper",
yref = "paper",
xanchor = "left",
showarrow = FALSE
)
}else{
fig <- plot_ly(df_i2, labels = ~area, values = ~value, type = 'pie',
# marker = list(colors = colors, line = list(color = '#FFFFFF', width = 1), sort = FALSE),
textposition = 'inside',
textinfo = 'label+percent',
showlegend = TRUE)
fig <- fig %>% layout(title = 'Pie Chart showing the total catch by type of spatial objects (identifed by its area) for the selected dataset and selected unit(s) (tons or numbers)',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
}
fig
# barplot_datasets <- ggplotly(fig, tooltip="text")
})
# st_read(con, query = paste0("SELECT dataset, area, unit, count(*), SUM(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset, area, unit ORDER BY dataset"))
# st_read(con, query = paste0("SELECT dataset, unit, count(*) AS count, SUM(value) AS value FROM (",sql_query(),") AS foo WHERE unit in ('t','MT','no') GROUP BY dataset, unit ORDER BY dataset"))
# output$barplot_datasets <- renderPlotly({
#
# df_i1 <- data_barplot_all_datasets()
# # df_i1 <- data_pie_area_catch()
# df_i1 <- df_i1 %>% filter(unit == 't') %>% dplyr::select(c(dataset, unit,value)) # %>% spread(dataset, value, fill=0)
#
# # df_i1$dataset <- factor(df_i1$dataset)
# # df_i1$unit <- factor(df_i1$unit)
#
# p <- ggplot(df_i1) + geom_bar(aes(x = dataset, stat=value, fill = unit))
# # geom_bar(aes(x = dataset, fill = factor(unit)), position = position_dodge(preserve = 'single'))
#
# # p <- ggplot(data=df_i1, aes(x = dataset, stat = value, fill = unit)) + geom_bar(stat = "identity", width = 1)
# p <- p + ggtitle("Catch by dataset") + xlab("") + ylab("Datasets") # Adds titles
# # p <- p + facet_grid(facets=. ~ dataset) # Side by side bar chart
# # p <- p + coord_polar(theta="y") # side by side pie chart
# #
#
# # Turn it interactive
# barplot_datasets <- ggplotly(p, tooltip="text")
#
#
# })
output$barplot_datasets <- renderPlotly({
# https://tutorials.cpsievert.me/20190821/#13
# https://stackoverflow.com/questions/55002248/plotly-stacked-bar-chart-add-trace-loop-issue
df_i1 <- data_barplot_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0)
# df_i1 <- st_read(con, query = paste0("SELECT dataset, unit, count(*)::decimal AS count, SUM(value) AS value FROM (SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets WHERE ST_Within(geom,ST_GeomFromText(('POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'),4326)) AND dataset IN ('global_catch_1deg_1m_ps_bb_firms_level0', 'global_catch_5deg_1m_firms_level0', 'global_catch_firms_level0', 'global_nominal_catch_firms_level0') AND species IN ('YFT') AND gear_group IN ('BB', 'LL', 'OTHER', 'PS', 'UNK') AND year IN ('1918', '1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019') AND area::varchar IN ('NA', '9101.3790835111', '825', '775', '750', '6995.49529425369', '6014.62349518009', '600', '525', '50', '450', '400', '375', '325', '3125', '3021.33388249682', '2750', '2625', '2612.4315513254', '2550', '250', '25', '2375', '2325', '2275', '2100', '200', '1425', '1350', '1300', '1250', '1125', '10040.35433829', '100', '1') AND unit IN ('MT', 'no', 't')) AS foo GROUP BY dataset, unit ORDER BY dataset")) %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0)
df_i1 <- as.data.frame(df_i1)
# df_i1 <- data_barplot_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% df_i1(id = rownames(.)) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0, -id) %>% plot_ly(x = ~id, y=~value, type="bar", color=~variable) %>% layout(barmode = "stack")
# mtcars %>% df_i1(id = rownames(.)) %>% gather(key = "variable",value = "value",-id) %>% plot_ly(x = ~id, y=~value, type="bar", color=~variable) %>% layout(barmode = "stack")
# fig <- plot_ly(data=df_i1)
# for(c in 2:length(colnames(df_i1))){
for(c in 1:length(colnames(dplyr::select(df_i1,-c(dataset))))){
this_column_name <- colnames(dplyr::select(df_i1,-c(dataset)))[c]
# df_i1$tmp <- dplyr::select(df_i1, c(dataset,!!this_column_name))
# this_df <- df_i1 %>% dplyr::select(c(dataset,!!this_column_name)) %>% rename(value=!!this_column_name)
if(c==1){
fig <- plot_ly(x = df_i1$dataset, y =df_i1[,this_column_name], type = 'bar', text=df_i1[,this_column_name], name = this_column_name, stroke = I("black"))
# fig <- plot_ly(df_i1, x = ~dataset, y =~value_no, type = 'bar', name = this_column_name)
}else{
fig <- fig %>% add_trace(y =df_i1[,this_column_name], text=df_i1[,this_column_name], name = this_column_name, stroke = I("black"))
# fig <- fig %>% add_trace(y =~count_t,type = 'bar', name = this_column_name)
# fig <- fig %>% add_trace(y = ~dplyr::select(df_i1_t, c(paste0(this_column,'_t'))), name = this_column)
}
}
fig <- fig %>% layout(title = "Total catch for selected datasets and units (weight or number of fish)",
xaxis = list(title = "Datasets"),
yaxis = list (title = "Count lines or catches in tons"))
fig
# fig <- plot_ly(df_i1, x = ~dataset, y = ~count, type = 'bar', name = 'Number of lines')
# fig <- fig %>% add_trace(y = ~value, name = 'Total catch (in tons)')
# fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group')
})
}
# Run the application
shinyApp(ui = ui, server = server)
juldebar/IRDTunaAtlas documentation built on March 9, 2024, 3:40 a.m.
R Package Documentation
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library(shiny)
library(geojsonio)
library(plyr)
library(dplyr)
library(DBI)
library(leaflet)
library(leaflet.extras)
library(leafpm)
library(mapedit)
library(sf)
library(sp)
library(plotly)
library(DT)
library(ncdf4)
library(raster)
library(maps)
library(ggplot2)
library(XML)
library(streamgraph)
library(viridis)
library(xts)
library(dygraphs)
library(tidyr)
####################################################################################################################################################################################################################################
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")
####################################################################################################################################################################################################################################
DRV=RPostgres::Postgres()
# source(file = "~/Desktop/CODES/IRDTunaAtlas/credentials.R")
source(file = "~/Bureau/CODES/IRDTunaAtlas/credentials.R")
###################################################################################################################################################################################################################################
new_wkt <- 'POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'
wkt <- reactiveVal(new_wkt)
switch_unit <- reactiveVal(TRUE)
query_metadata <- reactiveVal()
query_all_datasets <- reactiveVal()
# target_species<- c("YFT","BFT")
# target_year <- c(seq(1:10)+1994)
target_dataset <- dbGetQuery(con, "SELECT DISTINCT(dataset) FROM public.i1i2_spatial_all_datasets ORDER BY dataset;")
target_species <- dbGetQuery(con, "SELECT DISTINCT(species) FROM public.i1i2_spatial_all_datasets ORDER BY species;")
target_year <- dbGetQuery(con, "SELECT DISTINCT(year) FROM public.i1i2_spatial_all_datasets ORDER BY year;")
target_gear <- dbGetQuery(con, "SELECT DISTINCT(gear_group) as gear FROM public.i1i2_spatial_all_datasets ORDER BY gear_group;")
target_ocean <- dbGetQuery(con, "SELECT DISTINCT(ocean) as ocean FROM public.i1i2_spatial_all_datasets ORDER BY ocean;")
target_unit <- dbGetQuery(con, "SELECT DISTINCT(unit) AS unit FROM public.i1i2_spatial_all_datasets ORDER BY unit;")
target_area <- dbGetQuery(con, "SELECT DISTINCT(area)::varchar AS area FROM public.i1i2_spatial_all_datasets ORDER BY area DESC;")
default_species <- 'YFT'
default_year <- c(seq(min(target_year):max(target_year))+min(target_year)-1)
# default_gear <- c('BB','PS')
default_gear <- unique(target_gear)
# default_dataset <- 'global_catch_5deg_1m_firms_level0'
default_dataset <- unique(target_dataset$dataset)
default_unit <- c('MT','t','no')
# default_unit <- unique(target_unit$unit)
# default_area <- '25'
default_area <- unique(target_area$area)
#check what are existing / possible combinations between dimension values
filters_combinations <- dbGetQuery(con, "SELECT species, year, gear_group as gear FROM public.i1i2_spatial_all_datasets GROUP BY species, year,gear_group;")
ui <- fluidPage(
titlePanel("Tuna Atlas: spatial indicators (maps, charts, plots...)"),
navbarPage(title="TunaAtlas",
tabPanel("Interactive Indicator 11",
div(class="outer",
tags$head(includeCSS("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/styles.css")),
# leafletOutput('map_i11', width = "60%", height = 1500),
leafletOutput("mymap", width="100%", height="100%"),
absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 150, left = "3%", width = "21%", height = "auto",
selectInput(
inputId = "dataset",
label = "Dataset",
choices = target_dataset$dataset,
multiple = TRUE,
selected= default_dataset,
width = "99%"
),
selectInput(
inputId = "unit",
label = "Unit",
choices = target_unit$unit,
multiple = TRUE,
selected= default_unit,
width = "99%"
),
selectInput(
inputId = "area",
label = "Area",
choices = target_area$area,
multiple = TRUE,
selected= default_area,
width = "99%"
),
selectInput(
inputId = "species",
label = "Species",
choices = target_species$species,
multiple = TRUE,
selected= default_species,
width = "99%"
),
selectInput(
inputId = "year",
label = "Year",
choices = target_year$year,
multiple = TRUE,
selected= default_year,
width = "99%"
),
selectInput(
inputId = "gear",
label = "Gear",
choices = target_gear$gear,
multiple = TRUE,
selected= target_gear$gear,
width = "99%"
),
textInput("yourWKT","Paste you WKT",value=new_wkt),
verbatimTextOutput("value"),
actionButton(
inputId = "submit",
label = "Submit"
),
actionButton("resetWkt", "Reset WKT to global"),
tags$br()
),
absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 150, left = "auto", right="1%", width = "23%", height = "auto",
tags$br(),
plotlyOutput("pie_area_catch", width="100%"),
tags$br(),
actionButton(
inputId = "switched",
label = "Switch unit for pie chart"
),
plotlyOutput("barplot_datasets", width="100%"),
tags$br(),
plotlyOutput("pie_ratio_catch")
),
absolutePanel(id = "controls", class = "panel panel-default", bottom = "2%", left = "25%", width = "50%", fixed=TRUE, draggable = FALSE, height = "auto",
plotlyOutput("plotly_time_series_all_datasets")
# fluidRow(
# column(10,dygraphOutput("dygraph_all_datasets",height="400")),
# column(2,textOutput("legendDivID"))
# )
),
absolutePanel(id = "logo", class = "card", bottom = 15, left = 60, width = 80, fixed=TRUE, draggable = FALSE, height = "auto",
tags$a(href='https://www.ird.fr/', tags$img(src='https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/logo_IRD.svg',height='178',width='216'))
)
)
),
tabPanel(
title = "Plot indicator 2",
plotlyOutput("plot2")
),
tabPanel(
title = "Streamgraph indicator 2",
streamgraphOutput("plot2_streamgraph")
),
navbarMenu("Browse Data Tables",
tabPanel(
title = "Browse query_metadata",
DT::dataTableOutput("DT_query_metadata")
),
tabPanel(
title = "Browse data_all_datasets",
DT::dataTableOutput("DT_data_all_datasets")
),
tabPanel(
title = "Browse data_barplot_all_datasets",
DT::dataTableOutput("DT_data_barplot_all_datasets")
)
),
# tabPanel(
# title = "Your filters",
# textOutput("selected_var")
# ),
navbarMenu("Browse underlying SQL queries",
tabPanel(
title = "The main SQL query: sql_query: sql_query",
textOutput("sql_query")
),
tabPanel(
title = "SQL query: query_metadata",
textOutput("query_metadata")
),
tabPanel(
title = "SQL query: query_all_datasets",
textOutput("query_all_datasets")
)
),
tabPanel("About",
fluidRow(
column(6,
includeMarkdown("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/README.md")
),
column(6,
img(class="logo_IRD",
src=paste0("https://raw.githubusercontent.com/juldebar/IRDTunaAtlas/master/logo_IRD.svg")),
tags$small(
"Source: IRD",
"Julien Barde ",
"Funding : BlueCloud ",
a(href="https://www.documentation.ird.fr/hor/fdi:010012425",
"IRD Tuna Atlas (Alain Fontenau)"),
a(href="https://github.com/juldebar/IRDTunaAtlas/wiki/Indicator-I11-:-Catches-by-country",
"IRD Indicator 11"),
a(href="https://www.documentation.ird.fr/hor/fdi:010012425",
"IRD Tuna Atlas (Alain Fontenau)"),
a(href="https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers11-03/010012425.pdf",
"PDF")
)
)
)
)
)
)
server <- function(input, output, session) {
########################################################## Dynamic filters ##########################################################
change <- reactive({
unlist(strsplit(paste(c(input$species,input$year,input$gear),collapse="|"),"|",fixed=TRUE))
})
observeEvent(input$resetWkt, {
wkt(new_wkt)
})
observeEvent(input$species,{
temp <- filters_combinations %>% filter(species %in% change()[1])
updateSelectInput(session,"year",choices = unique(temp$year),selected=c(seq(min(temp$year):max(temp$year))+min(temp$year)-1))
updateSelectInput(session,"gear",choices = unique(temp$gear),selected=unique(temp$gear))
}
)
observeEvent(input$switched, {
if(switch_unit()){switch_unit(FALSE)}else{switch_unit(TRUE)}
})
# observeEvent(input$year,{
# temp <- filters_combinations %>% filter(species %in% change()[1], year %in% change()[2])
# updateSelectInput(session,"gear",choices = unique(temp$gear),selected=unique(temp$gear))
# }
# )
########################################################## DATA & SQL queries ##########################################################
#set the main parameterized query (options for geom might be st_collect(geom) or ST_ConvexHull(st_collect(geom)) as convexhull )
sql_query <- eventReactive(input$submit, {
if(is.null(input$year)){year_name=target_year$year}else{year_name=input$year}
# if(is.null(input$dataset)){dataset_name=target_dataset$dataset}else{year_name=input$dataset}
query <- glue::glue_sql(
"SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets
WHERE ST_Within(geom,ST_GeomFromText(({wkt*}),4326))
AND dataset IN ({dataset_name*})
AND species IN ({species_name*})
AND gear_group IN ({gear_group_name*})
AND year IN ({year_name*})
AND area::varchar IN ({area_name*})
AND unit IN ({unit_name*}) ",
wkt = wkt(),
dataset_name = input$dataset,
species_name = input$species,
gear_group_name = input$gear,
year_name = year_name,
unit_name = input$unit,
area_name = input$area,
.con = con)
},
ignoreNULL = FALSE)
metadata <- reactive({
query_metadata(paste0("SELECT geom, sum(value) AS value FROM(",sql_query(),") AS foo GROUP BY geom"))
st_read(con, query = query_metadata())
})
# unit, ocean, gear_group, year, species ;")
data_all_datasets <- eventReactive(input$submit, {
query_all_datasets(paste0("SELECT dataset, to_date(year::varchar(4),'YYYY') AS year, species, sum(value) as value, unit FROM (",sql_query(),") AS foo GROUP BY dataset, year, species, unit"))
st_read(con, query = query_all_datasets())
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_pie_all_datasets <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, sum(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_pie_area_catch <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, area, unit, count(*), SUM(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset, area, unit ORDER BY dataset"))
},
ignoreNULL = FALSE)
data_barplot_all_datasets <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, unit, count(*)::decimal AS count, SUM(value) AS value FROM (",sql_query(),") AS foo GROUP BY dataset, unit ORDER BY dataset"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_i1 <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT dataset, unit, ocean, to_date(year::varchar(4),'YYYY') AS year, species, sum(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset,unit, ocean, year, species"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
data_i2 <- eventReactive(input$submit, {
st_read(con, query = paste0("SELECT unit, gear_group AS gear_type, year, species, sum(value) as value FROM (",sql_query(),") AS foo GROUP BY unit, gear_group, year, species"))
},
# on.exit(dbDisconnect(conn), add = TRUE)
ignoreNULL = FALSE)
########################################################## Outputs: text & Data tables ##########################################################
output$value <- renderText({
wkt()
# output$value <- renderText({ input$caption })
})
output$sql_query <- renderText({
paste("Your SQL Query is : \n", sql_query())
})
output$query_metadata <- renderText({
paste("Your SQL Query is : \n", query_metadata())
})
output$query_all_datasets <- renderText({
paste("Your SQL Query is : \n", query_all_datasets())
})
# output$DT_data <- renderDT({
# data()
# })
output$DT_query_metadata <- renderDT({
metadata() %>% st_drop_geometry()
})
output$DT_data_all_datasets <- renderDT({
data_all_datasets()
})
output$DT_data_barplot_all_datasets <- renderDT({
# data_barplot_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0)
data_barplot_all_datasets()
})
output$selected_var <- renderText({
paste("You have selected:\n", input$species, "and \n", input$year, "and \n", input$flag, "and \n", wkt())
})
########################################################## Outputs: maps / plots / charts ##########################################################
output$mymap <- renderLeaflet({
df <- metadata()
centroid <- st_convex_hull(df) %>% st_centroid()
lat_centroid <- st_coordinates(centroid)[2]
lon_centroid <- st_coordinates(centroid)[1]
pal <- colorNumeric(
palette = "YlGnBu",
domain = df$value
)
# brewer.pal(7, "OrRd")
pal_fun <- colorQuantile( "YlOrRd", NULL, n = 10)
qpal <- colorQuantile(rev(viridis::viridis(10)),
df$value, n=10)
# https://r-spatial.github.io/sf/articles/sf5.html
map_leaflet <- leaflet() %>%
setView(lng = lon_centroid, lat =lat_centroid, zoom = 3
) %>%
clearBounds() %>%
addProviderTiles("Esri.OceanBasemap") %>%
addPolygons(data = df,
label = ~value,
popup = ~paste0("Captures pour cette espece: ", round(value), " tonnes(t) et des brouettes"),
# fillColor = ~pal_fun(value),
fillColor = ~qpal(value),
fill = TRUE,
fillOpacity = 0.8,
smoothFactor = 0.5
# color = ~pal(value)
) %>%
addDrawToolbar(
targetGroup = "draw",
editOptions = editToolbarOptions(
selectedPathOptions = selectedPathOptions()
)
) %>%
addLayersControl(
overlayGroups = c("draw"),
options = layersControlOptions(collapsed = FALSE)
) %>%
leaflet::addLegend("bottomright", pal = qpal, values = df$value,
title = "Total catch per cell for selected criteria",
labFormat = labelFormat(prefix = "MT "),
opacity = 1
)
})
observe({
#use the draw_stop event to detect when users finished drawing
feature <- input$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$geometry
# drawn_polygon <- Polygon(do.call(rbind,lapply(polygon_coordinates,function(x){c(x[[1]][1],x[[2]][1])})))
geoJson <- geojsonio::as.json(feature)
# spdf <- geojsonio::geojson_sp(feature)
geom <- st_read(geoJson)
wkt(st_as_text(st_geometry(geom[1,])))
coord <- st_as_text(st_geometry(geom[1,]))
north <- polygon_coordinates[[1]][[1]]
south <- polygon_coordinates[[2]][[1]]
east <- polygon_coordinates[[1]][[2]]
west <- polygon_coordinates[[2]][[2]]
if(is.null(polygon_coordinates))
return()
text<-paste("North ", north, "South ", east)
mymap_proxy = leafletProxy("mymap") %>% clearPopups() %>% addPopups(south,west,coord)
textOutput("wkt")
})
output$plot2 <- renderPlotly({
# df_i2 = data() %>% filter(year %in% input$year) %>% filter(gear_group %in% input$gear) %>% group_by(year,gear_group,species) %>% summarise(value = sum(value)) %>% dplyr::rename(gear_type=gear_group)
df_i2 <- data_i2()
i2 <- Atlas_i2_SpeciesByGear(as.data.frame(df_i2),
yearAttributeName="year",
speciesAttributeName="species",
valueAttributeName="value",
gearTypeAttributeName="gear_type",
withSparql=FALSE)
i2
})
output$plot2_streamgraph<- renderStreamgraph({
df_i2 = data_i2() %>%
streamgraph("gear_type", "value", "year", offset="zero", interpolate="step") %>%
sg_axis_x(1, "year", "%Y") %>%
sg_fill_brewer("PuOr") %>%
sg_legend(show=TRUE, label="I=RFMO - names: ")
})
output$dygraph_all_datasets <- renderDygraph({
# df_i1 = data_all_datasets() %>% filter(unit %in% c('t','MT')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
# df_i1 = data_all_datasets() %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 = data_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 <- as_tibble(df_i1) # %>% top_n(3)
tuna_catches_timeSeries <-NULL
if(length(unique(df_i1$unit))>1){
df_i1_t <- df_i1 %>% filter(unit == 't')
df_i1_no <- df_i1 %>% filter(unit == 'no')
# if(switch_unit()){
# df_i1 <- df_i1_no
# }else{
# df_i1 <- df_i1_t
# }
# if(length(colnames(dplyr::select(df_i1_t,-c(species,year,unit))))>1){
for(d in 1:length(colnames(dplyr::select(df_i1_t,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1_t,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_t, this_dataset))>0){
df_i1_t <- df_i1_t %>% dplyr::rename(!!paste0(this_dataset,'_t') := !!this_dataset)
this_time_serie <- xts(x = dplyr::select(df_i1_t, c(paste0(this_dataset,'_t'))), order.by = df_i1_t$year)
if(d==1){tuna_catches_timeSeries <- this_time_serie}else{
tuna_catches_timeSeries <- cbind(tuna_catches_timeSeries, this_time_serie)
}
}
}
# if(length(colnames(dplyr::select(df_i1_no,-c(species,year,unit))))>1){
for(d in 1:length(colnames(dplyr::select(df_i1_no,-c(species,year,unit))))){
this_dataset <- colnames(dplyr::select(df_i1_no,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_no,this_dataset))>0){
df_i1_no <- df_i1_no %>% dplyr::rename(!!paste0(this_dataset,'_no') := !!this_dataset)
this_time_serie <- xts(x = dplyr::select(df_i1_no, c(paste0(this_dataset,'_no'))), order.by = df_i1_no$year)
tuna_catches_timeSeries <- cbind(tuna_catches_timeSeries, this_time_serie)
}
}
}else{
for(d in 1:length(colnames(dplyr::select(df_i1,-c(species,year,unit))))){
this_dataset <- colnames(dplyr::select(df_i1,-c(species,year,unit)))[d]
this_time_serie <- xts(x = dplyr::select(df_i1, c(this_dataset)), order.by = df_i1$year)
if(d==1){tuna_catches_timeSeries <- this_time_serie}else{
tuna_catches_timeSeries <- cbind(tuna_catches_timeSeries, this_time_serie)
}
}
# create the area chart
# g1 <- dygraph(tuna_catches_timeSeries) # %>% dyOptions( fillGraph=TRUE )
# g1 <- dygraph(tuna_catches_timeSeries, main = "Catches by ocean") %>% dyRangeSelector() %>% dyLegend(labelsDiv = "legendDivID")
# %>%
# dyStackedBarGroup(c('global_catch_5deg_1m_firms_level0', 'global_catch_1deg_1m_ps_bb_firms_level0','spatial','nominal'))
# >% dyOptions( fillGraph=TRUE) %>% # create bar chart with the passed dygraph
# dyOptions(stackedGraph = stack())
# dySeries(iotc, label = "iotc") %>%
# dySeries(iattc, label = "iattc") %>%
# dySeries(iccat, label = "iccat")
#
}
g1 <- dygraph(tuna_catches_timeSeries, main = "Times series of values with selected units (tons or numbers) for the selected datasets") %>% dyRangeSelector() %>% dyLegend(labelsDiv = "legendDivID", labelsSeparateLines = T)
})
output$plotly_time_series_all_datasets <- renderPlotly({
# df_i1 = st_read(con, query = "SELECT dataset, to_date(year::varchar(4),'YYYY') AS year, species, sum(value) as value, unit FROM (SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets WHERE ST_Within(geom,ST_GeomFromText(('POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'),4326)) AND dataset IN ('global_catch_1deg_1m_ps_bb_firms_level0', 'global_catch_5deg_1m_firms_level0', 'global_catch_firms_level0', 'global_nominal_catch_firms_level0') AND species IN ('YFT') AND gear_group IN ('BB', 'LL', 'OTHER', 'PS', 'UNK') AND year IN ('1918', '1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019') AND area::varchar IN ('NA', '9101.3790835111', '825', '775', '750', '6995.49529425369', '6014.62349518009', '600', '525', '50', '450', '400', '375', '325', '3125', '3021.33388249682', '2750', '2625', '2612.4315513254', '2550', '250', '25', '2375', '2325', '2275', '2100', '200', '1425', '1350', '1300', '1250', '1125', '10040.35433829', '100', '1') AND unit IN ('MT', 'no', 't') ) AS foo GROUP BY dataset, year, species, unit") %>%
# mutate(unit=replace(unit,unit=='MT', 't')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 = data_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% spread(dataset, value, fill=0) # %>% mutate(total = rowSums(across(any_of(as.vector(target_ocean$ocean)))))
df_i1 <- as_tibble(df_i1) # %>% top_n(3)
if(length(unique(df_i1$unit))>1){
df_i1_t <- df_i1 %>% filter(unit == 't')
df_i1_no <- df_i1 %>% filter(unit == 'no')
for(d in 1:length(colnames(dplyr::select(df_i1_t,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1_t,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_t, this_dataset))>0){
# data <- df_i1_t %>% dplyr::select(c(year,!!this_dataset))
if(d==1){
fig <- plot_ly(df_i1_t, x = df_i1_t$year, y =df_i1_t[,this_dataset][[1]], name = paste0(this_dataset,"_",d), type = 'scatter', mode = 'lines' )
}else{
fig <- fig %>% add_trace(y = df_i1_t[,this_dataset][[1]], name = paste0(this_dataset,"_t"), mode = 'lines')
}
}
}
for(d in 1:length(colnames(dplyr::select(df_i1_no,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1_no,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1_no, this_dataset))>0){
fig <- fig %>% add_trace(x = df_i1_no$year, y = df_i1_no[,this_dataset][[1]], name = paste0(this_dataset,"_no"), mode = 'lines')
}
}
}else{
for(d in 1:length(colnames(dplyr::select(df_i1,-c(species,year,unit))))){
this_dataset <-colnames(dplyr::select(df_i1,-c(species,year,unit)))[d]
if(sum(dplyr::select(df_i1, this_dataset))>0){
# data <- df_i1 %>% dplyr::select(c(year,!!this_dataset))
if(d==1){
fig <- plot_ly(df_i1, x = df_i1$year, y =df_i1[,this_dataset][[1]], name = this_dataset, type = 'scatter', mode = 'lines' )
}else{
fig <- fig %>% add_trace(y = df_i1[,this_dataset][[1]], name = paste0(this_dataset,"_",d), mode = 'lines')
}
}
}
}
fig
})
output$pie_ratio_catch<- renderPlotly({
# output$plot_species<- renderPlot({
df_i2 = data_pie_all_datasets() # %>% spread(dataset, value, fill=0)
if('global_nominal_catch_firms_level0' %in% unique(df_i2$dataset)){
total <- filter(df_i2, dataset=='global_nominal_catch_firms_level0')
total <- total$value
}else{total=1}
df_i2 = df_i2 %>% mutate(value = value/total) %>% subset(dataset!='global_nominal_catch_firms_level0')
fig <- plot_ly(df_i2, labels = ~dataset, values = ~value, type = 'pie',
# marker = list(colors = colors, line = list(color = '#FFFFFF', width = 1), sort = FALSE),
textposition = 'inside',
textinfo = 'label+percent',
showlegend = TRUE)
fig <- fig %>% layout(title = 'Ratio of all datasets for selected units',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
fig
})
output$pie_area_catch<- renderPlotly({
df_i2 = data_pie_area_catch() %>% mutate(unit=replace(unit,unit=='MT', 't')) # %>% filter(unit == 't') # %>% filter(dataset=='global_nominal_catch_firms_level0')
# df_i2 = st_read(con, query = paste0("SELECT dataset, area, unit, count(*), SUM(value) as value FROM (SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets WHERE ST_Within(geom,ST_GeomFromText(('POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'),4326)) AND species IN ('YFT') AND gear_group IN ('LL', 'PS', 'BB', 'OTHER', 'UNK') AND year IN ('1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019') AND area::varchar IN ('NA', '9101.3790835111', '825', '775', '750', '6995.49529425369', '6014.62349518009', '600', '525', '50', '450', '400', '375', '325', '3125', '3021.33388249682', '2750', '2625', '2612.4315513254', '2550', '250', '25', '2375', '2325', '2275', '2100', '200', '1425', '1350', '1300', '1250', '1125', '10040.35433829', '100', '1') ) AS foo GROUP BY dataset, area, unit ORDER BY dataset"))
# df_i2 <- df_i2 %>% mutate(unit=replace(unit,unit=='MT', 't')) # %>% filter(unit == 't')
if(length(unique(df_i2$unit))>1){
df_i2_t <- df_i2 %>% filter(unit == 't')
df_i2_no <- df_i2 %>% filter(unit == 'no')
if(switch_unit()){
df_i2 <- df_i2_no
}else{
df_i2 <- df_i2_t
}
}
row=c(0,0,1,1)
column=c(0,1,0,1)
if(length(unique(df_i2$dataset))>1){
fig <- plot_ly()
for(d in 1:length(unique(df_i2$dataset))){
cat(df_i2$dataset[d])
fig <- fig %>% add_pie(data = df_i2 %>% filter(dataset == unique(df_i2$dataset)[d]), labels = ~area, values = ~value,
name = paste0("Dataset : ",df_i2$dataset[d]), domain = list(row =row[d], column =column[d]))
}
fig <- fig %>% layout(title = "One pie chart showing the total catch by type of spatial objects (identifed by its area) for each dataset using selected unit(s)", showlegend = T,
grid=list(rows=2, columns=2),
xaxis = list(showgrid = TRUE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = TRUE, zeroline = FALSE, showticklabels = FALSE)) %>% add_annotations(x=row+0.5,
y=column+05,
text = unique(df_i2$dataset),
xref = "paper",
yref = "paper",
xanchor = "left",
showarrow = FALSE
)
}else{
fig <- plot_ly(df_i2, labels = ~area, values = ~value, type = 'pie',
# marker = list(colors = colors, line = list(color = '#FFFFFF', width = 1), sort = FALSE),
textposition = 'inside',
textinfo = 'label+percent',
showlegend = TRUE)
fig <- fig %>% layout(title = 'Pie Chart showing the total catch by type of spatial objects (identifed by its area) for the selected dataset and selected unit(s) (tons or numbers)',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
}
fig
# barplot_datasets <- ggplotly(fig, tooltip="text")
})
# st_read(con, query = paste0("SELECT dataset, area, unit, count(*), SUM(value) as value FROM (",sql_query(),") AS foo GROUP BY dataset, area, unit ORDER BY dataset"))
# st_read(con, query = paste0("SELECT dataset, unit, count(*) AS count, SUM(value) AS value FROM (",sql_query(),") AS foo WHERE unit in ('t','MT','no') GROUP BY dataset, unit ORDER BY dataset"))
# output$barplot_datasets <- renderPlotly({
#
# df_i1 <- data_barplot_all_datasets()
# # df_i1 <- data_pie_area_catch()
# df_i1 <- df_i1 %>% filter(unit == 't') %>% dplyr::select(c(dataset, unit,value)) # %>% spread(dataset, value, fill=0)
#
# # df_i1$dataset <- factor(df_i1$dataset)
# # df_i1$unit <- factor(df_i1$unit)
#
# p <- ggplot(df_i1) + geom_bar(aes(x = dataset, stat=value, fill = unit))
# # geom_bar(aes(x = dataset, fill = factor(unit)), position = position_dodge(preserve = 'single'))
#
# # p <- ggplot(data=df_i1, aes(x = dataset, stat = value, fill = unit)) + geom_bar(stat = "identity", width = 1)
# p <- p + ggtitle("Catch by dataset") + xlab("") + ylab("Datasets") # Adds titles
# # p <- p + facet_grid(facets=. ~ dataset) # Side by side bar chart
# # p <- p + coord_polar(theta="y") # side by side pie chart
# #
#
# # Turn it interactive
# barplot_datasets <- ggplotly(p, tooltip="text")
#
#
# })
output$barplot_datasets <- renderPlotly({
# https://tutorials.cpsievert.me/20190821/#13
# https://stackoverflow.com/questions/55002248/plotly-stacked-bar-chart-add-trace-loop-issue
df_i1 <- data_barplot_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0)
# df_i1 <- st_read(con, query = paste0("SELECT dataset, unit, count(*)::decimal AS count, SUM(value) AS value FROM (SELECT dataset, unit, ocean, gear_group, year, species, value, area, geom FROM public.i1i2_spatial_all_datasets WHERE ST_Within(geom,ST_GeomFromText(('POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))'),4326)) AND dataset IN ('global_catch_1deg_1m_ps_bb_firms_level0', 'global_catch_5deg_1m_firms_level0', 'global_catch_firms_level0', 'global_nominal_catch_firms_level0') AND species IN ('YFT') AND gear_group IN ('BB', 'LL', 'OTHER', 'PS', 'UNK') AND year IN ('1918', '1919', '1920', '1921', '1922', '1923', '1924', '1925', '1926', '1927', '1928', '1929', '1930', '1931', '1932', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1941', '1942', '1943', '1944', '1945', '1946', '1947', '1948', '1949', '1950', '1951', '1952', '1953', '1954', '1955', '1956', '1957', '1958', '1959', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019') AND area::varchar IN ('NA', '9101.3790835111', '825', '775', '750', '6995.49529425369', '6014.62349518009', '600', '525', '50', '450', '400', '375', '325', '3125', '3021.33388249682', '2750', '2625', '2612.4315513254', '2550', '250', '25', '2375', '2325', '2275', '2100', '200', '1425', '1350', '1300', '1250', '1125', '10040.35433829', '100', '1') AND unit IN ('MT', 'no', 't')) AS foo GROUP BY dataset, unit ORDER BY dataset")) %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0)
df_i1 <- as.data.frame(df_i1)
# df_i1 <- data_barplot_all_datasets() %>% mutate(unit=replace(unit,unit=='MT', 't')) %>% df_i1(id = rownames(.)) %>% pivot_wider(names_from = unit, values_from = c("value", "count"), names_sep="_",values_fill = 0, -id) %>% plot_ly(x = ~id, y=~value, type="bar", color=~variable) %>% layout(barmode = "stack")
# mtcars %>% df_i1(id = rownames(.)) %>% gather(key = "variable",value = "value",-id) %>% plot_ly(x = ~id, y=~value, type="bar", color=~variable) %>% layout(barmode = "stack")
# fig <- plot_ly(data=df_i1)
# for(c in 2:length(colnames(df_i1))){
for(c in 1:length(colnames(dplyr::select(df_i1,-c(dataset))))){
this_column_name <- colnames(dplyr::select(df_i1,-c(dataset)))[c]
# df_i1$tmp <- dplyr::select(df_i1, c(dataset,!!this_column_name))
# this_df <- df_i1 %>% dplyr::select(c(dataset,!!this_column_name)) %>% rename(value=!!this_column_name)
if(c==1){
fig <- plot_ly(x = df_i1$dataset, y =df_i1[,this_column_name], type = 'bar', text=df_i1[,this_column_name], name = this_column_name, stroke = I("black"))
# fig <- plot_ly(df_i1, x = ~dataset, y =~value_no, type = 'bar', name = this_column_name)
}else{
fig <- fig %>% add_trace(y =df_i1[,this_column_name], text=df_i1[,this_column_name], name = this_column_name, stroke = I("black"))
# fig <- fig %>% add_trace(y =~count_t,type = 'bar', name = this_column_name)
# fig <- fig %>% add_trace(y = ~dplyr::select(df_i1_t, c(paste0(this_column,'_t'))), name = this_column)
}
}
fig <- fig %>% layout(title = "Total catch for selected datasets and units (weight or number of fish)",
xaxis = list(title = "Datasets"),
yaxis = list (title = "Count lines or catches in tons"))
fig
# fig <- plot_ly(df_i1, x = ~dataset, y = ~count, type = 'bar', name = 'Number of lines')
# fig <- fig %>% add_trace(y = ~value, name = 'Total catch (in tons)')
# fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group')
})
}
# Run the application
shinyApp(ui = ui, server = server)
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