In andreasnovotny/shaRk: shaRk: A tidyverse approach to Swedish natiolal monitoring data

# load data in 'global' chunk so it can be shared by all users of the dashboard
library(shiny)
library(tidyverse)
library(shaRk)

appDir <- system.file(package="shaRk")

zooplankton <- readRDS(file.path(appDir,"zoo.rds")) %>% 
  addDyntaxa() %>%
  annotateSHARK()

phytoplankton <- readRDS(file.path(appDir,"phyto.rds")) %>% 
  addDyntaxa() %>%
  annotateSHARK()

graph_theme<- theme(axis.text.y = element_text(size=12),
                    axis.text.x = element_text(face='plain',
                                               size=12),
                    axis.title.y = element_text(size=14),
                    axis.title.x = element_text(face='plain',
                                                size=10,
                                                vjust=-0.1,
                                                hjust=0.6),
                    legend.text = element_text(size = 14))

Monthly Means {data-icon="fa-chart-line"}

Column {.sidebar}

MONTHLY MEANS

Select your settings:

Universal

textInput("station", "Write the name of the station:", value="BY5 BORNHOLMSDJ")
#selectInput("depth", "Select depth of interest", choices = unique(zooplankton$Depth))

Zooplankton

selectInput(inputId = "zoounit", label = "Unit on x-axis:", choices = unique(zooplankton$unit))
selectInput("zootaxa", "Select taxa of interest:", choices = unique(zooplankton$Genus), multiple = TRUE)

Phytoplankton

selectInput(inputId = "phytounit", label = "Unit on x-axis:", choices = unique(phytoplankton$unit))
selectInput("phytotaxa", "Select taxa of interest:", choices = unique(phytoplankton$Genus), multiple = TRUE)

Andreas Novotny Stockholm University [e-mail]

Column

Zooplankton

renderPlot({
  subset <- zooplankton %>%
    filter(unit==input$zoounit,
           #Depth==30,
           dev_stage_code!="NP",
           Station==input$station,
           Genus %in% input$zootaxa) %>%
    group_by(Year, Month, Day, Genus, Station, Depth) %>% 
    summarise(Value=sum(Value)) %>% 
    group_by(Month, Genus, Station) %>%
    summarise(Value=mean(Value))

  ggplot(subset, aes(x = Month, y = Value, group = Genus)) +
    geom_line(aes(colour=Genus)) +
    scale_x_continuous('Month',breaks=seq(1,12,by=1),limits=c(1,12))+ # rescale Y
    scale_y_continuous(paste(input$zoounit))+ 
    theme_bw() +
    graph_theme

})

Phytoplankton

renderPlot({
  subset <- phytoplankton %>%
    filter(unit==input$phytounit,
           #Depth==30,
           Station==input$station,
           Genus %in% input$phytotaxa) %>%
    group_by(Year, Month, Day, Genus, Station, Depth) %>% 
    summarise(Value=sum(Value)) %>% 
    group_by(Month, Genus, Station) %>%
    summarise(Value=mean(Value))

  ggplot(subset, aes(x = Month, y = Value, group = Genus)) +
    geom_line(aes(colour=Genus)) +
    scale_x_continuous('Month',breaks=seq(1,12,by=1),limits=c(1,12))+ # rescale Y
    scale_y_continuous(paste(input$phytounit))+ 
    theme_bw() +
    graph_theme

})

phytoplankton$station

Station Map {data-icon="fa-globe"}

Column

Column

Available stations are:

as.data.frame(unique(zooplankton$station_name))

andreasnovotny/shaRk documentation built on Feb. 12, 2023, 7:06 p.m.