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app.R
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###########################################################################
### Soil GreenHouseGas Flux Visualisation and calculation tool ###
###########################################################################
## Author: Roman Hüppi
## Date : Feb. 2020
## Version: 1.1 (sanitize error off)
# libraries ---------------------------------------------------------------
library(shiny)
library(ggplot2)
# library(ggthemes)
library(plotly)
library(data.table)
library(DT)
library(gasfluxes)
library(ggsignif)
source("wrap-gasfluxes.R")
options(shiny.sanitize.errors = FALSE)
# Define UI for application
ui <- fluidPage(
img(src = "ETH_logo.jpg", height = 70, width = 200, align = "right"),
# Application title
titlePanel("Soil GHG Flux shiny tool"),
# Sidebar layout with a input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
radioButtons("ghgtask", "Select a task:",
choices = c("Visualise flux dataset" = "fluxvisual", # eval(c(expression("N"[2]*"O"))
"Calculate gasfluxes" = "gasfluxes"),
# "Cumulative fluxes" = "cumflux"),
selected = 'fluxvisual'),
radioButtons("gas.species", "GHG of your interest",
choices = c("Nitrous oxide" = "N2O", # eval(c(expression("N"[2]*"O"))
"Carbon dioxide" = "CO2",
"Methane" = "CH4"),
selected = 'N2O'),
radioButtons("separator","Input file separator", c("Comma" = ",","Semicolon" = ";"),
selected = ",", inline=T),
# Input: Select a file ----
fileInput("read.input", "Upload input file:",
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
conditionalPanel(
condition = "input.ghgtask == 'gasfluxes'",
numericInput("f.detect", "Enter the minimal detectable flux:", 0.02, min = 0.001, max = 1.1),
helpText("Use the 'minflxlim' shiny app to calculate f.detect:",
"https://sae-interactive-data.ethz.ch/minflxlim/"),
actionButton("gasfluxes.go", "Estimate gasfluxes")
),
# Horizontal line ----
tags$hr(),
# h4("Diverfarming's Dataset"),
#
# checkboxGroupInput("site","select Case Study sites:", c("CS09 - Trier DE" = "CS9",
# "CS10 - Jakobsalash HU" = "CS10",
# "CS11 - Villany HU" = "CS11"),
# # ghg.file$site[1] = ,
# selected = "CS10")
# ),
h4("Visualisation options"),
uiOutput("siteInput"),
# })
# tags$hr(),
selectInput("group.var",
label = "Grouping variable",
choices = c("Treatment factor 1",
"Treatment factor 2",
"Treatment factor 3",
"Chamber number",
"Block",
"Sub factor"),
selected = "Treatment factor 1"),
# # Input: Select quotes ----
radioButtons("reg.method", "Choose a flux calculation method",
choices = c("dynamic kappa.max HMR" = "dynamic.kappa.f0",
"robust linear" = "robust.linear.f0",
"non-linear - HMR" = "HMR.f0"),
selected = 'dynamic.kappa.f0'),
checkboxGroupInput("aggflux","Flux aggregation", c("none" = "no",
"cumulative" = "cumsum",
"aggregated" = "aggflx"),inline=T,
selected = "no")
), # end sidebarPanel
# Main panel for displaying outputs ----
mainPanel(
tabsetPanel(
tabPanel(p(icon("bar-chart"), "Timeline plots"),
plotlyOutput("timelinePlot"),
plotlyOutput("cumPlot"),
plotOutput("aggPlot", width = "60%")),
# Data
tabPanel(p(icon("table"), "Plot Data"),
# dataTableOutput(outputId="dTable")
# verbatimTextOutput("value"),
downloadButton("downloadData", "Download"),
DT::dataTableOutput("plot.table")
), #, end of "Dataset" tab panel
# conditionalPanel(
# condition = "input.ghgtask == 'gasfluxes'",
tabPanel(p(icon("line-chart"), "Chamber details"),
# selectInput("gasflux.pic",
# label = "Select an individual chamber flux:",
# choices = c("Treatment",
# "Chamber",
# "Block",
# "Position"),
# selected = "Treatment"),
plotOutput("pics"),
# plotOutput("pics"),
br(),
downloadButton("downloadGasfluxes", "Download"),
verbatimTextOutput("value"),
br(),
DT::dataTableOutput("plot.table2")
),
# ),
tabPanel(p(icon("question-circle"), "Help"),
includeMarkdown("help.Rmd")
) ## maybe not "hahahah"
# fluidRow(column(3, verbatimTextOutput("value")))
) # end of tab panel
) # end mainPanel
), # end sidebarLayout
img(src = "sae.png", height = 48, align = "left"),
img(src = "DiverFarming_logo.jpg", height = 150, align = "right")
) # end fluidPage
# Define server logic to read selected file ----
server <- function(input, output) {
# ghg.file <- reactive({
# data <- fread(paste0("data/gasflux-",input$gas.species,"_DiverFarming.csv"),sep = ";")
# return(data)
# })
output$siteInput <- renderUI({
if(is.null(input$read.input$datapath) == TRUE) {
ghg.file <- fread(paste0("data/gasflux-",input$gas.species,"_DiverFarming.csv")) # , sep = input$separator
} else {
ghg.file <- fread(input$read.input$datapath,
header = T,
sep = input$separator)
}
sites <- unique(ghg.file$site)
checkboxGroupInput("sites", "Choose sites", sites, selected = ghg.file$site[2], inline=T)
})
# get.flux.data <- reactive({
get.flux.data <- function(){
if (input$ghgtask == "fluxvisual" & is.null(input$read.input$datapath) == TRUE){ #
ghg.file <- fread(paste0("data/gasflux-",input$gas.species,"_DiverFarming.csv")) #, sep = input$separator)
# tryCatch(
# {
# ghg.file[ ,date.strp := as.POSIXct(date,format = "%Y-%m-%d")]
# # ghg.file <- ghg.file[is.finite(linear.f0),]
# ghg.file[is.na(robust.linear.f0), robust.linear.f0 := linear.f0] # use linear for if only 3 datapoints are available
# ghg.file[is.na(HMR.f0), HMR.f0 := robust.linear.f0] # use robust linear if HMR could not be fitted
#
# ghg.file[, block := as.factor(block)] # blocks as factors
# ghg.file[, chamber.nr := as.factor(chamber.nr)] # chamber number as factor
# ghg.file[, sub_factor := as.factor(sub_factor)] # sub_factor as factor
# })
# ghg.file
}
if(is.null(input$read.input$datapath) == FALSE){ # == FALSE & input$ghgtask == "gasfluxes"
# tryCatch(
# {
ghg.file <- fread(input$read.input$datapath,
header = T,
sep = input$separator
# quote = input$quote
)
} # end if
# else {
# stop()
# }
return(ghg.file)
} # end get.flux.data()
get.plot.data <- reactive({
ghg.file <- get.flux.data()
if (input$ghgtask == "gasfluxes" & is.null(input$read.input$datapath) == FALSE) { #
ghg.file <- gasfluxes.get()
}
tryCatch(
{
ghg.file[ ,date.strp := as.POSIXct(date,format = "%Y-%m-%d")]
# ghg.file <- ghg.file[is.finite(linear.f0),]
ghg.file[is.na(robust.linear.f0), robust.linear.f0 := linear.f0] # use linear for if only 3 datapoints are available
ghg.file[is.na(HMR.f0), HMR.f0 := robust.linear.f0] # use robust linear if HMR could not be fitted
# browser()
ghg.file[, block := as.factor(block)] # blocks as factors
ghg.file[, chamber.nr := as.factor(chamber.nr)] # chamber number as factor
ghg.file[, sub_factor := as.factor(sub_factor)] # sub_factor as factor
})
ghg.file.melt <- melt(ghg.file, id.vars = c("date.strp","site","chamber.nr","treatment_fact1","block","treatment_fact2","treatment_fact3","sub_factor"),
measure.vars = input$reg.method, value.name = "N2O.flux", variable.name = "method")
group.var2 <- switch(input$group.var,
"Treatment factor 1" = "treatment_fact1",
"Treatment factor 2" = "treatment_fact2",
"Treatment factor 3" = "treatment_fact3",
"Chamber number" = "chamber.nr" ,
"Block" = "block",
"Further factor" = "sub_factor")
#
# ghg.file.melted <- ghg.file.melt[site %in% input$site]
ghg.file.melted <- ghg.file.melt[site %in% input$sites]
setkey(ghg.file.melted, date.strp)
# if (input$aggflux == "cumsum") {ghg.file.melted$N2O.flux <- cumsum(ghg.file.melted$N2O.flux) }
ghg.file.plot <- ghg.file.melted[, .(mean = mean(N2O.flux), # site == select.site
sd = sd(N2O.flux,na.rm = TRUE),
n = length(N2O.flux)),
# , se = sd/sqrt(n)),
by = c("date.strp","site","method", group.var2)]
ghg.file.plot[, se := sd/sqrt(n)]
ghg.file.plot[, cums := cumsum(mean), group.var2]
ghg.file.plotsd <- ghg.file.melted[, .(mean = mean(N2O.flux), # site == select.site
# sd = sd(N2O.flux,na.rm = TRUE),
n = length(N2O.flux)),
# , se = sd/sqrt(n)),
by = c("date.strp","site","method","chamber.nr", "treatment_fact1", "treatment_fact2",
"treatment_fact3", "block","sub_factor")]
ghg.file.plotsd[, cums := cumsum(mean), "chamber.nr"]
ghg.file.plotsd[, agg.flx := agg.fluxes(mean,date.strp, timeunit = "hours"), by = "chamber.nr"]
ghg.file.plot$cums.sd <- ghg.file.plotsd[, .(cums.sd = sd(cums)), by = c("date.strp","site","method", group.var2)]$cums.sd # plugs in data for aggregation back in the the original dataframe
ghg.file.plot[, cums.se := cums.sd/sqrt(n)]
ghg.file.plot[, agg.flx := agg.fluxes(mean,date.strp, timeunit = "hours"), by = group.var2] # use gasflux agg.fluxes function to aggregate daily fluxes
return(ghg.file.plot)
# })
})
get.agg.data <- function(){
ghg.file <- get.flux.data()
if (input$ghgtask == "gasfluxes" & is.null(input$read.input$datapath) == FALSE) { #
ghg.file <- gasfluxes.get()
} # if(is.null(ghg.file$date.strp) == TRUE){
ghg.file[ ,date.strp := as.POSIXct(date,format = "%Y-%m-%d")]
# ghg.file <- ghg.file[is.finite(linear.f0),]
ghg.file[is.na(robust.linear.f0), robust.linear.f0 := linear.f0] # use linear for if only 3 datapoints are available
ghg.file[is.na(HMR.f0), HMR.f0 := robust.linear.f0] # use robust linear if HMR could not be fitted
ghg.file[, block := as.factor(block)] # blocks as factors
ghg.file[, chamber.nr := as.factor(chamber.nr)] # chamber number as factor
ghg.file[, sub_factor := as.factor(sub_factor)] # sub_factor as factor
# }
ghg.file.melt <- melt(ghg.file, id.vars = c("date.strp","site","chamber.nr","treatment_fact1","block","treatment_fact2","treatment_fact3","sub_factor"),
measure.vars = input$reg.method, value.name = "N2O.flux", variable.name = "method")
ghg.file.melted <- ghg.file.melt[site %in% input$sites]
setkey(ghg.file.melted, date.strp)
ghg.file.plotsd <- ghg.file.melted[, .(mean = mean(N2O.flux), # site == select.site
# sd = sd(N2O.flux,na.rm = TRUE),
n = length(N2O.flux)),
# , se = sd/sqrt(n)),
by = c("date.strp","site","method","chamber.nr", "treatment_fact1", "treatment_fact2",
"treatment_fact3", "block","sub_factor")]
# ghg.file.plotsd[, cums := cumsum(mean), "chamber.nr"]
# browser()
ghg.file.plotsd[, agg.flx := agg.fluxes(mean,date.strp, timeunit = "hours"),
by = c("site","method","chamber.nr", "treatment_fact1", "treatment_fact2",
"treatment_fact3", "block","sub_factor")]
ghg.file.plotsd.boxpl <- ghg.file.plotsd[, .(agg.flx = mean(agg.flx)), # site == select.site
# sd = sd(N2O.flux,na.rm = TRUE),
# , se = sd/sqrt(n)),
by = c("site","method","chamber.nr", "treatment_fact1", "treatment_fact2",
"treatment_fact3", "block","sub_factor")]
return(ghg.file.plotsd.boxpl)
} # end get.agg.data function
gasfluxes.get <- reactive({
# observeEvent(input$gasfluxes.go, {
input$gasfluxes.go
withProgress(message = ' Calculating gas fluxes. Please wait ...', {
# n <- length(ghg.file$ID)
# updateProgress <- function(detail = NULL) {
# progress$inc(amount = 1/n, detail = detail)
# }
flux.file <- wrap_gasfluxes(gc.samples = get.flux.data(), gas.species = input$gas.species, f.detect = input$f.detect)
})
return(flux.file)
# }, once = T)
})
gasfluxes.table <- reactive({
if (input$ghgtask == "fluxvisual") {
gasflx.data <- get.flux.data()
gasflx.data <- fread("data/gasflux-N2O_DiverFarming-Rshiny.csv")
gasflx.data[, c("ID","linear.f0.se","linear.f0.p","linear.C0","linear.AIC","linear.AICc","linear.RSE","linear.diagnostics",
"robust.linear.f0.se","robust.linear.f0.p","robust.linear.C0","robust.linear.weights","robust.linear.diagnostics",
"HMR.f0.se","HMR.f0.p","HMR.phi","HMR.AIC","HMR.AICc","HMR.RSE","day","month","year","f.detect.n2o","f.detect.co2","f.detect.ch4",
"t.meas","flux.se","flux.p","height [cm]","diameter [cm]") := NULL]
setcolorder(gasflx.data,
c("site","date","treatment_fact1","treatment_fact2","treatment_fact3","sub_factor","block","chamber.nr",
"linear.f0","linear.r","robust.linear.f0","HMR.f0","HMR.kappa","kappa.max","dynamic.kappa.f0","method","HMR.diagnostics"))
}
else if (input$ghgtask == "gasfluxes") {
gasflx.data <- gasfluxes.get()
# # gasflx.data <- fread("data/gasflxvis_gasfluxes_example_input.csv")
# Cols.chosen <- c("linear.f0.se","linear.f0.p","linear.C0","linear.AIC","linear.AICc","linear.RSE","linear.diagnostics",
# "robust.linear.f0.se","robust.linear.f0.p","robust.linear.C0","robust.linear.weights","robust.linear.diagnostics",
# "HMR.f0.se","HMR.f0.p","HMR.phi","HMR.AIC","HMR.AICc","HMR.RSE","t.meas","flux.se","flux.p")
# gasflx.data[, (Cols.chosen) := NULL]
# setcolorder(gasflx.data,
# c("site","date","treatment_fact1","treatment_fact2","treatment_fact3","sub_factor","block","chamber.nr",
# "CO2.TCD.mol","N2O.ECD.mol","CH4.FID.mol","chamber.A","chamber.V","vial.time","chamber.time"))
gasflx.data[, c("ID","linear.f0.se","linear.f0.p","linear.C0","linear.AIC","linear.AICc","linear.RSE","linear.diagnostics",
"robust.linear.f0.se","robust.linear.f0.p","robust.linear.C0","robust.linear.weights","robust.linear.diagnostics",
"HMR.f0.se","HMR.f0.p","HMR.phi","HMR.AIC","HMR.AICc","HMR.RSE","day","month","year","f.detect.n2o","f.detect.co2","f.detect.ch4",
"t.meas","flux.se","flux.p","height [cm]","diameter [cm]") := NULL]
setcolorder(gasflx.data,
c("site","date","treatment_fact1","treatment_fact2","treatment_fact3","sub_factor","block","chamber.nr",
"linear.f0","linear.r","robust.linear.f0","HMR.f0","HMR.kappa","kappa.max","dynamic.kappa.f0","method","HMR.diagnostics"))
}
return(gasflx.data)
})
output$plot.table <- DT::renderDataTable(datatable(get.plot.data(), options = list(pageLength = 15,lengthChange=T))
%>% formatSignif(c(5:8),3)
%>% formatDate(1) )
output$plot.table2 <- DT::renderDataTable(datatable(gasfluxes.table(), options = list(pageLength = 15,lengthChange=T))
%>% formatSignif(c(9:15),3))
# output$value <- renderPrint({input$plot.table2_rows_selected })
output$timelinePlot <- renderPlotly({
if(any(input$aggflux == "no") == TRUE) {
group.var2 <- switch(input$group.var,
"Treatment factor 1" = "treatment_fact1",
"Treatment factor 2" = "treatment_fact2",
"Treatment factor 3" = "treatment_fact3",
"Chamber number" = "chamber.nr" ,
"Block" = "block")
gas.unit <- switch(input$gas.species,
"N2O" = "mg N<sub>2</sub>O",
"CO2" = "g CO<sub>2</sub>" ,
"CH4" = "mg CH<sub>4</sub>")
ggplotly(
ggplot(data = get.plot.data(), aes_string(x ="date.strp", y = "mean", colour = group.var2)) +
geom_point(size = 0.95, show.legend = T) +
# ggplot(data = ghg.file.plot, aes(x = date.strp, y = mean, colour = group.var)) +
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.2) +
geom_line() + #ylim(0,1) +
# geom_smooth(method = "loess") +
theme_light() + # theme_gdocs() + # theme_dark() +
# theme(legend.position="top") + # this does not work in ggplotly
ylab(paste0("flux [",gas.unit," m<sup>-2</sub> h<sup>-1</sub>]")) + xlab("date") + labs(colour = paste0(" grouped by:\n ",input$group.var,sep="")) #
, height = 400, dynamicTicks = T) %>%layout(legend = list(orientation = "v", # show entries horizontally
xanchor = "right" )) # ) , # use center of legend as anchor
} # end if
}) # end tielinePlot
output$cumPlot <- renderPlotly({
if(any(input$aggflux == "cumsum") == TRUE) {
group.var2 <- switch(input$group.var,
"Treatment factor 1" = "treatment_fact1",
"Treatment factor 2" = "treatment_fact2",
"Treatment factor 3" = "treatment_fact3",
"Chamber number" = "chamber.nr" ,
"Block" = "block")
gas.unit <- switch(input$gas.species,
"N2O" = "mg N<sub>2</sub>O",
"CO2" = "g CO<sub>2</sub>" ,
"CH4" = "mg CH<sub>4</sub>")
ggplotly(
ggplot(data = get.plot.data(), aes_string(x ="date.strp", y = "cums", colour = group.var2)) +
geom_point(size = 0.95, show.legend = T) +
# ggplot(data = ghg.file.plot, aes(x = date.strp, y = mean, colour = group.var)) +
geom_errorbar(aes(ymin=cums-cums.se, ymax=cums+cums.se), width=.2) +
geom_line() + #ylim(0,1) +
theme_light() + # theme_gdocs() +
ylab(paste0("flux [",gas.unit," m<sup>-2</sub> h<sup>-1</sub>]")) + xlab("date") + labs(colour = paste0(" grouped by:\n ",input$group.var,sep="")) #
, height = 400, dynamicTicks = T) %>%layout(legend = list(orientation = "v", # show entries horizontally
xanchor = "right" )) # ) , # use center of legend as anchor
# x = 0.5)
} # end if
}) # end cumflux plot
output$aggPlot <- renderPlot({
if(any(input$aggflux == "aggflx") == TRUE) {
boxplot.data <- get.agg.data()
group.var2 <- switch(input$group.var,
"Treatment factor 1" = "treatment_fact1",
"Treatment factor 2" = "treatment_fact2",
"Treatment factor 3" = "treatment_fact3",
"Chamber number" = "chamber.nr" ,
"Block" = "block")
gas.unit <- switch(input$gas.species,
"N2O" = expression(aggregated~emission~(mg~N[2]*O~m^-2)), #"mg N[2]O",
"CO2" = expression(aggregated~emission~(g~CO[2]~m^-2)), #"g CO[2]" ,
"CH4" = expression(aggregated~emission~(mg~CH[4]~m^-2)) ) #"mg CH[4]")
# eval(parse(text = group.var2)) expression(Anthropogenic~SO[4]^{"2-"}~(ngm^-3))
# ggplotly(
ggplot(data = boxplot.data, aes_string(x = group.var2, y = "agg.flx", fill = group.var2)) +
theme_light() + # theme_gdocs() +
geom_signif(comparisons = combn(unique(eval(parse(text = paste("boxplot.data$",group.var2)))), 2, simplify = FALSE), #ghg.file.plotsd.boxpl list(unique(boxplot.data$group.var2)),
map_signif_level=TRUE) +
ylab(gas.unit) + xlab("treatments") + # ",gas.unit,"
# ylab(paste0(bquote('aggregated emissions [',gas.unit,']'))) +
geom_boxplot()
# , width = 300)
} # end if aggflx
} ) # end aggPlot
# Downloadable csv of selected dataset ----
output$downloadData <- downloadHandler(
# content = function(file) {
filename = function() {
paste("GHG-plotdata_",input$gas.species,".csv", sep="")
},
content = function(file) {
fwrite(get.plot.data(), file, row.names = FALSE, sep = ",")
}
)
output$downloadGasfluxes <- downloadHandler(
# content = function(file) {
filename = function() {
paste("GHG-gasfluxes_",input$gas.species,".csv", sep="")
},
content = function(file) {
fwrite(gasfluxes.table(), file, row.names = FALSE, sep = ",")
}
)
output$pics <- renderImage({
# When input$n is 1, filename is ./images/image1.jpeg$
# filename <- normalizePath(file.path('./pics',
# paste('15-06-18_CS10_12_oat_c2_interrow_2_50.png', sep='')))
# # pics.select <- input$plot.table2_rows_selected
pics.select <- input$plot.table2_row_last_clicked
# pics.length <- length(pics.select)
# filename <- NA
# for(s in 1:pics.length) filename <- c(filename, normalizePath(file.path('./pics',
# paste0(ghg.file$ID[s],'.png', sep=''))))
# img = readImage("/pics/pics/10_05_18_CS10_1.png", type = "png")
#
# )) (filename[1],normalizePath(file.path('./pics',
# paste0(ghg.file$ID[pics.select],'.png', sep=''))) )
# if(is.null(input$plot.table2_rows_selected) == FALSE) {
# filename <- normalizePath(file.path('./pics',
# paste0(gasfluxes.get()$ID[pics.select[pics.length]],'.png', sep='')))
# pic.date <- as.Date(gasfluxes.get()$date[pics.select],format = "%d-%m-%y")
gasflux.pics <- gasfluxes.get()
filename <- normalizePath(file.path('./pics', paste0(gasflux.pics$date[pics.select],'_', # paste0('./pics_',input$gas.species)
gasflux.pics$site[pics.select],'_',
gasflux.pics$chamber.nr[pics.select],'_',
gasflux.pics$treatment_fact1[pics.select],'_',
gasflux.pics$treatment_fact2[pics.select],'_',
gasflux.pics$treatment_fact3[pics.select],'_',
gasflux.pics$block[pics.select],'_',
gasflux.pics$sub_factor[pics.select],
'.png', sep='')))
list(src = filename)
# }
}, deleteFile = FALSE)
} # end server function
# Run the application
shinyApp(ui = ui, server = server)