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app.R
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app.R
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library(shiny);library(dplyr);library(lubridate)
#Dependencies can be install by calling:
#install.packages(c("shiny", "dplyr", "lubridate"))
#FluxBandit
#Shiny app for interactive processing and calculation of greenhouse gas emissions using commercial and DIY type sensor systems
#Kenneth Thorø Martinsen
#https://github.com/KennethTM/FluxBandit
version <- "FluxBandit-v0.7"
options(shiny.maxRequestSize=50*1024^2)
#Sensor specific functions for parsing sensor data
#Function should clean/prepare data for flux calculations and return dataframe with columns:
#datetime, relative humidity (%), air temperature (celcius), CO2 (ppm), CH4 (ppm) and water (ppm)
parse_diy <- function(path){
df <- read.csv(path) |>
filter(!is.na(datetime)) |>
filter(lead(!is.na(SampleNumber)), !is.na(SampleNumber)) |>
rename(rh = RH., ch4_smv=CH4smV) |>
mutate(datetime = ymd_hms(datetime),
airt = as.numeric(tempC),
abs_H = (6.112*exp((17.67*airt)/(airt+243.5))*rh*18.02)/((273.15+airt)*100*0.08314),
ppm_H20 = 1358.326542*abs_H,
co2 = (K30_CO2/(1-(ppm_H20/10^6))),
V0 = abs_H*5.160442+268.39739,
RsR0 = ((5000/ch4_smv)-1)/((5000/V0)-1),
ch4 = 19.969879*(RsR0^-1.5626939)+-0.0093223822*abs_H*(19.969879*RsR0^-1.5626939)-15.366139) |>
rename(water = ppm_H20) %>%
group_by(datetime) %>%
summarise_at(vars(rh, airt, co2, ch4, water), list(mean)) |>
select(datetime, rh, airt, co2, ch4, water)
return(df)
}
parse_lgr <- function(path){
df <- read.csv(path, skip=1) |>
mutate(datetime = dmy_hms(SysTime)) |>
select(datetime, co2 = X.CO2.d_ppm, ch4 = X.CH4.d_ppm, airt = GasT_C, water = X.H2O._ppm)
return(df)
}
parse_licor <- function(path){
df <- read.table(path, skip=7, header=FALSE) |>
mutate(datetime = ymd_hms(paste(V7, V8)),
ch4 = V11/1000) |>
select(datetime, water = V9, co2 = V10, ch4, airt = V13) |>
filter(!is.na(co2))
return(df)
}
sensor_parsers <- list("DIY" = parse_diy,
"LGR" = parse_lgr,
"LICOR" = parse_licor)
ui <- fluidPage(
titlePanel(version),
em("'Making flux calculations so simple it should be criminal'"),
p("Follow the 6 steps to select, save and export your flux measurements!"),
sidebarLayout(
sidebarPanel(
tags$b("1) Select sensor type"),
radioButtons("filetype", "", choices = c("DIY", "LGR", "LICOR"), selected = ""),
tags$p("Optional: Sensor start time (yyyy-mm-dd hh:mm:ss):"), textInput("time_input", NULL, value="", width = "200px"),
tags$b("2) Upload text file"),
fileInput("file", "",
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values",
"text/plain",
".csv", ".txt")),
tags$hr(),
tags$b("Step 3) Enter metadata"),
tags$p("Chamber volume (L):"), numericInput("chamber_vol", NULL, 280, min = 0, width = "100px"),
tags$p("Chamber area (m2):"), numericInput("chamber_area", NULL, 0.33, min = 0, width = "100px"),
tags$p("Atmos. pressure (atm):"), numericInput("atm_pres", NULL, 1, min = 0, width = "100px"),
tags$b("Step 4) Adjust time range"),
sliderInput("range", "",
ymd_hm("2021-01-01 12:00"),
ymd_hm("2021-12-31 12:00"),
c(ymd_hm("2021-01-01 12:00"),
ymd_hm("2021-12-31 12:00")),
60*60*24,
timezone="+0000"),
tags$hr(),
tags$b("5) Select and save flux (repeat)"),
tags$br(),
tags$p("Sample ID (optional):"), textInput("sample_id", NULL, "ID", width = "200px"),
actionButton("save", "Save"),
tags$hr(),
tags$b("6) Download data"),
tags$br(),
#Button to save the file
downloadButton('download', 'Download')
),
mainPanel(
tags$b("Main plot"),
p("Use slider (step 4) to adjust the x-axis"),
plotOutput("plot",
brush = brushOpts(
id = "plot_brush",
resetOnNew = TRUE
)),
tags$hr(),
tags$b("Zoom plot"),
p("Use mouse to select measurements in the main plot"),
plotOutput("plot_zoom"),
htmlOutput("result_string"),
tags$hr(),
tags$b("Saved data"),
p("Table with saved data, export table as '.csv' file using the download button"),
tableOutput("results")
)
)
)
server <- function(input, output, session){
data <- reactive({
req(input$file)
req(input$filetype)
df <- sensor_parsers[[input$filetype]](input$file$datapath)
time_start <- min(df$datetime)
time_end <- max(df$datetime)
updateSliderInput(session, "range", value = c(time_start, time_end),
min = time_start, max = time_end, step = 60)
return(df)
})
data_out <- data.frame()
output$plot <- renderPlot({
req(input$file)
data_subset <- data() %>%
filter(between(datetime, input$range[1], input$range[2]))
par(mar = c(5,4,4,4) + 0.1)
plot(x = data_subset$datetime,
y = data_subset$co2,
ylab=expression("CO"[2]*" (ppm)"),
xlab="Datetime",
main = "Overview plot")
mtext(expression("CH"[4]*" (ppm)"), side = 4, line = 3, col="coral")
co2_min = min(data_subset$co2)
co2_max = max(data_subset$co2)
ch4_min = min(data_subset$ch4)
ch4_max = max(data_subset$ch4)
ch4_scaled = (co2_max - co2_min)*((data_subset$ch4-ch4_min)/(ch4_max - ch4_min))+co2_min
ch4_labels = pretty(data_subset$ch4)
ch4_at = (co2_max - co2_min)*((ch4_labels-ch4_min)/(ch4_max - ch4_min))+co2_min
points(x = data_subset$datetime, y = ch4_scaled, col="coral")
axis(4, at = ch4_at, labels = ch4_labels, col="coral", col.ticks="coral")
legend("topright",
c(expression("CO"[2]), expression("CH"[4])),
col = c("black", "coral"), pch=19)
})
ranges2 <- reactiveValues(x = NULL, y = NULL)
data_subset <- reactive({
req(input$file)
if (!is.null(ranges2$x)) {
ranges2$x <- as_datetime(ranges2$x)
data_subset <- data() %>%
filter(between(datetime, ranges2$x[1], ranges2$x[2]),
between(co2, ranges2$y[1], ranges2$y[2])) %>%
mutate(sec = cumsum(c(0, diff(as.numeric(datetime)))))
lm_model_co2 <- lm(co2~sec, data = data_subset)
slope_co2 <- coef(lm_model_co2)[2]
intercept_co2 <- coef(lm_model_co2)[1]
r2_co2 <- summary(lm_model_co2)$r.squared
lm_model_ch4 <- lm(ch4~sec, data = data_subset)
slope_ch4 <- coef(lm_model_ch4)[2]
intercept_ch4 <- coef(lm_model_ch4)[1]
r2_ch4 <- summary(lm_model_ch4)$r.squared
mean_temp <- mean(data_subset$airt)
R <- 0.08206 #L atm K^-1 mol^-1
co2_flux <- (slope_co2*(input$chamber_vol*input$atm_pres))/(R*(273.15+mean_temp)*input$chamber_area)
ch4_flux <- (slope_ch4*(input$chamber_vol*input$atm_pres))/(R*(273.15+mean_temp)*input$chamber_area)
results_string <- paste0("<b>CO<sub>2</sub></b>: slope = ", round(slope_co2, 2), " (ppm s<sup>-1</sup>)",
", flux = ", round(co2_flux, 2), " (µmol m<sup>-2</sup> s<sup>-1</sup>)",
", R<sup>2</sup> = ", round(r2_co2, 2),
"<br>",
"<b>CH<sub>4</sub></b>: slope = ", round(slope_ch4, 2), " (ppm s<sup>-1</sup>)",
", flux = ", round(ch4_flux, 2), " (µmol m<sup>-2</sup> s<sup>-1</sup>)",
", R<sup>2</sup> = ", round(r2_ch4, 2))
results <- data.frame("version" = version,
"processing_date" = strftime(Sys.time(), "%Y-%m-%d %H:%M:%S", tz="UTC"),
"sensor_type" = input$filetype,
"id" = as.character(input$sample_id),
"start" = strftime(ranges2$x[1], "%Y-%m-%d %H:%M:%S", tz="UTC"),
"end" = strftime(ranges2$x[2], "%Y-%m-%d %H:%M:%S", tz="UTC"),
"CO2_slope" = slope_co2,
"CO2_intercept" = intercept_co2,
"CO2_R2" = r2_co2,
"CH4_slope" = slope_ch4,
"CH4_intercept" = intercept_ch4,
"CH4_R2" = r2_ch4,
"temperature" = mean_temp,
"chamber_volume" = input$chamber_vol,
"chamber_area" = input$chamber_area,
"CO2_flux_umol_m2_s" = co2_flux,
"CH4_flux_umol_m2_s" = ch4_flux)
}else{
data_subset <- data() %>%
mutate(sec = cumsum(c(0, diff(as.numeric(datetime)))))
results <- data.frame()
results_string <- ""
}
return(list("df" = data_subset,
"results" = results,
"results_string" = results_string))
})
output$plot_zoom <- renderPlot({
data <- data_subset()
par(mar = c(5,4,4,4) + 0.1)
plot(x = data$df$sec,
y = data$df$co2,
ylab=expression("CO"[2]*" (ppm)"),
xlab="Time steps",
main= "Zoom plot")
mtext(expression("CH"[4]*" (ppm)"), side = 4, line = 3, col="coral")
co2_min = min(data$df$co2)
co2_max = max(data$df$co2)
water_min = min(data$df$water)
water_max = max(data$df$water)
water_scaled = (co2_max - co2_min)*((data$df$water-water_min)/(water_max - water_min))+co2_min
points(x = data$df$sec, y = water_scaled, col="lightblue", type="l")
ch4_min = min(data$df$ch4)
ch4_max = max(data$df$ch4)
ch4_scaled = (co2_max - co2_min)*((data$df$ch4-ch4_min)/(ch4_max - ch4_min))+co2_min
ch4_labels = pretty(data$df$ch4)
ch4_at = (co2_max - co2_min)*((ch4_labels-ch4_min)/(ch4_max - ch4_min))+co2_min
points(x = data$df$sec, y = ch4_scaled, col="coral")
axis(4, at = ch4_at, labels = ch4_labels, col="coral", col.ticks="coral")
if (!is.null(ranges2$x)){
output$result_string <- renderText(data$results_string)
abline(data$results$CO2_intercept,
data$results$CO2_slope,
col = "black", lwd = 4)
lm_model_ch4_scaled <- lm(ch4_scaled~data$df$sec)
slope_ch4_scaled <- coef(lm_model_ch4_scaled)[2]
intercept_ch4_scaled <- coef(lm_model_ch4_scaled)[1]
abline(intercept_ch4_scaled,
slope_ch4_scaled,
col = "coral", lwd = 4)
}
legend("topright",
c(expression("CO"[2]), expression("CH"[4]), expression("H"[2]*"O")),
col = c("black", "coral", "lightblue"), pch=19)
})
observe({
brush <- input$plot_brush
if (!is.null(brush)) {
ranges2$x <- c(brush$xmin, brush$xmax)
ranges2$y <- c(brush$ymin, brush$ymax)
} else {
ranges2$x <- NULL
ranges2$y <- NULL
}
})
observeEvent(input$save,{
data_out <<- rbind(data_out, data_subset()$results)
output$results <- renderTable(data_out[, c("id", "start", "end",
"CO2_R2", "CO2_flux_umol_m2_s",
"CH4_R2", "CH4_flux_umol_m2_s")])
})
output$download <- downloadHandler(
filename = function() {
paste0(version, "-", Sys.Date(), ".csv")
},
content = function(file) {
write.csv(data_out, file, row.names = FALSE)
})
}
shinyApp(ui, server)