In RespChamberProc: Processing Data from Respiration Chambers

library(knitr)
opts_chunk$set(out.extra='style="display:block; margin: auto"'
    #, fig.align="center"
    , fig.width=4.3, fig.height=3.2, dev.args=list(pointsize=10)
    , message=FALSE
    , results='hold'
    )
knit_hooks$set(spar = function(before, options, envir) {
    if (before){
        par( las=1 )                   #also y axis labels horizontal
        par(mar=c(2.0,3.3,0,0)+0.3 )  #margins
        par(tck=0.02 )                          #axe-tick length inside plots             
        par(mgp=c(1.1,0.2,0) )  #positioning of axis title, axis labels, axis
     }
})
# genVigs("severalCycles")

Processing several measurement cycles

isDevelopMode <- TRUE
library(twDev)
setwd('..');loadPkg()

if(!exists("isDevelopMode")) library(RespChamberProc)
set.seed(0815)      # for reproducible results

Determine subsets of single measurment cycles

First, the data is loaded. Here, directly from zipped logger-output.

fName <- system.file("genData/SMANIE_Chamber1_26032015.zip", package = "RespChamberProc")
if( nzchar(fName) ){ ds <- ds0 <- readDat(unz(fName, file=unzip(fName, list=TRUE)[1,"Name"] ),tz="UTC") }
head(ds)
plot( CO2_LI840 ~ TIMESTAMP, ds, ylab="CO2 (ppm)", xlab="Time")

The dataset contains several measurment cycles of light and dark chambers with increasing or decreasing concentations respectively.

First, we correct the pressure to standard units and correct the CO2 concentrations for water vapour.

ds$Pa <- ds0$AirPres * 100  # convert hPa to Pa
ds$CO2_dry <- corrConcDilution(ds, colConc = "CO2_LI840", colVapour = "H2O_LI840")
ds$H2O_dry <- corrConcDilution(ds, colConc = "H2O_LI840", colVapour = "H2O_LI840")
ds$VPD <- calcVPD( ds$SurTemp, ds$Pa, ds$H2O_LI840)

In order to process each measurement cycle independently, we first determine parts of the time series that are contiguous, i.e. without gaps and without change of an index variable, here variable collar.

dsChunk <- subsetContiguous(ds, colTime="TIMESTAMP", colIndex="Collar") 
head(dsChunk)

The new modified contains a new variable, iChunk, holding a factor that changes with different measurment cycles. This factor can be used to select subset of single measurement cycles.

dss <- subset(dsChunk, iChunk==15)
plot( CO2_dry ~ TIMESTAMP, dss, ylab="CO2 (ppm)", xlab="time (Minute:Second)")

Computing the flux

Function calcClosedChamberFluxForChunks helps you with subsetting the data and applying function calcClosedChamberFlux to each subset.

# for demonstration use only the first 20 cycles
dsChunk20 <- subset(dsChunk, as.integer(iChunk) <= 20) 
chamberVol=0.6*0.6*0.6      # chamber was a cube of 0.6m length
surfaceArea=0.6*0.6

resChunks1 <- calcClosedChamberFluxForChunks(dsChunk20, colTemp="T_LI840"
    ,fRegress = c(lin = regressFluxLinear, tanh = regressFluxTanh)  # linear and saturating shape
    ,debugInfo=list(omitEstimateLeverage=TRUE)  # faster
    ,volume=chamberVol
    ,area=surfaceArea
)
head(resChunks1)

The results are similar as for calcClosedChamberFlux, unless there are several rows identified by additional key column iChunk.

Plotting faceted data and fits

library(twDev)
reloadPkg("RespChamberProc")

Plot the results to dectect problems.

library(ggplot2)
plots <- plotCampaignConcSeries( dsChunk20, resChunks1, plotsPerPage=64L)  
print(plots$plot[[1]]) # print the first page

If argument fileName is provided to plotCampaignConcSeries. All plots are written to a pdf. If there are more cycles, i.e. plots, than argument plotsPerPage(default 64) there will be several pages in the pdf.