if(!exists("isDevelopMode")) library(RespChamberProc)
set.seed(0815) # for reproducible results
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")
## # A tibble: 6 x 17
## TIMESTAMP RECORD Chamber Collar AirTemp AirPres PAR
## <dttm> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 2015-03-26 06:18:28 261827 1 0 5.136 987.6682 5.295341
## 2 2015-03-26 06:18:29 261828 1 0 5.136 987.7363 5.295341
## 3 2015-03-26 06:18:30 261829 1 0 5.136 987.6682 5.295341
## 4 2015-03-26 06:18:31 261830 1 0 5.126 987.7363 5.295341
## 5 2015-03-26 06:18:32 261831 1 0 5.126 987.6682 5.295341
## 6 2015-03-26 06:18:33 261832 1 0 5.126 987.6682 5.295341
## # ... with 10 more variables: BodyTemp <dbl>, SurTemp <dbl>,
## # SoilTemp <dbl>, SoilMoist <dbl>, CO2_LI840 <dbl>, H2O_LI840 <dbl>,
## # T_LI840 <dbl>, P_LI840 <dbl>, PTemp <dbl>, Batt <dbl>
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)
## # A tibble: 6 x 22
## iChunk TIMESTAMP RECORD Chamber Collar AirTemp AirPres
## <fctr> <dttm> <int> <int> <int> <dbl> <dbl>
## 1 4 2015-03-26 06:19:20 261864 1 1 4.993 987.7363
## 2 4 2015-03-26 06:19:21 261865 1 1 4.993 987.8045
## 3 4 2015-03-26 06:19:22 261866 1 1 4.984 987.7363
## 4 4 2015-03-26 06:19:23 261867 1 1 4.984 987.8045
## 5 4 2015-03-26 06:19:24 261868 1 1 4.984 987.8045
## 6 4 2015-03-26 06:19:25 261869 1 1 4.984 987.8045
## # ... with 15 more variables: PAR <dbl>, BodyTemp <dbl>, SurTemp <dbl>,
## # SoilTemp <dbl>, SoilMoist <dbl>, CO2_LI840 <dbl>, H2O_LI840 <dbl>,
## # T_LI840 <dbl>, P_LI840 <dbl>, PTemp <dbl>, Batt <dbl>, Pa <dbl>,
## # CO2_dry <dbl>, H2O_dry <dbl>, VPD <dbl>
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)")
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)
## # A tibble: 6 x 16
## # Groups: iChunk [6]
## iChunk flux fluxMedian sdFlux tLag lagIndex autoCorr
## <fctr> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
## 1 4 2.064670 NA 0.017284759 0 1 0.7054929
## 2 5 2.267797 NA 0.023260267 0 1 0.4862560
## 3 6 1.987172 NA 0.010605873 6 7 0.5562358
## 4 7 2.176030 NA 0.020329119 0 1 0.4185527
## 5 8 1.103253 NA 0.022461987 10 11 0.4499593
## 6 9 1.561600 NA 0.009077496 3 4 0.4984416
## # ... with 9 more variables: AIC <dbl>, sdFluxRegression <dbl>,
## # sdFluxLeverage <dbl>, iFRegress <dbl>, sdResid <dbl>, iqrResid <dbl>,
## # r2 <dbl>, times <list>, model <list>
The results are similar as for calcClosedChamberFlux, unless there are several rows identified by additional key column iChunk.
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.