exec/2019_04_25_Reorganize_sapflowdata_forFigure.R

In bmcnellis/SDEF.analysis: Data Analysis for 2016 San Dimas Experimental Forest Project

library(matrixStats)
# import sap flow data
setwd("D:/R_Workspace/SDEF/SDEF.analysis/data")
# this data is on a 0-1 scale... relative to maximum flux
load('max_daily_adenostoma_unaggregated.Rdata')
load("Adenostoma_2016_mphillips_processed_noavg.rda")
ad <- Adenostoma_2016_mphillips_processed_noavg
ad$date <- as.Date(ad$day, format = "%Y-%m-%d")
ad$Native <- ((ad$max_flux)/max(ad$max_flux, na.rm = TRUE))

sap1 <- max_daily
colnames(sap1) <- c("date", "P1", "P2", "P3", "P4", "P5", "P6", "P7",
                   "P8", "P9", "P10", "P11", "P12", "P13", "P14", "P15",
                   "P16")
sap1$mean <- rowMeans(sap1[, c(2:17)], na.rm = TRUE)
sap1$se <- apply(sap1[, -c(1, 18)], 1, plotrix::std.error)

#apply a two week running average to sap flow data
sap <- sap1[c(1,18)]
timesteps <- 14
df.mean <- sap
df.mean$date <- as.POSIXlt(df.mean$date)
df.mean[,2] <- zoo::rollmean(x=df.mean[,2], k=timesteps, fill=NA)
plot(df.mean)
colnames(df.mean) <- c("date", "mean_Flux")
df.mean$date <- as.character(df.mean$date)
df.mean$date <- as.Date(df.mean$date, format="%Y-%m-%d")
sap <- df.mean

#apply a two week running average to sapflow se data
sap2 <- sap1[c(1,19)]
timesteps <- 14
df.mean <- sap2
df.mean$date <- as.POSIXlt(df.mean$date)
df.mean[,2] <- zoo::rollmean(x=df.mean[,2], k=timesteps, fill=NA)
plot(df.mean)
colnames(df.mean) <- c("date", "se")
df.mean$date <- as.character(df.mean$date)
df.mean$date <- as.Date(df.mean$date, format="%Y-%m-%d")

sap$se <- df.mean$se

sap$measurement <- c(1:191)

sap <- na.omit(sap)

sap$Native<- ((sap$mean_Flux)/max(sap$mean_Flux))

sap$min <- (sap$Native - sap$se)
sap$max <- (sap$Native + sap$se)
save(sap, file = "Sapflow_normalizedtomax_14dayavg.Rdata")