/
fig7_mean_heatdose_time_trt.R
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fig7_mean_heatdose_time_trt.R
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####################################################################################
##SEASONALITY OF FIRE IN THE SOUTHERN APPALACHIAN MOUNTAINS, USA
##R code for plotting time series of time-integrated thermocouple probe heating
##Joint Fire Science Program Project #16-1-06-12
##Matthew C. Vaughan, M.S. Graduate
##Department of Forestry and Environmental Conservation, Clemson University
##Last updated 08/01/21, R version 4.1.0
####################################################################################
##initial setup##
#set working directory#
setwd("G:/My Drive/MS-Clemson/JFSP Seasonality/analysis/R/treatment effects/fuels and fire behavior")
#access libraries#
library(readxl)
library(tidyverse)
library(gridExtra)
library(cowplot)
library(ggpubr)
library(scales)
library(tidyquant)
library(naniar)
library(zoo)
library(data.table)
#import raw data from Excel worksheet#
#thermocouple temperature recordings for values >= 60 °C
tcp_ABS60 <- read_excel("Data/tcp_temp_abs60_ssm_adj_nrrm.xlsx",
col_types = c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric"))
tcp_ABS60 <- data.frame(tcp_ABS60)
#convert data frame to tidy structure#
tcp_ABS60_tidy <- gather(tcp_ABS60, GPID, temp, -time_sm_dstadj_s)
#add location information (DID, RID, Trt, UID, PID) to new fields
tcp_ABS60_tidy$DID <- substr(tcp_ABS60_tidy$GPID, start = 1, stop = 2)
tcp_ABS60_tidy$RID <- substr(tcp_ABS60_tidy$GPID, start = 1, stop = 3)
tcp_ABS60_tidy$Trt <- substr(tcp_ABS60_tidy$GPID, start = 4, stop = 4)
tcp_ABS60_tidy$UID <- substr(tcp_ABS60_tidy$GPID, start = 1, stop = 4)
tcp_ABS60_tidy$PID <- substr(tcp_ABS60_tidy$GPID, start = 1, stop = 7)
#re-arrange order of fields
tcp_ABS60_tidy <- tcp_ABS60_tidy[, c(1, 4, 5, 6, 7, 8, 2, 3)]
##calculate averages for individual burns (x4 ds, x3 gs)##
#dormant season#
#AP1D#
#filter data
ap1d_ABS60_tidy <- subset(tcp_ABS60_tidy, UID %in% "ap1d")
#instantaneous
ap1d_ABS60_inst_avg = tapply(ap1d_ABS60_tidy$temp, ap1d_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling
ap1d_ABS60_1hroll_avg = frollmean(ap1d_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#bind variables to data frame
ap1d_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s, ap1d_ABS60_inst_avg, ap1d_ABS60_1hroll_avg)
#AP2D#
#filter data
ap2d_ABS60_tidy <- subset(tcp_ABS60_tidy, UID %in% "ap2d")
#instantaneous
ap2d_ABS60_inst_avg = tapply(ap2d_ABS60_tidy$temp, ap2d_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling
ap2d_ABS60_1hroll_avg = frollmean(ap2d_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#bind variables to data frame
ap2d_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s, ap2d_ABS60_inst_avg, ap2d_ABS60_1hroll_avg)
#CR2D#
#filter data
cr2d_ABS60_tidy <- subset(tcp_ABS60_tidy, UID %in% "cr2d")
#instantaneous
cr2d_ABS60_inst_avg = tapply(cr2d_ABS60_tidy$temp, cr2d_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling
cr2d_ABS60_1hroll_avg = frollmean(cr2d_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#bind variables to data frame
cr2d_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s, cr2d_ABS60_inst_avg, cr2d_ABS60_1hroll_avg)
#CR3D#
#filter data
cr3d_ABS60_tidy <- subset(tcp_ABS60_tidy, UID %in% "cr3d")
#instantaneous
cr3d_ABS60_inst_avg = tapply(cr3d_ABS60_tidy$temp, cr3d_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling
cr3d_ABS60_1hroll_avg = frollmean(cr3d_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#bind variables to data frame
cr3d_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s, cr3d_ABS60_inst_avg, cr3d_ABS60_1hroll_avg)
#growing season#
#AP1G#
#filter data
ap1g_ABS60_tidy <- subset(tcp_ABS60_tidy, UID %in% "ap1g")
#instantaneous
ap1g_ABS60_inst_avg = tapply(ap1g_ABS60_tidy$temp, ap1g_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling
ap1g_ABS60_1hroll_avg = frollmean(ap1g_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#bind variables to data frame
ap1g_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s, ap1g_ABS60_inst_avg, ap1g_ABS60_1hroll_avg)
#AP2G#
#filter data
ap2g_ABS60_tidy <- subset(tcp_ABS60_tidy, UID %in% "ap2g")
#instantaneous
ap2g_ABS60_inst_avg = tapply(ap2g_ABS60_tidy$temp, ap2g_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling
ap2g_ABS60_1hroll_avg = frollmean(ap2g_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#bind variables to data frame
ap2g_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s, ap2g_ABS60_inst_avg, ap2g_ABS60_1hroll_avg)
#CR2G#
#filter data
cr2g_ABS60_tidy <- subset(tcp_ABS60_tidy, UID %in% "cr2g")
#instantaneous
cr2g_ABS60_inst_avg = tapply(cr2g_ABS60_tidy$temp, cr2g_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling
cr2g_ABS60_1hroll_avg = frollmean(cr2g_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#bind variables to data frame
cr2g_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s, cr2g_ABS60_inst_avg, cr2g_ABS60_1hroll_avg)
##calculate metrics for treatments (d, g)##
#dormant season#
#filter data
d_ABS60_tidy <- subset(tcp_ABS60_tidy, Trt %in% "d")
#sample size (n)
#instantaneous
d_ABS60_inst_n = tapply(d_ABS60_tidy$temp, d_ABS60_tidy$time_sm_dstadj_s, function(x) length(which(!is.na(x))))
#1 hr rolling mean
d_ABS60_1hroll_n = frollmean(d_ABS60_inst_n, 3600, align = "center", na.rm = TRUE)
#mean (avg)
#instantaneous
d_ABS60_inst_avg = tapply(d_ABS60_tidy$temp, d_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling mean
d_ABS60_1hroll_avg = frollmean(d_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#standard deviation (sd)
#instantaneous
d_ABS60_inst_sd = tapply(d_ABS60_tidy$temp, d_ABS60_tidy$time_sm_dstadj_s, sd, na.rm = TRUE)
#1 hr rolling mean
d_ABS60_1hroll_sd = frollmean(d_ABS60_inst_sd, 3600, align = "center", na.rm = TRUE)
#standard error of the mean (sem)
#instantaneous
d_ABS60_inst_sem = d_ABS60_inst_sd / sqrt(d_ABS60_inst_n)
#1 hr rolling mean
d_ABS60_1hroll_sem = frollmean(d_ABS60_inst_sem, 3600, align = "center", na.rm = TRUE)
#95% confidence interval (ci)
#upper bound
#instantaneous
d_ABS60_inst_ci_lower <- d_ABS60_inst_avg + qt((1 - 0.95) / 2, df = d_ABS60_inst_n - 1) * d_ABS60_inst_sem
#1 hr rolling mean
d_ABS60_1hroll_ci_lower = frollmean(d_ABS60_inst_ci_lower, 3600, align = "center", na.rm = TRUE)
#lower bound
#instantaneous
d_ABS60_inst_ci_upper <- d_ABS60_inst_avg - qt((1 - 0.95) / 2, df = d_ABS60_inst_n - 1) * d_ABS60_inst_sem
#1 hr rolling mean
d_ABS60_1hroll_ci_upper = frollmean(d_ABS60_inst_ci_upper, 3600, align = "center", na.rm = TRUE)
#bind metrics to data frame
d_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s,
d_ABS60_inst_n, d_ABS60_1hroll_n,
d_ABS60_inst_avg, d_ABS60_1hroll_avg,
d_ABS60_inst_sd, d_ABS60_1hroll_sd,
d_ABS60_inst_sem, d_ABS60_1hroll_sem,
d_ABS60_inst_ci_lower, d_ABS60_1hroll_ci_lower,
d_ABS60_inst_ci_upper, d_ABS60_1hroll_ci_upper)
#growing season#
#filter data
g_ABS60_tidy <- subset(tcp_ABS60_tidy, Trt %in% "g")
#sample size (n)
#instantaneous
g_ABS60_inst_n = tapply(g_ABS60_tidy$temp, g_ABS60_tidy$time_sm_dstadj_s, function(x) length(which(!is.na(x))))
#1 hr rolling mean
g_ABS60_1hroll_n = frollmean(g_ABS60_inst_n, 3600, align = "center", na.rm = TRUE)
#mean (avg)
#instantaneous
g_ABS60_inst_avg = tapply(g_ABS60_tidy$temp, g_ABS60_tidy$time_sm_dstadj_s, mean, na.rm = TRUE)
#1 hr rolling mean
g_ABS60_1hroll_avg = frollmean(g_ABS60_inst_avg, 3600, align = "center", na.rm = TRUE)
#standard deviation (sd)
#instantaneous
g_ABS60_inst_sd = tapply(g_ABS60_tidy$temp, g_ABS60_tidy$time_sm_dstadj_s, sd, na.rm = TRUE)
#1 hr rolling mean
g_ABS60_1hroll_sd = frollmean(g_ABS60_inst_sd, 3600, align = "center", na.rm = TRUE)
#standard error of the mean (sem)
#instantaneous
g_ABS60_inst_sem = g_ABS60_inst_sd / sqrt(g_ABS60_inst_n)
#1 hr rolling mean
g_ABS60_1hroll_sem = frollmean(g_ABS60_inst_sem, 3600, align = "center", na.rm = TRUE)
#95% confidence interval (ci)
#upper bound
#instantaneous
g_ABS60_inst_ci_lower <- g_ABS60_inst_avg + qt((1 - 0.95) / 2, df = g_ABS60_inst_n - 1) * g_ABS60_inst_sem
#1 hr rolling mean
g_ABS60_1hroll_ci_lower = frollmean(g_ABS60_inst_ci_lower, 3600, align = "center", na.rm = TRUE)
#lower bound
#instantaneous
g_ABS60_inst_ci_upper <- g_ABS60_inst_avg - qt((1 - 0.95) / 2, df = g_ABS60_inst_n - 1) * g_ABS60_inst_sem
#1 hr rolling mean
g_ABS60_1hroll_ci_upper = frollmean(g_ABS60_inst_ci_upper, 3600, align = "center", na.rm = TRUE)
#bind metrics to data frame
g_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s,
g_ABS60_inst_n, g_ABS60_1hroll_n,
g_ABS60_inst_avg, g_ABS60_1hroll_avg,
g_ABS60_inst_sd, g_ABS60_1hroll_sd,
g_ABS60_inst_sem, g_ABS60_1hroll_sem,
g_ABS60_inst_ci_lower, g_ABS60_1hroll_ci_lower,
g_ABS60_inst_ci_upper, g_ABS60_1hroll_ci_upper)
##bind series to new data frame(s)##
#individual unit means#
unit_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s,
#ap1d_ABS60_inst_avg = ap1d_ABS60_avg$ap1d_ABS60_inst_avg,
ap1d_ABS60_1hroll_avg = ap1d_ABS60_avg$ap1d_ABS60_1hroll_avg,
#ap2d_ABS60_inst_avg = ap2d_ABS60_avg$ap2d_ABS60_inst_avg,
ap2d_ABS60_1hroll_avg = ap2d_ABS60_avg$ap2d_ABS60_1hroll_avg,
#cr2d_ABS60_inst_avg = cr2d_ABS60_avg$cr2d_ABS60_inst_avg,
cr2d_ABS60_1hroll_avg = cr2d_ABS60_avg$cr2d_ABS60_1hroll_avg,
#cr3d_ABS60_inst_avg = cr3d_ABS60_avg$cr3d_ABS60_inst_avg,
cr3d_ABS60_1hroll_avg = cr3d_ABS60_avg$cr3d_ABS60_1hroll_avg,
#ap1g_ABS60_inst_avg = ap1g_ABS60_avg$ap1g_ABS60_inst_avg,
ap1g_ABS60_1hroll_avg = ap1g_ABS60_avg$ap1g_ABS60_1hroll_avg,
#ap2g_ABS60_inst_avg = ap2g_ABS60_avg$ap2g_ABS60_inst_avg,
ap2g_ABS60_1hroll_avg = ap2g_ABS60_avg$ap2g_ABS60_1hroll_avg,
#cr2g_ABS60_inst_avg = cr2g_ABS60_avg$cr2g_ABS60_inst_avg,
cr2g_ABS60_1hroll_avg = cr2g_ABS60_avg$cr2g_ABS60_1hroll_avg)
#treatment means#
trt_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s,
#d_ABS60_inst_avg = d_ABS60_avg$d_ABS60_inst_avg,
d_ABS60_1hroll_avg = d_ABS60_avg$d_ABS60_1hroll_avg,
#g_ABS60_inst_avg = g_ABS60_avg$g_ABS60_inst_avg,
g_ABS60_1hroll_avg = g_ABS60_avg$g_ABS60_1hroll_avg)
#treatment SE#
trtSE_ABS60_avg <- data.frame(time_sm_dstadj_s = tcp_ABS60$time_sm_dstadj_s,
d_ABS60_1hroll_avg = d_ABS60_avg$d_ABS60_1hroll_avg,
#d_ABS60_inst_sem = d_ABS60_avg$d_ABS60_inst_sem,
d_ABS60_1hroll_sem = d_ABS60_avg$d_ABS60_1hroll_sem,
g_ABS60_1hroll_avg = g_ABS60_avg$g_ABS60_1hroll_avg,
#g_ABS60_inst_sem = g_ABS60_avg$g_ABS60_inst_sem,
g_ABS60_1hroll_sem = g_ABS60_avg$g_ABS60_1hroll_sem)
#d_ABS60_inst_n = d_ABS60_avg$d_ABS60_inst_n,
#d_ABS60_1hroll_n = d_ABS60_avg$d_ABS60_1hroll_n,
#d_ABS60_inst_sd = d_ABS60_avg$d_ABS60_inst_sd,
#d_ABS60_1hroll_sd = d_ABS60_avg$d_ABS60_1hroll_sd,
#d_ABS60_inst_ci_lower = d_ABS60_avg$d_ABS60_inst_ci_lower,
#d_ABS60_1hroll_ci_lower = d_ABS60_avg$d_ABS60_1hroll_ci_lower,
#d_ABS60_inst_ci_upper = d_ABS60_avg$d_ABS60_inst_ci_upper,
#d_ABS60_1hroll_ci_upper = d_ABS60_avg$d_ABS60_1hroll_ci_upper,
#g_ABS60_inst_n = g_ABS60_avg$g_ABS60_inst_n,
#g_ABS60_1hroll_n = g_ABS60_avg$g_ABS60_1hroll_n,
#g_ABS60_inst_sd = g_ABS60_avg$g_ABS60_inst_sd,
#g_ABS60_1hroll_sd = g_ABS60_avg$g_ABS60_1hroll_sd,
#g_ABS60_inst_ci_lower = g_ABS60_avg$g_ABS60_inst_ci_lower,
#g_ABS60_1hroll_ci_lower = g_ABS60_avg$g_ABS60_1hroll_ci_lower,
#g_ABS60_inst_ci_upper = g_ABS60_avg$g_ABS60_inst_ci_upper,
#g_ABS60_1hroll_ci_upper = g_ABS60_avg$g_ABS60_1hroll_ci_upper)
#convert data frame(s) to tidy structure#
unit_ABS60_avg_tidy <- gather(unit_ABS60_avg, var, val, -time_sm_dstadj_s)
trt_ABS60_avg_tidy <- gather(trt_ABS60_avg, var, val, -time_sm_dstadj_s)
#trtSE_ABS60_avg_tidy <- gather(trtSE_ABS60_avg, var, val, -time_sm_dstadj_s)
##plot 1 hr rolling mean time series for each treatment##
plot.dose.burn <- ggplot() +
#treatment means#
geom_line(trt_ABS60_avg_tidy,
mapping = aes(x = time_sm_dstadj_s, y = val, color = var, alpha = var),
size = 1.5) +
#define colors
scale_color_manual(name = element_blank(),
breaks = c("d_ABS60_1hroll_avg",
"g_ABS60_1hroll_avg"),
labels = c("DS (all), with std err",
"GS (all), with std err"),
values = c("sienna4", "green4")) + #D and G
#define alpha transparency values
scale_alpha_manual(name = element_blank(),
breaks = c("d_ABS60_1hroll_avg",
"g_ABS60_1hroll_avg"),
labels = c("DS (all), with std err",
"GS (all), with std err"),
values = c(1, 1)) + #D and G
#treatment SE#
#dormant season
geom_ribbon(trtSE_ABS60_avg,
mapping = aes(x = time_sm_dstadj_s,
ymin = d_ABS60_1hroll_avg - (d_ABS60_1hroll_sem / 2),
ymax = d_ABS60_1hroll_avg + (d_ABS60_1hroll_sem / 2)),
fill = "sienna4",
alpha = 0.35) +
#growing season
geom_ribbon(trtSE_ABS60_avg,
mapping = aes(x = time_sm_dstadj_s,
ymin = g_ABS60_1hroll_avg - (g_ABS60_1hroll_sem / 2),
ymax = g_ABS60_1hroll_avg + (g_ABS60_1hroll_sem / 2)),
fill = "green4",
alpha = 0.35) +
#write axis labels
labs(x = "Time of burn day (hh:mm)",
y = "1 hr moving average of thermocouple\nheating for temperatures \u2265 60 °C (°C s)") +
#set theme details
theme_classic() +
theme(axis.text.x = element_text(size = 11),
axis.title.x = element_text(face = "bold", color = "black", size = 15),
axis.text.y = element_text(size = 11),
axis.title.y = element_text(face = "bold", color = "black", size = 15),
#set legend details
legend.position = c(0.8, 0.9),
legend.background = element_rect(fill = "gray90"),
legend.title = element_blank(),
legend.text = element_text(color = "black", size = 12),
legend.key.width = unit(2, "line")) +
#set continuous x-axis scale with defined breaks
scale_x_continuous(limits = c(41400, 66600),
breaks = c(43200, 50400, 57600, 64800),
labels = c("12:00", "14:00", "16:00", "18:00"),
expand = c(0, 0))
plot.dose.burn
#save to file
png("output/figures/ms 1/fig7_mean_heatdose_time_trt.png",
width = 6.5,
height = 5,
units = "in",
res = 600)
plot.dose.burn
dev.off()