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Lesson_25.R
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library(chillR)
library(ggplot2)
library(gganimate)
data <- read.csv("data/interactive_plot_PLS.csv", sep = ";")
# This part is to re-code the different conditions
data[which(data$Final_Condition == "Outside"), "Final_condition_2"] <- 1
data[which(data$Final_Condition == "Un-heated"), "Final_condition_2"] <- 2
data[which(data$Final_Condition == "Heated"), "Final_condition_2"] <- 3
# Implement the plot
ggplot(data, aes(Day, Final_condition_2, color = factor(Treatment, levels = c(1 : 33)))) +
geom_jitter(size = 4) +
geom_path(size = 1) +
scale_y_continuous(breaks = c(1, 2, 3),
labels = c("Outside", "Un-Heated", "Heated")) +
scale_x_continuous(breaks = as.numeric(levels(as.factor(data$Day))),
labels = levels(as.factor(data$Day))) +
labs(x = "Days of experiment", y = "Condition", color = "Treatment") +
theme_bw() +
theme(axis.text.y = element_text(angle = 90, hjust = 0.5),
legend.position = "none") +
transition_reveal(Day)
library(lubridate)
pheno_data<-read_tab("data/final_bio_data_S1_S2_apple.csv")
weather_data<-read_tab("data/final_weather_data_S1_S2.csv")
### reloading old functions
library(ggplot2)
library(colorRamps)
library(patchwork)
library(fields)
library(reshape2)
library(metR)
library(ggplot2)
library(colorRamps)
ggplot_PLS<-function(PLS_results)
{
library(ggplot2)
PLS_gg<-PLS_results$PLS_summary
PLS_gg[,"Month"]<-trunc(PLS_gg$Date/100)
PLS_gg[,"Day"]<-PLS_gg$Date-PLS_gg$Month*100
PLS_gg[,"Date"]<-ISOdate(2002,PLS_gg$Month,PLS_gg$Day)
PLS_gg[which(PLS_gg$JDay<=0),"Date"]<-ISOdate(2001,PLS_gg$Month[which(PLS_gg$JDay<=0)],PLS_gg$Day[which(PLS_gg$JDay<=0)])
PLS_gg[,"VIP_importance"]<-PLS_gg$VIP>=0.8
PLS_gg[,"VIP_Coeff"]<-factor(sign(PLS_gg$Coef)*PLS_gg$VIP_importance)
VIP_plot<- ggplot(PLS_gg,aes(x=Date,y=VIP)) +
geom_bar(stat='identity',aes(fill=VIP>0.8)) +
scale_fill_manual(name="VIP",
labels = c("<0.8", ">0.8"),
values = c("FALSE"="grey", "TRUE"="blue")) +
theme_bw(base_size=15) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank() )
coeff_plot<- ggplot(PLS_gg,aes(x=Date,y=Coef)) +
geom_bar(stat='identity',aes(fill=VIP_Coeff)) +
scale_fill_manual(name="Effect direction",
labels = c("Advancing", "Unimportant","Delaying"),
values = c("-1"="red", "0"="grey","1"="dark green")) +
theme_bw(base_size=15) +
ylab("PLS coefficient") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank() )
temp_plot<- ggplot(PLS_gg) +
geom_ribbon(aes(x=Date,ymin=Tmean-Tstdev,ymax=Tmean+Tstdev),fill="grey") +
geom_ribbon(aes(x=Date,ymin=Tmean-Tstdev*(VIP_Coeff==-1),ymax=Tmean+Tstdev*(VIP_Coeff==-1)),fill="red") +
geom_ribbon(aes(x=Date,ymin=Tmean-Tstdev*(VIP_Coeff==1),ymax=Tmean+Tstdev*(VIP_Coeff==1)),fill="dark green") +
geom_line(aes(x=Date,y=Tmean)) +
theme_bw(base_size=15) +
ylab(expression(paste(T[mean]," (°C)")))
library(patchwork)
plot<- (VIP_plot +
coeff_plot +
temp_plot +
plot_layout(ncol=1,
guides = "collect")
) & theme(legend.position = "right",
legend.text = element_text(size=8),
legend.title = element_text(size=10),
axis.title.x=element_blank())
plot
}
plot_PLS_chill_force<-function(plscf,
chill_metric="Chill_Portions",
heat_metric="GDH",
chill_label="CP",
heat_label="GDH",
chill_phase=c(-48,62),
heat_phase=c(-5,105.5))
{
PLS_gg<-plscf[[chill_metric]][[heat_metric]]$PLS_summary
PLS_gg[,"Month"]<-trunc(PLS_gg$Date/100)
PLS_gg[,"Day"]<-PLS_gg$Date-PLS_gg$Month*100
PLS_gg[,"Date"]<-ISOdate(2002,PLS_gg$Month,PLS_gg$Day)
PLS_gg[which(PLS_gg$JDay<=0),"Date"]<-ISOdate(2001,PLS_gg$Month[which(PLS_gg$JDay<=0)],PLS_gg$Day[which(PLS_gg$JDay<=0)])
PLS_gg[,"VIP_importance"]<-PLS_gg$VIP>=0.8
PLS_gg[,"VIP_Coeff"]<-factor(sign(PLS_gg$Coef)*PLS_gg$VIP_importance)
chill_start_date<-ISOdate(2001,12,31)+chill_phase[1]*24*3600
chill_end_date<-ISOdate(2001,12,31)+chill_phase[2]*24*3600
heat_start_date<-ISOdate(2001,12,31)+heat_phase[1]*24*3600
heat_end_date<-ISOdate(2001,12,31)+heat_phase[2]*24*3600
temp_plot<- ggplot(PLS_gg) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "red") +
annotate("rect",
xmin = ISOdate(2001,12,31) + min(plscf$pheno$pheno,na.rm=TRUE)*24*3600,
xmax = ISOdate(2001,12,31) + max(plscf$pheno$pheno,na.rm=TRUE)*24*3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "black") +
geom_vline(xintercept = ISOdate(2001,12,31) + median(plscf$pheno$pheno,na.rm=TRUE)*24*3600, linetype = "dashed") +
geom_ribbon(aes(x=Date,
ymin=MetricMean - MetricStdev ,
ymax=MetricMean + MetricStdev ),
fill="grey") +
geom_ribbon(aes(x=Date,
ymin=MetricMean - MetricStdev * (VIP_Coeff==-1),
ymax=MetricMean + MetricStdev * (VIP_Coeff==-1)),
fill="red") +
geom_ribbon(aes(x=Date,
ymin=MetricMean - MetricStdev * (VIP_Coeff==1),
ymax=MetricMean + MetricStdev * (VIP_Coeff==1)),
fill="dark green") +
geom_line(aes(x=Date,y=MetricMean )) +
facet_wrap(vars(Type), scales = "free_y",
strip.position="left",
labeller = labeller(Type = as_labeller(c(Chill=paste0("Chill (",chill_label,")"),Heat=paste0("Heat (",heat_label,")")) )) ) +
ggtitle("Daily chill and heat accumulation rates") +
theme_bw(base_size=15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size =12),
plot.title = element_text(hjust = 0.5),
axis.title.y=element_blank()
)
VIP_plot<- ggplot(PLS_gg,aes(x=Date,y=VIP)) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "red") +
annotate("rect",
xmin = ISOdate(2001,12,31) + min(plscf$pheno$pheno,na.rm=TRUE)*24*3600,
xmax = ISOdate(2001,12,31) + max(plscf$pheno$pheno,na.rm=TRUE)*24*3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "black") +
geom_vline(xintercept = ISOdate(2001,12,31) + median(plscf$pheno$pheno,na.rm=TRUE)*24*3600, linetype = "dashed") +
geom_bar(stat='identity',aes(fill=VIP>0.8)) +
facet_wrap(vars(Type), scales="free",
strip.position="left",
labeller = labeller(Type = as_labeller(c(Chill="VIP for chill",Heat="VIP for heat") )) ) +
scale_y_continuous(limits=c(0,max(plscf[[chill_metric]][[heat_metric]]$PLS_summary$VIP))) +
ggtitle("Variable Importance in the Projection (VIP) scores") +
theme_bw(base_size=15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size =12),
plot.title = element_text(hjust = 0.5),
axis.title.y=element_blank()
) +
scale_fill_manual(name="VIP",
labels = c("<0.8", ">0.8"),
values = c("FALSE"="grey", "TRUE"="blue")) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
coeff_plot<- ggplot(PLS_gg,aes(x=Date,y=Coef)) +
annotate("rect",
xmin = chill_start_date,
xmax = chill_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "blue") +
annotate("rect",
xmin = heat_start_date,
xmax = heat_end_date,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "red") +
annotate("rect",
xmin = ISOdate(2001,12,31) + min(plscf$pheno$pheno,na.rm=TRUE)*24*3600,
xmax = ISOdate(2001,12,31) + max(plscf$pheno$pheno,na.rm=TRUE)*24*3600,
ymin = -Inf,
ymax = Inf,
alpha = .1,fill = "black") +
geom_vline(xintercept = ISOdate(2001,12,31) + median(plscf$pheno$pheno,na.rm=TRUE)*24*3600, linetype = "dashed") +
geom_bar(stat='identity',aes(fill=VIP_Coeff)) +
facet_wrap(vars(Type), scales="free",
strip.position="left",
labeller = labeller(Type = as_labeller(c(Chill="MC for chill",Heat="MC for heat") )) ) +
scale_y_continuous(limits=c(min(plscf[[chill_metric]][[heat_metric]]$PLS_summary$Coef),
max(plscf[[chill_metric]][[heat_metric]]$PLS_summary$Coef))) +
ggtitle("Model coefficients (MC)") +
theme_bw(base_size=15) +
theme(strip.background = element_blank(),
strip.placement = "outside",
strip.text.y = element_text(size =12),
plot.title = element_text(hjust = 0.5),
axis.title.y=element_blank()
) +
scale_fill_manual(name="Effect direction",
labels = c("Advancing", "Unimportant","Delaying"),
values = c("-1"="red", "0"="grey","1"="dark green")) +
ylab("PLS coefficient") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
library(patchwork)
plot<- (VIP_plot +
coeff_plot +
temp_plot +
plot_layout(ncol=1,
guides = "collect")
) & theme(legend.position = "right",
legend.text = element_text(size=8),
legend.title = element_text(size=10),
axis.title.x=element_blank())
plot
}
pheno_trend_ggplot<-function(temps,
pheno,
chill_phase,
heat_phase,
exclude_years=NA,
phenology_stage="Bloom")
{
library(fields)
library(reshape2)
library(metR)
library(ggplot2)
library(colorRamps)
# first, a sub-function (function defined within a function) to
# compute the temperature means
mean_temp_period<-function(temps,
start_JDay,
end_JDay,
end_season = end_JDay)
{ temps_JDay<-make_JDay(temps)
temps_JDay[,"Season"]<-temps_JDay$Year
if(start_JDay>end_season)
temps_JDay$Season[which(temps_JDay$JDay>=start_JDay)]<-
temps_JDay$Year[which(temps_JDay$JDay>=start_JDay)]+1
if(start_JDay>end_JDay)
sub_temps<-subset(temps_JDay,JDay<=end_JDay|JDay>=start_JDay)
if(start_JDay<=end_JDay)
sub_temps<-subset(temps_JDay,JDay<=end_JDay&JDay>=start_JDay)
mean_temps<-aggregate(sub_temps[,c("Tmin","Tmax")],
by=list(sub_temps$Season),
FUN=function(x) mean(x, na.rm=TRUE))
mean_temps[,"n_days"]<-aggregate(sub_temps[,"Tmin"],
by=list(sub_temps$Season),
FUN=length)[,2]
mean_temps[,"Tmean"]<-(mean_temps$Tmin+mean_temps$Tmax)/2
mean_temps<-mean_temps[,c(1,4,2,3,5)]
colnames(mean_temps)[1]<-"End_year"
return(mean_temps)
}
mean_temp_chill<-mean_temp_period(temps = temps,
start_JDay = chill_phase[1],
end_JDay = chill_phase[2],
end_season = heat_phase[2])
mean_temp_heat<-mean_temp_period(temps = temps,
start_JDay = heat_phase[1],
end_JDay = heat_phase[2],
end_season = heat_phase[2])
mean_temp_chill<-
mean_temp_chill[which(mean_temp_chill$n_days >=
max(mean_temp_chill$n_days)-1),]
mean_temp_heat<-
mean_temp_heat[which(mean_temp_heat$n_days >=
max(mean_temp_heat$n_days)-1),]
mean_chill<-mean_temp_chill[,c("End_year","Tmean")]
colnames(mean_chill)[2]<-"Tmean_chill"
mean_heat<-mean_temp_heat[,c("End_year","Tmean")]
colnames(mean_heat)[2]<-"Tmean_heat"
phase_Tmeans<-merge(mean_chill,mean_heat, by="End_year")
colnames(pheno)<-c("End_year","pheno")
Tmeans_pheno<-merge(phase_Tmeans,pheno, by="End_year")
if(!is.na(exclude_years[1]))
Tmeans_pheno<-Tmeans_pheno[which(!Tmeans_pheno$End_year %in% exclude_years),]
# Kriging interpolation
k<-Krig(x=as.matrix(Tmeans_pheno[,c("Tmean_chill","Tmean_heat")]),
Y=Tmeans_pheno$pheno)
pred<-predictSurface(k)
predictions<-as.data.frame(pred$z)
colnames(predictions) <- pred$y
predictions <- data.frame(Tmean_chill = pred$x, predictions)
melted<-melt(predictions,na.rm=TRUE,id.vars="Tmean_chill")
colnames(melted)<-c("Tmean_chill","Tmean_heat","value")
melted$Tmean_heat<-unique(pred$y)[as.numeric(melted$Tmean_heat)]
ggplot(melted,aes(x=Tmean_chill,y=Tmean_heat,z=value)) +
geom_contour_fill(bins=60) +
scale_fill_gradientn(colours=alpha(matlab.like(15)),
name=paste(phenology_stage,"date \n(day of the year)")) +
geom_contour(col="black") +
geom_text_contour(stroke = 0.2) +
geom_point(data=Tmeans_pheno,
aes(x=Tmean_chill,y=Tmean_heat,z=NULL),
size=0.7) +
ylab(expression(paste("Forcing phase ", T[mean]," (",degree,"C)"))) +
xlab(expression(paste("Chilling phase ", T[mean]," (",degree,"C)"))) +
theme_bw(base_size=15)
}
Chill_model_sensitivity<-function(latitude,
temp_models=list(Dynamic_Model=Dynamic_Model,GDH=GDH),
month_range=c(10,11,12,1,2,3),
Tmins=c(-10:20),
Tmaxs=c(-5:30))
{
mins<-NA
maxs<-NA
metrics<-as.list(rep(NA,length(temp_models)))
names(metrics)<-names(temp_models)
month<-NA
for(mon in month_range)
{
days_month<-as.numeric(difftime( ISOdate(2002,mon+1,1),
ISOdate(2002,mon,1) ))
if(mon==12) days_month<-31
weather<-make_all_day_table(data.frame(Year=c(2001,2002),
Month=c(mon,mon),
Day=c(1,days_month),
Tmin=c(0,0),Tmax=c(0,0)))
for(tmin in Tmins)
for(tmax in Tmaxs)
if(tmax>=tmin)
{
weather$Tmin<-tmin
weather$Tmax<-tmax
hourtemps<-stack_hourly_temps(weather,
latitude=latitude)$hourtemps$Temp
for(tm in 1:length(temp_models))
metrics[[tm]]<-c(metrics[[tm]],do.call(temp_models[[tm]],
list(hourtemps))[length(hourtemps)]/(length(hourtemps)/24))
mins<-c(mins,tmin)
maxs<-c(maxs,tmax)
month<-c(month,mon)
}
}
results<-cbind(data.frame(Month=month,Tmin=mins,Tmax=maxs),
as.data.frame(metrics))
results<-results[!is.na(results$Month),]
}
Chill_sensitivity_temps<-function(chill_model_sensitivity_table,
temperatures,
temp_model,
month_range=c(10,11,12,1,2,3),
Tmins=c(-10:20),
Tmaxs=c(-5:30),
legend_label="Chill/day (CP)")
{
library(ggplot2)
library(colorRamps)
cmst<-chill_model_sensitivity_table
cmst<-cmst[which(cmst$Month %in% month_range),]
cmst$Month_names<- factor(cmst$Month, levels=month_range,
labels=month.name[month_range])
DM_sensitivity<-ggplot(cmst,aes_string(x="Tmin",y="Tmax",fill=temp_model)) +
geom_tile() +
scale_fill_gradientn(colours=alpha(matlab.like(15), alpha = .5),
name=legend_label) +
xlim(Tmins[1],Tmins[length(Tmins)]) +
ylim(Tmaxs[1],Tmaxs[length(Tmaxs)])
temperatures<-
temperatures[which(temperatures$Month %in% month_range),]
temperatures[which(temperatures$Tmax<temperatures$Tmin),
c("Tmax","Tmin")]<-NA
temperatures$Month_names <- factor(temperatures$Month,
levels=month_range, labels=month.name[month_range])
DM_sensitivity +
geom_point(data=temperatures,
aes(x=Tmin,y=Tmax,fill=NULL,color="Temperature"),
size=0.2) +
facet_wrap(vars(Month_names)) +
scale_color_manual(values = "black",
labels = "Daily temperature \nextremes (°C)",
name="Observed at site" ) +
guides(fill = guide_colorbar(order = 1),
color = guide_legend(order = 2)) +
ylab("Tmax (°C)") +
xlab("Tmin (°C)") +
theme_bw(base_size=15)
}
### done with reloading
pheno_data$Year<-pheno_data$Treatment+2000
weather_data$Year[which(weather_data$Month<6)]<-
weather_data$Treatment[which(weather_data$Month<6)]+2000
weather_data$Year[which(weather_data$Month>=6)]<-
weather_data$Treatment[which(weather_data$Month>=6)]+1999
day_month_from_JDay<-function(year,JDay)
{
fulldate<-ISOdate(year-1,12,31)+JDay*3600*24
return(list(day(fulldate),month(fulldate)))
}
day_month_from_JDay(2000,307)
weather_data$Day<-day_month_from_JDay(weather_data$Year,weather_data$JDay)[[1]]
weather_data$Month<-day_month_from_JDay(weather_data$Year,weather_data$JDay)[[2]]
pls_out<-PLS_pheno(weather_data=weather_data,bio_data = pheno_data)
ggplot_PLS(pls_out)
temps_hourly<-stack_hourly_temps(weather_data,latitude=50.6)
daychill<-daily_chill(hourtemps=temps_hourly,
running_mean=1,
models = list(Chilling_Hours = Chilling_Hours, Utah_Chill_Units = Utah_Model,
Chill_Portions = Dynamic_Model, GDH = GDH)
)
plscf<-PLS_chill_force(daily_chill_obj=daychill,
bio_data_frame=pheno_data[!is.na(pheno_data$pheno),],
split_month=6,
chill_models = "Chill_Portions",
heat_models = "GDH",
runn_means = 11)
plot_PLS_chill_force(plscf,
chill_metric="Chill_Portions",
heat_metric="GDH",
chill_label="CP",
heat_label="GDH",
chill_phase=c(-76,10),
heat_phase=c(17,97.5))
chill_phase<-c(290,10)
heat_phase<-c(17,97.5)
chill<-tempResponse(hourtemps = temps_hourly,
Start_JDay = chill_phase[1],
End_JDay = chill_phase[2],
models = list(Chill_Portions = Dynamic_Model),
misstolerance=10)
heat<-tempResponse(hourtemps = temps_hourly,
Start_JDay = heat_phase[1],
End_JDay = heat_phase[2],
models = list(GDH = GDH))
chill_requirement <- median(chill$Chill_Portions)
chill_req_error <- quantile(chill$Chill_Portions, c(0.25,0.75))
heat_requirement <- median(heat$GDH)
heat_req_error <- quantile(heat$GDH, c(0.25,0.75))
# the following takes a bit of time
Model_sensitivities_CKA<-
Chill_model_sensitivity(latitude=50.6,
temp_models=list(Dynamic_Model=Dynamic_Model,GDH=GDH),
month_range=c(10:12,1:5))
write.csv(Model_sensitivities_CKA,
"data/Model_sensitivities_CKA.csv",row.names = FALSE)
# now we can plot the observed temperatures in the context of model sensitivity
Chill_sensitivity_temps(Model_sensitivities_CKA,
weather_data,
temp_model="Dynamic_Model",
month_range=c(10,11,12,1,2,3),
legend_label="Chill per day \n(Chill Portions)") +
ggtitle("Chill model sensitivity at Klein-Altendorf on steroids")
# and here's the temperature response plot for the enhanced PLS analysis
pheno_trend_ggplot(temps=weather_data,
pheno=pheno_data[,c("Year","pheno")],
chill_phase=chill_phase,
heat_phase=heat_phase,
exclude_years=pheno_data$Year[is.na(pheno_data$pheno)],
phenology_stage="Bloom")