library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Darlington <- sunrise.set(54.52361,-1.5594579999999496, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Darlington$sunrise
sunset <- Darlington$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Darlington["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Darlington["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Darlington["timestamp"] <- align.time(Darlington$sunrise, 60*10)
Darlington <- Darlington[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Darlington, aes(x=Darlington$timestamp)) + geom_line(aes(y=Darlington$sr)) + geom_line(aes(y=Darlington$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Darlington 2024", x = "Datum", y = "Zeit")
pdf("Darlington_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Darlington_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Darlington["Sonnenaufgang"] <- strftime(Darlington$sr, format="%H:%M")
Darlington["Sonnenuntergang"] <- strftime(Darlington$ss, format="%H:%M")
write.table(Darlington, file="Darlington_SaSu.csv", dec=',', sep=';', row.names=FALSE)