library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Fiebing <- sunrise.set(53.3528381,7.6774491999999555, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Fiebing$sunrise
sunset <- Fiebing$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Fiebing["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Fiebing["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Fiebing["timestamp"] <- align.time(Fiebing$sunrise, 60*10)
Fiebing <- Fiebing[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Fiebing, aes(x=Fiebing$timestamp)) + geom_line(aes(y=Fiebing$sr)) + geom_line(aes(y=Fiebing$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Fiebing 2023", x = "Datum", y = "Zeit")
pdf("Fiebing_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Fiebing_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Fiebing["Sonnenaufgang"] <- strftime(Fiebing$sr, format="%H:%M")
Fiebing["Sonnenuntergang"] <- strftime(Fiebing$ss, format="%H:%M")
write.table(Fiebing, file="Fiebing_SaSu.csv", dec=',', sep=';', row.names=FALSE)