library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Aachen <- sunrise.set(50.63035444857536,6.285510063171387, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Aachen$sunrise
sunset <- Aachen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Aachen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Aachen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Aachen["timestamp"] <- align.time(Aachen$sunrise, 60*10)
Aachen <- Aachen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Aachen, aes(x=Aachen$timestamp)) + geom_line(aes(y=Aachen$sr)) + geom_line(aes(y=Aachen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Aachen 2024", x = "Datum", y = "Zeit")
pdf("Aachen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Aachen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Aachen["Sonnenaufgang"] <- strftime(Aachen$sr, format="%H:%M")
Aachen["Sonnenuntergang"] <- strftime(Aachen$ss, format="%H:%M")
write.table(Aachen, file="Aachen_SaSu.csv", dec=',', sep=';', row.names=FALSE)