library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Baesweiler <- sunrise.set(50.90633649999999,6.18253279999999, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Baesweiler$sunrise
sunset <- Baesweiler$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Baesweiler["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Baesweiler["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Baesweiler["timestamp"] <- align.time(Baesweiler$sunrise, 60*10)
Baesweiler <- Baesweiler[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Baesweiler, aes(x=Baesweiler$timestamp)) + geom_line(aes(y=Baesweiler$sr)) + geom_line(aes(y=Baesweiler$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Baesweiler 2023", x = "Datum", y = "Zeit")
pdf("Baesweiler_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Baesweiler_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Baesweiler["Sonnenaufgang"] <- strftime(Baesweiler$sr, format="%H:%M")
Baesweiler["Sonnenuntergang"] <- strftime(Baesweiler$ss, format="%H:%M")
write.table(Baesweiler, file="Baesweiler_SaSu.csv", dec=',', sep=';', row.names=FALSE)