library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Clenze <- sunrise.set(52.9357195,10.954878, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Clenze$sunrise
sunset <- Clenze$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Clenze["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Clenze["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Clenze["timestamp"] <- align.time(Clenze$sunrise, 60*10)
Clenze <- Clenze[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Clenze, aes(x=Clenze$timestamp)) + geom_line(aes(y=Clenze$sr)) + geom_line(aes(y=Clenze$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Clenze 2023", x = "Datum", y = "Zeit")
pdf("Clenze_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Clenze_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Clenze["Sonnenaufgang"] <- strftime(Clenze$sr, format="%H:%M")
Clenze["Sonnenuntergang"] <- strftime(Clenze$ss, format="%H:%M")
write.table(Clenze, file="Clenze_SaSu.csv", dec=',', sep=';', row.names=FALSE)