library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_09120 <- sunrise.set(50.804263,12.9051769, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_09120$sunrise
sunset <- DE_09120$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_09120["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_09120["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_09120["timestamp"] <- align.time(DE_09120$sunrise, 60*10)
DE_09120 <- DE_09120[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_09120, aes(x=DE_09120$timestamp)) + geom_line(aes(y=DE_09120$sr)) + geom_line(aes(y=DE_09120$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_09120 2023", x = "Datum", y = "Zeit")
pdf("DE_09120_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_09120_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_09120["Sonnenaufgang"] <- strftime(DE_09120$sr, format="%H:%M")
DE_09120["Sonnenuntergang"] <- strftime(DE_09120$ss, format="%H:%M")
write.table(DE_09120, file="DE_09120_SaSu.csv", dec=',', sep=';', row.names=FALSE)