library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_21217 <- sunrise.set(53.40560171918155,9.997966289520264, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_21217$sunrise
sunset <- DE_21217$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_21217["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_21217["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_21217["timestamp"] <- align.time(DE_21217$sunrise, 60*10)
DE_21217 <- DE_21217[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_21217, aes(x=DE_21217$timestamp)) + geom_line(aes(y=DE_21217$sr)) + geom_line(aes(y=DE_21217$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_21217 2023", x = "Datum", y = "Zeit")
pdf("DE_21217_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_21217_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_21217["Sonnenaufgang"] <- strftime(DE_21217$sr, format="%H:%M")
DE_21217["Sonnenuntergang"] <- strftime(DE_21217$ss, format="%H:%M")
write.table(DE_21217, file="DE_21217_SaSu.csv", dec=',', sep=';', row.names=FALSE)