library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
_Waren <- sunrise.set(53.516667,12.684, "2023/01/01", timezone="MET", num.days=370)
sunrise <- _Waren$sunrise
sunset <- _Waren$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
_Waren["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
_Waren["ss"] <- as.POSIXct(sunset, format = "%H:%M")
_Waren["timestamp"] <- align.time(_Waren$sunrise, 60*10)
_Waren <- _Waren[c("timestamp", "sr", "ss")]
locsrss <- ggplot(_Waren, aes(x=_Waren$timestamp)) + geom_line(aes(y=_Waren$sr)) + geom_line(aes(y=_Waren$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - _Waren 2023", x = "Datum", y = "Zeit")
pdf("_Waren_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="_Waren_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
_Waren["Sonnenaufgang"] <- strftime(_Waren$sr, format="%H:%M")
_Waren["Sonnenuntergang"] <- strftime(_Waren$ss, format="%H:%M")
write.table(_Waren, file="_Waren_SaSu.csv", dec=',', sep=';', row.names=FALSE)