library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
De_10245 <- sunrise.set(52.5019332,13.4554471, "2023/01/01", timezone="MET", num.days=370)
sunrise <- De_10245$sunrise
sunset <- De_10245$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
De_10245["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
De_10245["ss"] <- as.POSIXct(sunset, format = "%H:%M")
De_10245["timestamp"] <- align.time(De_10245$sunrise, 60*10)
De_10245 <- De_10245[c("timestamp", "sr", "ss")]
locsrss <- ggplot(De_10245, aes(x=De_10245$timestamp)) + geom_line(aes(y=De_10245$sr)) + geom_line(aes(y=De_10245$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - De_10245 2023", x = "Datum", y = "Zeit")
pdf("De_10245_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="De_10245_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
De_10245["Sonnenaufgang"] <- strftime(De_10245$sr, format="%H:%M")
De_10245["Sonnenuntergang"] <- strftime(De_10245$ss, format="%H:%M")
write.table(De_10245, file="De_10245_SaSu.csv", dec=',', sep=';', row.names=FALSE)