library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
De_09648 <- sunrise.set(50.970958,12.976111899999978, "2023/01/01", timezone="MET", num.days=370)
sunrise <- De_09648$sunrise
sunset <- De_09648$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
De_09648["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
De_09648["ss"] <- as.POSIXct(sunset, format = "%H:%M")
De_09648["timestamp"] <- align.time(De_09648$sunrise, 60*10)
De_09648 <- De_09648[c("timestamp", "sr", "ss")]
locsrss <- ggplot(De_09648, aes(x=De_09648$timestamp)) + geom_line(aes(y=De_09648$sr)) + geom_line(aes(y=De_09648$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - De_09648 2023", x = "Datum", y = "Zeit")
pdf("De_09648_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="De_09648_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
De_09648["Sonnenaufgang"] <- strftime(De_09648$sr, format="%H:%M")
De_09648["Sonnenuntergang"] <- strftime(De_09648$ss, format="%H:%M")
write.table(De_09648, file="De_09648_SaSu.csv", dec=',', sep=';', row.names=FALSE)