library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE,02977 <- sunrise.set(51.418214134795335,14.224622748437469, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE,02977$sunrise
sunset <- DE,02977$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE,02977["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE,02977["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE,02977["timestamp"] <- align.time(DE,02977$sunrise, 60*10)
DE,02977 <- DE,02977[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE,02977, aes(x=DE,02977$timestamp)) + geom_line(aes(y=DE,02977$sr)) + geom_line(aes(y=DE,02977$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE,02977 2023", x = "Datum", y = "Zeit")
pdf("DE,02977_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE,02977_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE,02977["Sonnenaufgang"] <- strftime(DE,02977$sr, format="%H:%M")
DE,02977["Sonnenuntergang"] <- strftime(DE,02977$ss, format="%H:%M")
write.table(DE,02977, file="DE,02977_SaSu.csv", dec=',', sep=';', row.names=FALSE)