library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Degenfeld <- sunrise.set(48.725995,9.878862000000026, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Degenfeld$sunrise
sunset <- Degenfeld$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Degenfeld["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Degenfeld["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Degenfeld["timestamp"] <- align.time(Degenfeld$sunrise, 60*10)
Degenfeld <- Degenfeld[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Degenfeld, aes(x=Degenfeld$timestamp)) + geom_line(aes(y=Degenfeld$sr)) + geom_line(aes(y=Degenfeld$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Degenfeld 2023", x = "Datum", y = "Zeit")
pdf("Degenfeld_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Degenfeld_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Degenfeld["Sonnenaufgang"] <- strftime(Degenfeld$sr, format="%H:%M")
Degenfeld["Sonnenuntergang"] <- strftime(Degenfeld$ss, format="%H:%M")
write.table(Degenfeld, file="Degenfeld_SaSu.csv", dec=',', sep=';', row.names=FALSE)