library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Stockberg <- sunrise.set(47.22880712862891,9.242913722991943, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Stockberg$sunrise
sunset <- Stockberg$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Stockberg["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Stockberg["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Stockberg["timestamp"] <- align.time(Stockberg$sunrise, 60*10)
Stockberg <- Stockberg[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Stockberg, aes(x=Stockberg$timestamp)) + geom_line(aes(y=Stockberg$sr)) + geom_line(aes(y=Stockberg$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Stockberg 2024", x = "Datum", y = "Zeit")
pdf("Stockberg_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Stockberg_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Stockberg["Sonnenaufgang"] <- strftime(Stockberg$sr, format="%H:%M")
Stockberg["Sonnenuntergang"] <- strftime(Stockberg$ss, format="%H:%M")
write.table(Stockberg, file="Stockberg_SaSu.csv", dec=',', sep=';', row.names=FALSE)