library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Flammersfeld <- sunrise.set(50.6474944,7.527637099999993, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Flammersfeld$sunrise
sunset <- Flammersfeld$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Flammersfeld["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Flammersfeld["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Flammersfeld["timestamp"] <- align.time(Flammersfeld$sunrise, 60*10)
Flammersfeld <- Flammersfeld[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Flammersfeld, aes(x=Flammersfeld$timestamp)) + geom_line(aes(y=Flammersfeld$sr)) + geom_line(aes(y=Flammersfeld$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Flammersfeld 2023", x = "Datum", y = "Zeit")
pdf("Flammersfeld_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Flammersfeld_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Flammersfeld["Sonnenaufgang"] <- strftime(Flammersfeld$sr, format="%H:%M")
Flammersfeld["Sonnenuntergang"] <- strftime(Flammersfeld$ss, format="%H:%M")
write.table(Flammersfeld, file="Flammersfeld_SaSu.csv", dec=',', sep=';', row.names=FALSE)