library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Dieburg <- sunrise.set(49.9010547,8.84449589999997, "2022/01/01", timezone="MET", num.days=370)
sunrise <- Dieburg$sunrise
sunset <- Dieburg$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Dieburg["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Dieburg["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Dieburg["timestamp"] <- align.time(Dieburg$sunrise, 60*10)
Dieburg <- Dieburg[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Dieburg, aes(x=Dieburg$timestamp)) + geom_line(aes(y=Dieburg$sr)) + geom_line(aes(y=Dieburg$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Dieburg 2022", x = "Datum", y = "Zeit")
pdf("Dieburg_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Dieburg_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Dieburg["Sonnenaufgang"] <- strftime(Dieburg$sr, format="%H:%M")
Dieburg["Sonnenuntergang"] <- strftime(Dieburg$ss, format="%H:%M")
write.table(Dieburg, file="Dieburg_SaSu.csv", dec=',', sep=';', row.names=FALSE)