library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Aarhus <- sunrise.set(56.1581354,10.2120017, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Aarhus$sunrise
sunset <- Aarhus$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Aarhus["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Aarhus["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Aarhus["timestamp"] <- align.time(Aarhus$sunrise, 60*10)
Aarhus <- Aarhus[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Aarhus, aes(x=Aarhus$timestamp)) + geom_line(aes(y=Aarhus$sr)) + geom_line(aes(y=Aarhus$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Aarhus 2024", x = "Datum", y = "Zeit")
pdf("Aarhus_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Aarhus_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Aarhus["Sonnenaufgang"] <- strftime(Aarhus$sr, format="%H:%M")
Aarhus["Sonnenuntergang"] <- strftime(Aarhus$ss, format="%H:%M")
write.table(Aarhus, file="Aarhus_SaSu.csv", dec=',', sep=';', row.names=FALSE)