library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Ahaus <- sunrise.set(52.07354499778169,6.992454528808594, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Ahaus$sunrise
sunset <- Ahaus$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Ahaus["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Ahaus["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Ahaus["timestamp"] <- align.time(Ahaus$sunrise, 60*10)
Ahaus <- Ahaus[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Ahaus, aes(x=Ahaus$timestamp)) + geom_line(aes(y=Ahaus$sr)) + geom_line(aes(y=Ahaus$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Ahaus 2024", x = "Datum", y = "Zeit")
pdf("Ahaus_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Ahaus_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Ahaus["Sonnenaufgang"] <- strftime(Ahaus$sr, format="%H:%M")
Ahaus["Sonnenuntergang"] <- strftime(Ahaus$ss, format="%H:%M")
write.table(Ahaus, file="Ahaus_SaSu.csv", dec=',', sep=';', row.names=FALSE)