library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Aach <- sunrise.set(49.789631983795964,6.569674015045166, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Aach$sunrise
sunset <- Aach$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Aach["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Aach["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Aach["timestamp"] <- align.time(Aach$sunrise, 60*10)
Aach <- Aach[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Aach, aes(x=Aach$timestamp)) + geom_line(aes(y=Aach$sr)) + geom_line(aes(y=Aach$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Aach 2023", x = "Datum", y = "Zeit")
pdf("Aach_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Aach_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Aach["Sonnenaufgang"] <- strftime(Aach$sr, format="%H:%M")
Aach["Sonnenuntergang"] <- strftime(Aach$ss, format="%H:%M")
write.table(Aach, file="Aach_SaSu.csv", dec=',', sep=';', row.names=FALSE)