library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Waakirchen <- sunrise.set(47.7727065,11.672990099999992, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Waakirchen$sunrise
sunset <- Waakirchen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Waakirchen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Waakirchen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Waakirchen["timestamp"] <- align.time(Waakirchen$sunrise, 60*10)
Waakirchen <- Waakirchen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Waakirchen, aes(x=Waakirchen$timestamp)) + geom_line(aes(y=Waakirchen$sr)) + geom_line(aes(y=Waakirchen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Waakirchen 2024", x = "Datum", y = "Zeit")
pdf("Waakirchen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Waakirchen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Waakirchen["Sonnenaufgang"] <- strftime(Waakirchen$sr, format="%H:%M")
Waakirchen["Sonnenuntergang"] <- strftime(Waakirchen$ss, format="%H:%M")
write.table(Waakirchen, file="Waakirchen_SaSu.csv", dec=',', sep=';', row.names=FALSE)