library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Erding <- sunrise.set(48.3114648,11.918875800000023, "2022/01/01", timezone="MET", num.days=370)
sunrise <- Erding$sunrise
sunset <- Erding$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Erding["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Erding["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Erding["timestamp"] <- align.time(Erding$sunrise, 60*10)
Erding <- Erding[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Erding, aes(x=Erding$timestamp)) + geom_line(aes(y=Erding$sr)) + geom_line(aes(y=Erding$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Erding 2022", x = "Datum", y = "Zeit")
pdf("Erding_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Erding_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Erding["Sonnenaufgang"] <- strftime(Erding$sr, format="%H:%M")
Erding["Sonnenuntergang"] <- strftime(Erding$ss, format="%H:%M")
write.table(Erding, file="Erding_SaSu.csv", dec=',', sep=';', row.names=FALSE)