library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Dasing <- sunrise.set(48.3861122,11.0469892, "2022/01/01", timezone="MET", num.days=370)
sunrise <- Dasing$sunrise
sunset <- Dasing$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Dasing["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Dasing["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Dasing["timestamp"] <- align.time(Dasing$sunrise, 60*10)
Dasing <- Dasing[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Dasing, aes(x=Dasing$timestamp)) + geom_line(aes(y=Dasing$sr)) + geom_line(aes(y=Dasing$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Dasing 2022", x = "Datum", y = "Zeit")
pdf("Dasing_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Dasing_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Dasing["Sonnenaufgang"] <- strftime(Dasing$sr, format="%H:%M")
Dasing["Sonnenuntergang"] <- strftime(Dasing$ss, format="%H:%M")
write.table(Dasing, file="Dasing_SaSu.csv", dec=',', sep=';', row.names=FALSE)