library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Chur <- sunrise.set(46.85726210000001,9.526730499999985, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Chur$sunrise
sunset <- Chur$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Chur["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Chur["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Chur["timestamp"] <- align.time(Chur$sunrise, 60*10)
Chur <- Chur[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Chur, aes(x=Chur$timestamp)) + geom_line(aes(y=Chur$sr)) + geom_line(aes(y=Chur$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Chur 2024", x = "Datum", y = "Zeit")
pdf("Chur_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Chur_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Chur["Sonnenaufgang"] <- strftime(Chur$sr, format="%H:%M")
Chur["Sonnenuntergang"] <- strftime(Chur$ss, format="%H:%M")
write.table(Chur, file="Chur_SaSu.csv", dec=',', sep=';', row.names=FALSE)