library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Belp <- sunrise.set(46.8927297,7.4981, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Belp$sunrise
sunset <- Belp$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Belp["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Belp["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Belp["timestamp"] <- align.time(Belp$sunrise, 60*10)
Belp <- Belp[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Belp, aes(x=Belp$timestamp)) + geom_line(aes(y=Belp$sr)) + geom_line(aes(y=Belp$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Belp 2023", x = "Datum", y = "Zeit")
pdf("Belp_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Belp_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Belp["Sonnenaufgang"] <- strftime(Belp$sr, format="%H:%M")
Belp["Sonnenuntergang"] <- strftime(Belp$ss, format="%H:%M")
write.table(Belp, file="Belp_SaSu.csv", dec=',', sep=';', row.names=FALSE)