library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Beckum <- sunrise.set(51.76064189864957,7.995086029272443, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Beckum$sunrise
sunset <- Beckum$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Beckum["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Beckum["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Beckum["timestamp"] <- align.time(Beckum$sunrise, 60*10)
Beckum <- Beckum[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Beckum, aes(x=Beckum$timestamp)) + geom_line(aes(y=Beckum$sr)) + geom_line(aes(y=Beckum$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Beckum 2024", x = "Datum", y = "Zeit")
pdf("Beckum_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Beckum_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Beckum["Sonnenaufgang"] <- strftime(Beckum$sr, format="%H:%M")
Beckum["Sonnenuntergang"] <- strftime(Beckum$ss, format="%H:%M")
write.table(Beckum, file="Beckum_SaSu.csv", dec=',', sep=';', row.names=FALSE)