library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bansin <- sunrise.set(53.96812329999999,14.137602499999957, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Bansin$sunrise
sunset <- Bansin$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bansin["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bansin["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bansin["timestamp"] <- align.time(Bansin$sunrise, 60*10)
Bansin <- Bansin[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bansin, aes(x=Bansin$timestamp)) + geom_line(aes(y=Bansin$sr)) + geom_line(aes(y=Bansin$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bansin 2024", x = "Datum", y = "Zeit")
pdf("Bansin_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bansin_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bansin["Sonnenaufgang"] <- strftime(Bansin$sr, format="%H:%M")
Bansin["Sonnenuntergang"] <- strftime(Bansin$ss, format="%H:%M")
write.table(Bansin, file="Bansin_SaSu.csv", dec=',', sep=';', row.names=FALSE)