library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bramsche <- sunrise.set(52.41638826718971,7.9794524154342525, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Bramsche$sunrise
sunset <- Bramsche$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bramsche["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bramsche["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bramsche["timestamp"] <- align.time(Bramsche$sunrise, 60*10)
Bramsche <- Bramsche[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bramsche, aes(x=Bramsche$timestamp)) + geom_line(aes(y=Bramsche$sr)) + geom_line(aes(y=Bramsche$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bramsche 2023", x = "Datum", y = "Zeit")
pdf("Bramsche_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bramsche_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bramsche["Sonnenaufgang"] <- strftime(Bramsche$sr, format="%H:%M")
Bramsche["Sonnenuntergang"] <- strftime(Bramsche$ss, format="%H:%M")
write.table(Bramsche, file="Bramsche_SaSu.csv", dec=',', sep=';', row.names=FALSE)