library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Böbingen <- sunrise.set(48.8187469,9.923107500000015, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Böbingen$sunrise
sunset <- Böbingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Böbingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Böbingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Böbingen["timestamp"] <- align.time(Böbingen$sunrise, 60*10)
Böbingen <- Böbingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Böbingen, aes(x=Böbingen$timestamp)) + geom_line(aes(y=Böbingen$sr)) + geom_line(aes(y=Böbingen$ss)) + labs(title = " Sunrise/Sunset - Böbingen 2024", x = "Date", y = "Time")
pdf("Böbingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Böbingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Böbingen["Sunrise"] <- strftime(Böbingen$sr, format="%H:%M")
Böbingen["Sunset"] <- strftime(Böbingen$ss, format="%H:%M")
write.table(Böbingen, file="Böbingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)