library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Budenheim_ <- sunrise.set(50.0241889,8.1727507, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Budenheim_$sunrise
sunset <- Budenheim_$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Budenheim_["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Budenheim_["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Budenheim_["timestamp"] <- align.time(Budenheim_$sunrise, 60*10)
Budenheim_ <- Budenheim_[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Budenheim_, aes(x=Budenheim_$timestamp)) + geom_line(aes(y=Budenheim_$sr)) + geom_line(aes(y=Budenheim_$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Budenheim_ 2024", x = "Datum", y = "Zeit")
pdf("Budenheim__SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Budenheim__SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Budenheim_["Sonnenaufgang"] <- strftime(Budenheim_$sr, format="%H:%M")
Budenheim_["Sonnenuntergang"] <- strftime(Budenheim_$ss, format="%H:%M")
write.table(Budenheim_, file="Budenheim__SaSu.csv", dec=',', sep=';', row.names=FALSE)