library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Baabe_ <- sunrise.set(54.3603502,13.704912899999954, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Baabe_$sunrise
sunset <- Baabe_$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Baabe_["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Baabe_["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Baabe_["timestamp"] <- align.time(Baabe_$sunrise, 60*10)
Baabe_ <- Baabe_[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Baabe_, aes(x=Baabe_$timestamp)) + geom_line(aes(y=Baabe_$sr)) + geom_line(aes(y=Baabe_$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Baabe_ 2024", x = "Datum", y = "Zeit")
pdf("Baabe__SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Baabe__SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Baabe_["Sonnenaufgang"] <- strftime(Baabe_$sr, format="%H:%M")
Baabe_["Sonnenuntergang"] <- strftime(Baabe_$ss, format="%H:%M")
write.table(Baabe_, file="Baabe__SaSu.csv", dec=',', sep=';', row.names=FALSE)