library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Hoisten <- sunrise.set(51.1434006,6.702440799999977, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Hoisten$sunrise
sunset <- Hoisten$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Hoisten["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Hoisten["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Hoisten["timestamp"] <- align.time(Hoisten$sunrise, 60*10)
Hoisten <- Hoisten[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Hoisten, aes(x=Hoisten$timestamp)) + geom_line(aes(y=Hoisten$sr)) + geom_line(aes(y=Hoisten$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Hoisten 2024", x = "Datum", y = "Zeit")
pdf("Hoisten_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Hoisten_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Hoisten["Sonnenaufgang"] <- strftime(Hoisten$sr, format="%H:%M")
Hoisten["Sonnenuntergang"] <- strftime(Hoisten$ss, format="%H:%M")
write.table(Hoisten, file="Hoisten_SaSu.csv", dec=',', sep=';', row.names=FALSE)