library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Eschweiler <- sunrise.set(50.8107322,6.263075, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Eschweiler$sunrise
sunset <- Eschweiler$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Eschweiler["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Eschweiler["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Eschweiler["timestamp"] <- align.time(Eschweiler$sunrise, 60*10)
Eschweiler <- Eschweiler[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Eschweiler, aes(x=Eschweiler$timestamp)) + geom_line(aes(y=Eschweiler$sr)) + geom_line(aes(y=Eschweiler$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Eschweiler 2023", x = "Datum", y = "Zeit")
pdf("Eschweiler_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Eschweiler_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Eschweiler["Sonnenaufgang"] <- strftime(Eschweiler$sr, format="%H:%M")
Eschweiler["Sonnenuntergang"] <- strftime(Eschweiler$ss, format="%H:%M")
write.table(Eschweiler, file="Eschweiler_SaSu.csv", dec=',', sep=';', row.names=FALSE)