library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Ahlen <- sunrise.set(51.76868973964185,7.8792572021484375, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Ahlen$sunrise
sunset <- Ahlen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Ahlen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Ahlen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Ahlen["timestamp"] <- align.time(Ahlen$sunrise, 60*10)
Ahlen <- Ahlen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Ahlen, aes(x=Ahlen$timestamp)) + geom_line(aes(y=Ahlen$sr)) + geom_line(aes(y=Ahlen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Ahlen 2023", x = "Datum", y = "Zeit")
pdf("Ahlen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Ahlen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Ahlen["Sonnenaufgang"] <- strftime(Ahlen$sr, format="%H:%M")
Ahlen["Sonnenuntergang"] <- strftime(Ahlen$ss, format="%H:%M")
write.table(Ahlen, file="Ahlen_SaSu.csv", dec=',', sep=';', row.names=FALSE)