library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Ditzingen <- sunrise.set(48.8264662,9.067556400000058, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Ditzingen$sunrise
sunset <- Ditzingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Ditzingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Ditzingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Ditzingen["timestamp"] <- align.time(Ditzingen$sunrise, 60*10)
Ditzingen <- Ditzingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Ditzingen, aes(x=Ditzingen$timestamp)) + geom_line(aes(y=Ditzingen$sr)) + geom_line(aes(y=Ditzingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Ditzingen 2023", x = "Datum", y = "Zeit")
pdf("Ditzingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Ditzingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Ditzingen["Sonnenaufgang"] <- strftime(Ditzingen$sr, format="%H:%M")
Ditzingen["Sonnenuntergang"] <- strftime(Ditzingen$ss, format="%H:%M")
write.table(Ditzingen, file="Ditzingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)