library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Echterdingen <- sunrise.set(48.69432477370376,9.169839303393587, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Echterdingen$sunrise
sunset <- Echterdingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Echterdingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Echterdingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Echterdingen["timestamp"] <- align.time(Echterdingen$sunrise, 60*10)
Echterdingen <- Echterdingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Echterdingen, aes(x=Echterdingen$timestamp)) + geom_line(aes(y=Echterdingen$sr)) + geom_line(aes(y=Echterdingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Echterdingen 2023", x = "Datum", y = "Zeit")
pdf("Echterdingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Echterdingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Echterdingen["Sonnenaufgang"] <- strftime(Echterdingen$sr, format="%H:%M")
Echterdingen["Sonnenuntergang"] <- strftime(Echterdingen$ss, format="%H:%M")
write.table(Echterdingen, file="Echterdingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)