library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Burladingen <- sunrise.set(48.2923433,9.1095899, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Burladingen$sunrise
sunset <- Burladingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Burladingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Burladingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Burladingen["timestamp"] <- align.time(Burladingen$sunrise, 60*10)
Burladingen <- Burladingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Burladingen, aes(x=Burladingen$timestamp)) + geom_line(aes(y=Burladingen$sr)) + geom_line(aes(y=Burladingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Burladingen 2023", x = "Datum", y = "Zeit")
pdf("Burladingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Burladingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Burladingen["Sonnenaufgang"] <- strftime(Burladingen$sr, format="%H:%M")
Burladingen["Sonnenuntergang"] <- strftime(Burladingen$ss, format="%H:%M")
write.table(Burladingen, file="Burladingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)