library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
50859_k��ln <- sunrise.set(50.9534818,6.835814199999959, "2022/01/01", timezone="MET", num.days=370)
sunrise <- 50859_k��ln$sunrise
sunset <- 50859_k��ln$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
50859_k��ln["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
50859_k��ln["ss"] <- as.POSIXct(sunset, format = "%H:%M")
50859_k��ln["timestamp"] <- align.time(50859_k��ln$sunrise, 60*10)
50859_k��ln <- 50859_k��ln[c("timestamp", "sr", "ss")]
locsrss <- ggplot(50859_k��ln, aes(x=50859_k��ln$timestamp)) + geom_line(aes(y=50859_k��ln$sr)) + geom_line(aes(y=50859_k��ln$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 50859_k��ln 2022", x = "Datum", y = "Zeit")
pdf("50859_k��ln_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="50859_k��ln_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
50859_k��ln["Sonnenaufgang"] <- strftime(50859_k��ln$sr, format="%H:%M")
50859_k��ln["Sonnenuntergang"] <- strftime(50859_k��ln$ss, format="%H:%M")
write.table(50859_k��ln, file="50859_k��ln_SaSu.csv", dec=',', sep=';', row.names=FALSE)