library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D��ren <- sunrise.set(50.85721817946077,6.455583572387695, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D��ren$sunrise
sunset <- D��ren$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D��ren["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D��ren["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D��ren["timestamp"] <- align.time(D��ren$sunrise, 60*10)
D��ren <- D��ren[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D��ren, aes(x=D��ren$timestamp)) + geom_line(aes(y=D��ren$sr)) + geom_line(aes(y=D��ren$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D��ren 2023", x = "Datum", y = "Zeit")
pdf("D��ren_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D��ren_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D��ren["Sonnenaufgang"] <- strftime(D��ren$sr, format="%H:%M")
D��ren["Sonnenuntergang"] <- strftime(D��ren$ss, format="%H:%M")
write.table(D��ren, file="D��ren_SaSu.csv", dec=',', sep=';', row.names=FALSE)