library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_98544 <- sunrise.set(50.657286,10.678656, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_98544$sunrise
sunset <- D_98544$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_98544["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_98544["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_98544["timestamp"] <- align.time(D_98544$sunrise, 60*10)
D_98544 <- D_98544[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_98544, aes(x=D_98544$timestamp)) + geom_line(aes(y=D_98544$sr)) + geom_line(aes(y=D_98544$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_98544 2023", x = "Datum", y = "Zeit")
pdf("D_98544_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_98544_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_98544["Sonnenaufgang"] <- strftime(D_98544$sr, format="%H:%M")
D_98544["Sonnenuntergang"] <- strftime(D_98544$ss, format="%H:%M")
write.table(D_98544, file="D_98544_SaSu.csv", dec=',', sep=';', row.names=FALSE)