library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
A_3484 <- sunrise.set(48.3793996681906,15.78409194946289, "2023/01/01", timezone="MET", num.days=370)
sunrise <- A_3484$sunrise
sunset <- A_3484$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
A_3484["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
A_3484["ss"] <- as.POSIXct(sunset, format = "%H:%M")
A_3484["timestamp"] <- align.time(A_3484$sunrise, 60*10)
A_3484 <- A_3484[c("timestamp", "sr", "ss")]
locsrss <- ggplot(A_3484, aes(x=A_3484$timestamp)) + geom_line(aes(y=A_3484$sr)) + geom_line(aes(y=A_3484$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - A_3484 2023", x = "Datum", y = "Zeit")
pdf("A_3484_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="A_3484_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
A_3484["Sonnenaufgang"] <- strftime(A_3484$sr, format="%H:%M")
A_3484["Sonnenuntergang"] <- strftime(A_3484$ss, format="%H:%M")
write.table(A_3484, file="A_3484_SaSu.csv", dec=',', sep=';', row.names=FALSE)