library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
A_8462 <- sunrise.set(46.7109958,15.5332892, "2024/01/01", timezone="MET", num.days=370)
sunrise <- A_8462$sunrise
sunset <- A_8462$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
A_8462["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
A_8462["ss"] <- as.POSIXct(sunset, format = "%H:%M")
A_8462["timestamp"] <- align.time(A_8462$sunrise, 60*10)
A_8462 <- A_8462[c("timestamp", "sr", "ss")]
locsrss <- ggplot(A_8462, aes(x=A_8462$timestamp)) + geom_line(aes(y=A_8462$sr)) + geom_line(aes(y=A_8462$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - A_8462 2024", x = "Datum", y = "Zeit")
pdf("A_8462_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="A_8462_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
A_8462["Sonnenaufgang"] <- strftime(A_8462$sr, format="%H:%M")
A_8462["Sonnenuntergang"] <- strftime(A_8462$ss, format="%H:%M")
write.table(A_8462, file="A_8462_SaSu.csv", dec=',', sep=';', row.names=FALSE)