library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
AT_8010 <- sunrise.set(47.06410015311188,15.466604232788086, "2023/01/01", timezone="MET", num.days=370)
sunrise <- AT_8010$sunrise
sunset <- AT_8010$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
AT_8010["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
AT_8010["ss"] <- as.POSIXct(sunset, format = "%H:%M")
AT_8010["timestamp"] <- align.time(AT_8010$sunrise, 60*10)
AT_8010 <- AT_8010[c("timestamp", "sr", "ss")]
locsrss <- ggplot(AT_8010, aes(x=AT_8010$timestamp)) + geom_line(aes(y=AT_8010$sr)) + geom_line(aes(y=AT_8010$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - AT_8010 2023", x = "Datum", y = "Zeit")
pdf("AT_8010_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="AT_8010_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
AT_8010["Sonnenaufgang"] <- strftime(AT_8010$sr, format="%H:%M")
AT_8010["Sonnenuntergang"] <- strftime(AT_8010$ss, format="%H:%M")
write.table(AT_8010, file="AT_8010_SaSu.csv", dec=',', sep=';', row.names=FALSE)