library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
9511_alta <- sunrise.set(69.97801995075896,23.288698196411133, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 9511_alta$sunrise
sunset <- 9511_alta$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
9511_alta["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
9511_alta["ss"] <- as.POSIXct(sunset, format = "%H:%M")
9511_alta["timestamp"] <- align.time(9511_alta$sunrise, 60*10)
9511_alta <- 9511_alta[c("timestamp", "sr", "ss")]
locsrss <- ggplot(9511_alta, aes(x=9511_alta$timestamp)) + geom_line(aes(y=9511_alta$sr)) + geom_line(aes(y=9511_alta$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 9511_alta 2023", x = "Datum", y = "Zeit")
pdf("9511_alta_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="9511_alta_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
9511_alta["Sonnenaufgang"] <- strftime(9511_alta$sr, format="%H:%M")
9511_alta["Sonnenuntergang"] <- strftime(9511_alta$ss, format="%H:%M")
write.table(9511_alta, file="9511_alta_SaSu.csv", dec=',', sep=';', row.names=FALSE)