library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Cala_millor <- sunrise.set(39.6043024,3.3783644000000095, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Cala_millor$sunrise
sunset <- Cala_millor$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Cala_millor["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Cala_millor["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Cala_millor["timestamp"] <- align.time(Cala_millor$sunrise, 60*10)
Cala_millor <- Cala_millor[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Cala_millor, aes(x=Cala_millor$timestamp)) + geom_line(aes(y=Cala_millor$sr)) + geom_line(aes(y=Cala_millor$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Cala_millor 2023", x = "Datum", y = "Zeit")
pdf("Cala_millor_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Cala_millor_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Cala_millor["Sonnenaufgang"] <- strftime(Cala_millor$sr, format="%H:%M")
Cala_millor["Sonnenuntergang"] <- strftime(Cala_millor$ss, format="%H:%M")
write.table(Cala_millor, file="Cala_millor_SaSu.csv", dec=',', sep=';', row.names=FALSE)