library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Corralejo <- sunrise.set(28.7296937,-13.8671499, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Corralejo$sunrise
sunset <- Corralejo$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Corralejo["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Corralejo["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Corralejo["timestamp"] <- align.time(Corralejo$sunrise, 60*10)
Corralejo <- Corralejo[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Corralejo, aes(x=Corralejo$timestamp)) + geom_line(aes(y=Corralejo$sr)) + geom_line(aes(y=Corralejo$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Corralejo 2023", x = "Datum", y = "Zeit")
pdf("Corralejo_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Corralejo_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Corralejo["Sonnenaufgang"] <- strftime(Corralejo$sr, format="%H:%M")
Corralejo["Sonnenuntergang"] <- strftime(Corralejo$ss, format="%H:%M")
write.table(Corralejo, file="Corralejo_SaSu.csv", dec=',', sep=';', row.names=FALSE)