library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Amsterdam <- sunrise.set(52.3702157,4.895167899999933, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Amsterdam$sunrise
sunset <- Amsterdam$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Amsterdam["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Amsterdam["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Amsterdam["timestamp"] <- align.time(Amsterdam$sunrise, 60*10)
Amsterdam <- Amsterdam[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Amsterdam, aes(x=Amsterdam$timestamp)) + geom_line(aes(y=Amsterdam$sr)) + geom_line(aes(y=Amsterdam$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Amsterdam 2024", x = "Datum", y = "Zeit")
pdf("Amsterdam_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Amsterdam_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Amsterdam["Sonnenaufgang"] <- strftime(Amsterdam$sr, format="%H:%M")
Amsterdam["Sonnenuntergang"] <- strftime(Amsterdam$ss, format="%H:%M")
write.table(Amsterdam, file="Amsterdam_SaSu.csv", dec=',', sep=';', row.names=FALSE)