library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Berlin <- sunrise.set(52.499372236459024,13.36538605261228, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Berlin$sunrise
sunset <- Berlin$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Berlin["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Berlin["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Berlin["timestamp"] <- align.time(Berlin$sunrise, 60*10)
Berlin <- Berlin[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Berlin, aes(x=Berlin$timestamp)) + geom_line(aes(y=Berlin$sr)) + geom_line(aes(y=Berlin$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Berlin 2024", x = "Datum", y = "Zeit")
pdf("Berlin_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Berlin_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Berlin["Sonnenaufgang"] <- strftime(Berlin$sr, format="%H:%M")
Berlin["Sonnenuntergang"] <- strftime(Berlin$ss, format="%H:%M")
write.table(Berlin, file="Berlin_SaSu.csv", dec=',', sep=';', row.names=FALSE)