library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
De_goppeln <- sunrise.set(50.9882234,13.7578221, "2023/01/01", timezone="MET", num.days=370)
sunrise <- De_goppeln$sunrise
sunset <- De_goppeln$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
De_goppeln["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
De_goppeln["ss"] <- as.POSIXct(sunset, format = "%H:%M")
De_goppeln["timestamp"] <- align.time(De_goppeln$sunrise, 60*10)
De_goppeln <- De_goppeln[c("timestamp", "sr", "ss")]
locsrss <- ggplot(De_goppeln, aes(x=De_goppeln$timestamp)) + geom_line(aes(y=De_goppeln$sr)) + geom_line(aes(y=De_goppeln$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - De_goppeln 2023", x = "Datum", y = "Zeit")
pdf("De_goppeln_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="De_goppeln_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
De_goppeln["Sonnenaufgang"] <- strftime(De_goppeln$sr, format="%H:%M")
De_goppeln["Sonnenuntergang"] <- strftime(De_goppeln$ss, format="%H:%M")
write.table(De_goppeln, file="De_goppeln_SaSu.csv", dec=',', sep=';', row.names=FALSE)