library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
35039_DE <- sunrise.set(50.792114885285315,8.783762454986572, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 35039_DE$sunrise
sunset <- 35039_DE$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
35039_DE["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
35039_DE["ss"] <- as.POSIXct(sunset, format = "%H:%M")
35039_DE["timestamp"] <- align.time(35039_DE$sunrise, 60*10)
35039_DE <- 35039_DE[c("timestamp", "sr", "ss")]
locsrss <- ggplot(35039_DE, aes(x=35039_DE$timestamp)) + geom_line(aes(y=35039_DE$sr)) + geom_line(aes(y=35039_DE$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 35039_DE 2023", x = "Datum", y = "Zeit")
pdf("35039_DE_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="35039_DE_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
35039_DE["Sonnenaufgang"] <- strftime(35039_DE$sr, format="%H:%M")
35039_DE["Sonnenuntergang"] <- strftime(35039_DE$ss, format="%H:%M")
write.table(35039_DE, file="35039_DE_SaSu.csv", dec=',', sep=';', row.names=FALSE)