library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
44536_l��nen <- sunrise.set(51.6037535,7.477477099999987, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 44536_l��nen$sunrise
sunset <- 44536_l��nen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
44536_l��nen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
44536_l��nen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
44536_l��nen["timestamp"] <- align.time(44536_l��nen$sunrise, 60*10)
44536_l��nen <- 44536_l��nen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(44536_l��nen, aes(x=44536_l��nen$timestamp)) + geom_line(aes(y=44536_l��nen$sr)) + geom_line(aes(y=44536_l��nen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 44536_l��nen 2023", x = "Datum", y = "Zeit")
pdf("44536_l��nen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="44536_l��nen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
44536_l��nen["Sonnenaufgang"] <- strftime(44536_l��nen$sr, format="%H:%M")
44536_l��nen["Sonnenuntergang"] <- strftime(44536_l��nen$ss, format="%H:%M")
write.table(44536_l��nen, file="44536_l��nen_SaSu.csv", dec=',', sep=';', row.names=FALSE)