library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
�� <- sunrise.set(67.88056139999999,12.982643300000063, "2023/01/01", timezone="MET", num.days=370)
sunrise <- ��$sunrise
sunset <- ��$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
��["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
��["ss"] <- as.POSIXct(sunset, format = "%H:%M")
��["timestamp"] <- align.time(��$sunrise, 60*10)
�� <- ��[c("timestamp", "sr", "ss")]
locsrss <- ggplot(��, aes(x=��$timestamp)) + geom_line(aes(y=��$sr)) + geom_line(aes(y=��$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - �� 2023", x = "Datum", y = "Zeit")
pdf("��_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="��_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
��["Sonnenaufgang"] <- strftime(��$sr, format="%H:%M")
��["Sonnenuntergang"] <- strftime(��$ss, format="%H:%M")
write.table(��, file="��_SaSu.csv", dec=',', sep=';', row.names=FALSE)