library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
de_45329 <- sunrise.set(51.5108223,7.014328299999988, "2023/01/01", timezone="MET", num.days=370)
sunrise <- de_45329$sunrise
sunset <- de_45329$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
de_45329["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
de_45329["ss"] <- as.POSIXct(sunset, format = "%H:%M")
de_45329["timestamp"] <- align.time(de_45329$sunrise, 60*10)
de_45329 <- de_45329[c("timestamp", "sr", "ss")]
locsrss <- ggplot(de_45329, aes(x=de_45329$timestamp)) + geom_line(aes(y=de_45329$sr)) + geom_line(aes(y=de_45329$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - de_45329 2023", x = "Datum", y = "Zeit")
pdf("de_45329_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="de_45329_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
de_45329["Sonnenaufgang"] <- strftime(de_45329$sr, format="%H:%M")
de_45329["Sonnenuntergang"] <- strftime(de_45329$ss, format="%H:%M")
write.table(de_45329, file="de_45329_SaSu.csv", dec=',', sep=';', row.names=FALSE)