library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
18059 <- sunrise.set(54.032685,12.099800800000025, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 18059$sunrise
sunset <- 18059$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
18059["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
18059["ss"] <- as.POSIXct(sunset, format = "%H:%M")
18059["timestamp"] <- align.time(18059$sunrise, 60*10)
18059 <- 18059[c("timestamp", "sr", "ss")]
locsrss <- ggplot(18059, aes(x=18059$timestamp)) + geom_line(aes(y=18059$sr)) + geom_line(aes(y=18059$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 18059 2023", x = "Datum", y = "Zeit")
pdf("18059_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="18059_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
18059["Sonnenaufgang"] <- strftime(18059$sr, format="%H:%M")
18059["Sonnenuntergang"] <- strftime(18059$ss, format="%H:%M")
write.table(18059, file="18059_SaSu.csv", dec=',', sep=';', row.names=FALSE)