library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
17358 <- sunrise.set(53.6223109,14.013637199999948, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 17358$sunrise
sunset <- 17358$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
17358["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
17358["ss"] <- as.POSIXct(sunset, format = "%H:%M")
17358["timestamp"] <- align.time(17358$sunrise, 60*10)
17358 <- 17358[c("timestamp", "sr", "ss")]
locsrss <- ggplot(17358, aes(x=17358$timestamp)) + geom_line(aes(y=17358$sr)) + geom_line(aes(y=17358$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 17358 2023", x = "Datum", y = "Zeit")
pdf("17358_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="17358_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
17358["Sonnenaufgang"] <- strftime(17358$sr, format="%H:%M")
17358["Sonnenuntergang"] <- strftime(17358$ss, format="%H:%M")
write.table(17358, file="17358_SaSu.csv", dec=',', sep=';', row.names=FALSE)