library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
22453 <- sunrise.set(53.61252589999999,9.965985499999988, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 22453$sunrise
sunset <- 22453$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
22453["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
22453["ss"] <- as.POSIXct(sunset, format = "%H:%M")
22453["timestamp"] <- align.time(22453$sunrise, 60*10)
22453 <- 22453[c("timestamp", "sr", "ss")]
locsrss <- ggplot(22453, aes(x=22453$timestamp)) + geom_line(aes(y=22453$sr)) + geom_line(aes(y=22453$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 22453 2023", x = "Datum", y = "Zeit")
pdf("22453_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="22453_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
22453["Sonnenaufgang"] <- strftime(22453$sr, format="%H:%M")
22453["Sonnenuntergang"] <- strftime(22453$ss, format="%H:%M")
write.table(22453, file="22453_SaSu.csv", dec=',', sep=';', row.names=FALSE)