library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
09353 <- sunrise.set(50.79110170000001,12.738841500000035, "2024/01/01", timezone="MET", num.days=370)
sunrise <- 09353$sunrise
sunset <- 09353$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
09353["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
09353["ss"] <- as.POSIXct(sunset, format = "%H:%M")
09353["timestamp"] <- align.time(09353$sunrise, 60*10)
09353 <- 09353[c("timestamp", "sr", "ss")]
locsrss <- ggplot(09353, aes(x=09353$timestamp)) + geom_line(aes(y=09353$sr)) + geom_line(aes(y=09353$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 09353 2024", x = "Datum", y = "Zeit")
pdf("09353_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="09353_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
09353["Sonnenaufgang"] <- strftime(09353$sr, format="%H:%M")
09353["Sonnenuntergang"] <- strftime(09353$ss, format="%H:%M")
write.table(09353, file="09353_SaSu.csv", dec=',', sep=';', row.names=FALSE)