library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
09496 <- sunrise.set(50.60886439999999,13.184678299999973, "2022/01/01", timezone="MET", num.days=370)
sunrise <- 09496$sunrise
sunset <- 09496$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
09496["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
09496["ss"] <- as.POSIXct(sunset, format = "%H:%M")
09496["timestamp"] <- align.time(09496$sunrise, 60*10)
09496 <- 09496[c("timestamp", "sr", "ss")]
locsrss <- ggplot(09496, aes(x=09496$timestamp)) + geom_line(aes(y=09496$sr)) + geom_line(aes(y=09496$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 09496 2022", x = "Datum", y = "Zeit")
pdf("09496_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="09496_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
09496["Sonnenaufgang"] <- strftime(09496$sr, format="%H:%M")
09496["Sonnenuntergang"] <- strftime(09496$ss, format="%H:%M")
write.table(09496, file="09496_SaSu.csv", dec=',', sep=';', row.names=FALSE)