library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
90431 <- sunrise.set(49.4459471,11.026757299999986, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 90431$sunrise
sunset <- 90431$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
90431["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
90431["ss"] <- as.POSIXct(sunset, format = "%H:%M")
90431["timestamp"] <- align.time(90431$sunrise, 60*10)
90431 <- 90431[c("timestamp", "sr", "ss")]
locsrss <- ggplot(90431, aes(x=90431$timestamp)) + geom_line(aes(y=90431$sr)) + geom_line(aes(y=90431$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 90431 2023", x = "Datum", y = "Zeit")
pdf("90431_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="90431_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
90431["Sonnenaufgang"] <- strftime(90431$sr, format="%H:%M")
90431["Sonnenuntergang"] <- strftime(90431$ss, format="%H:%M")
write.table(90431, file="90431_SaSu.csv", dec=',', sep=';', row.names=FALSE)