library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
92665 <- sunrise.set(49.7156367,12.159547299999986, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 92665$sunrise
sunset <- 92665$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
92665["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
92665["ss"] <- as.POSIXct(sunset, format = "%H:%M")
92665["timestamp"] <- align.time(92665$sunrise, 60*10)
92665 <- 92665[c("timestamp", "sr", "ss")]
locsrss <- ggplot(92665, aes(x=92665$timestamp)) + geom_line(aes(y=92665$sr)) + geom_line(aes(y=92665$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 92665 2023", x = "Datum", y = "Zeit")
pdf("92665_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="92665_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
92665["Sonnenaufgang"] <- strftime(92665$sr, format="%H:%M")
92665["Sonnenuntergang"] <- strftime(92665$ss, format="%H:%M")
write.table(92665, file="92665_SaSu.csv", dec=',', sep=';', row.names=FALSE)