library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
91359 <- sunrise.set(49.6990783,11.1819092, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 91359$sunrise
sunset <- 91359$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
91359["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
91359["ss"] <- as.POSIXct(sunset, format = "%H:%M")
91359["timestamp"] <- align.time(91359$sunrise, 60*10)
91359 <- 91359[c("timestamp", "sr", "ss")]
locsrss <- ggplot(91359, aes(x=91359$timestamp)) + geom_line(aes(y=91359$sr)) + geom_line(aes(y=91359$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 91359 2023", x = "Datum", y = "Zeit")
pdf("91359_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="91359_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
91359["Sonnenaufgang"] <- strftime(91359$sr, format="%H:%M")
91359["Sonnenuntergang"] <- strftime(91359$ss, format="%H:%M")
write.table(91359, file="91359_SaSu.csv", dec=',', sep=';', row.names=FALSE)