library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
86179 <- sunrise.set(48.29370115383244,10.891313552856445, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 86179$sunrise
sunset <- 86179$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
86179["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
86179["ss"] <- as.POSIXct(sunset, format = "%H:%M")
86179["timestamp"] <- align.time(86179$sunrise, 60*10)
86179 <- 86179[c("timestamp", "sr", "ss")]
locsrss <- ggplot(86179, aes(x=86179$timestamp)) + geom_line(aes(y=86179$sr)) + geom_line(aes(y=86179$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 86179 2023", x = "Datum", y = "Zeit")
pdf("86179_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="86179_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
86179["Sonnenaufgang"] <- strftime(86179$sr, format="%H:%M")
86179["Sonnenuntergang"] <- strftime(86179$ss, format="%H:%M")
write.table(86179, file="86179_SaSu.csv", dec=',', sep=';', row.names=FALSE)