library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
59387 <- sunrise.set(51.7956602,7.625261300000034, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 59387$sunrise
sunset <- 59387$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
59387["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
59387["ss"] <- as.POSIXct(sunset, format = "%H:%M")
59387["timestamp"] <- align.time(59387$sunrise, 60*10)
59387 <- 59387[c("timestamp", "sr", "ss")]
locsrss <- ggplot(59387, aes(x=59387$timestamp)) + geom_line(aes(y=59387$sr)) + geom_line(aes(y=59387$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 59387 2023", x = "Datum", y = "Zeit")
pdf("59387_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="59387_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
59387["Sonnenaufgang"] <- strftime(59387$sr, format="%H:%M")
59387["Sonnenuntergang"] <- strftime(59387$ss, format="%H:%M")
write.table(59387, file="59387_SaSu.csv", dec=',', sep=';', row.names=FALSE)