library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
21514 <- sunrise.set(53.528064301939835,10.621891021728516, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 21514$sunrise
sunset <- 21514$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
21514["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
21514["ss"] <- as.POSIXct(sunset, format = "%H:%M")
21514["timestamp"] <- align.time(21514$sunrise, 60*10)
21514 <- 21514[c("timestamp", "sr", "ss")]
locsrss <- ggplot(21514, aes(x=21514$timestamp)) + geom_line(aes(y=21514$sr)) + geom_line(aes(y=21514$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 21514 2023", x = "Datum", y = "Zeit")
pdf("21514_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="21514_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
21514["Sonnenaufgang"] <- strftime(21514$sr, format="%H:%M")
21514["Sonnenuntergang"] <- strftime(21514$ss, format="%H:%M")
write.table(21514, file="21514_SaSu.csv", dec=',', sep=';', row.names=FALSE)