library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
28870 <- sunrise.set(53.117035,9.1186231, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 28870$sunrise
sunset <- 28870$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
28870["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
28870["ss"] <- as.POSIXct(sunset, format = "%H:%M")
28870["timestamp"] <- align.time(28870$sunrise, 60*10)
28870 <- 28870[c("timestamp", "sr", "ss")]
locsrss <- ggplot(28870, aes(x=28870$timestamp)) + geom_line(aes(y=28870$sr)) + geom_line(aes(y=28870$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 28870 2023", x = "Datum", y = "Zeit")
pdf("28870_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="28870_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
28870["Sonnenaufgang"] <- strftime(28870$sr, format="%H:%M")
28870["Sonnenuntergang"] <- strftime(28870$ss, format="%H:%M")
write.table(28870, file="28870_SaSu.csv", dec=',', sep=';', row.names=FALSE)