library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bad_Wildungen <- sunrise.set(51.1178991,9.1219134, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Bad_Wildungen$sunrise
sunset <- Bad_Wildungen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bad_Wildungen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bad_Wildungen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bad_Wildungen["timestamp"] <- align.time(Bad_Wildungen$sunrise, 60*10)
Bad_Wildungen <- Bad_Wildungen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bad_Wildungen, aes(x=Bad_Wildungen$timestamp)) + geom_line(aes(y=Bad_Wildungen$sr)) + geom_line(aes(y=Bad_Wildungen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bad_Wildungen 2024", x = "Datum", y = "Zeit")
pdf("Bad_Wildungen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bad_Wildungen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bad_Wildungen["Sonnenaufgang"] <- strftime(Bad_Wildungen$sr, format="%H:%M")
Bad_Wildungen["Sonnenuntergang"] <- strftime(Bad_Wildungen$ss, format="%H:%M")
write.table(Bad_Wildungen, file="Bad_Wildungen_SaSu.csv", dec=',', sep=';', row.names=FALSE)