library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bad_saarow <- sunrise.set(52.2742962,14.0546568, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Bad_saarow$sunrise
sunset <- Bad_saarow$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bad_saarow["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bad_saarow["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bad_saarow["timestamp"] <- align.time(Bad_saarow$sunrise, 60*10)
Bad_saarow <- Bad_saarow[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bad_saarow, aes(x=Bad_saarow$timestamp)) + geom_line(aes(y=Bad_saarow$sr)) + geom_line(aes(y=Bad_saarow$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bad_saarow 2023", x = "Datum", y = "Zeit")
pdf("Bad_saarow_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bad_saarow_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bad_saarow["Sonnenaufgang"] <- strftime(Bad_saarow$sr, format="%H:%M")
Bad_saarow["Sonnenuntergang"] <- strftime(Bad_saarow$ss, format="%H:%M")
write.table(Bad_saarow, file="Bad_saarow_SaSu.csv", dec=',', sep=';', row.names=FALSE)