library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bad_schussenried <- sunrise.set(48.0048324,9.6565073, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Bad_schussenried$sunrise
sunset <- Bad_schussenried$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bad_schussenried["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bad_schussenried["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bad_schussenried["timestamp"] <- align.time(Bad_schussenried$sunrise, 60*10)
Bad_schussenried <- Bad_schussenried[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bad_schussenried, aes(x=Bad_schussenried$timestamp)) + geom_line(aes(y=Bad_schussenried$sr)) + geom_line(aes(y=Bad_schussenried$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bad_schussenried 2023", x = "Datum", y = "Zeit")
pdf("Bad_schussenried_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bad_schussenried_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bad_schussenried["Sonnenaufgang"] <- strftime(Bad_schussenried$sr, format="%H:%M")
Bad_schussenried["Sonnenuntergang"] <- strftime(Bad_schussenried$ss, format="%H:%M")
write.table(Bad_schussenried, file="Bad_schussenried_SaSu.csv", dec=',', sep=';', row.names=FALSE)