library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
___Sarnen <- sunrise.set(46.8961292,8.2448384, "2024/01/01", timezone="MET", num.days=370)
sunrise <- ___Sarnen$sunrise
sunset <- ___Sarnen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
___Sarnen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
___Sarnen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
___Sarnen["timestamp"] <- align.time(___Sarnen$sunrise, 60*10)
___Sarnen <- ___Sarnen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(___Sarnen, aes(x=___Sarnen$timestamp)) + geom_line(aes(y=___Sarnen$sr)) + geom_line(aes(y=___Sarnen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - ___Sarnen 2024", x = "Datum", y = "Zeit")
pdf("___Sarnen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="___Sarnen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
___Sarnen["Sonnenaufgang"] <- strftime(___Sarnen$sr, format="%H:%M")
___Sarnen["Sonnenuntergang"] <- strftime(___Sarnen$ss, format="%H:%M")
write.table(___Sarnen, file="___Sarnen_SaSu.csv", dec=',', sep=';', row.names=FALSE)