library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bangkok <- sunrise.set(13.757327,100.502008, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Bangkok$sunrise
sunset <- Bangkok$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bangkok["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bangkok["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bangkok["timestamp"] <- align.time(Bangkok$sunrise, 60*10)
Bangkok <- Bangkok[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bangkok, aes(x=Bangkok$timestamp)) + geom_line(aes(y=Bangkok$sr)) + geom_line(aes(y=Bangkok$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bangkok 2024", x = "Datum", y = "Zeit")
pdf("Bangkok_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bangkok_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bangkok["Sonnenaufgang"] <- strftime(Bangkok$sr, format="%H:%M")
Bangkok["Sonnenuntergang"] <- strftime(Bangkok$ss, format="%H:%M")
write.table(Bangkok, file="Bangkok_SaSu.csv", dec=',', sep=';', row.names=FALSE)