library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Dakar <- sunrise.set(14.7645042,-17.366028599999936, "2022/01/01", timezone="MET", num.days=370)
sunrise <- Dakar$sunrise
sunset <- Dakar$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Dakar["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Dakar["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Dakar["timestamp"] <- align.time(Dakar$sunrise, 60*10)
Dakar <- Dakar[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Dakar, aes(x=Dakar$timestamp)) + geom_line(aes(y=Dakar$sr)) + geom_line(aes(y=Dakar$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Dakar 2022", x = "Datum", y = "Zeit")
pdf("Dakar_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Dakar_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Dakar["Sonnenaufgang"] <- strftime(Dakar$sr, format="%H:%M")
Dakar["Sonnenuntergang"] <- strftime(Dakar$ss, format="%H:%M")
write.table(Dakar, file="Dakar_SaSu.csv", dec=',', sep=';', row.names=FALSE)