library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
29683 <- sunrise.set(52.90625098406812,9.772671909936548, "2022/01/01", timezone="MET", num.days=370)
sunrise <- 29683$sunrise
sunset <- 29683$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
29683["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
29683["ss"] <- as.POSIXct(sunset, format = "%H:%M")
29683["timestamp"] <- align.time(29683$sunrise, 60*10)
29683 <- 29683[c("timestamp", "sr", "ss")]
locsrss <- ggplot(29683, aes(x=29683$timestamp)) + geom_line(aes(y=29683$sr)) + geom_line(aes(y=29683$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 29683 2022", x = "Datum", y = "Zeit")
pdf("29683_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="29683_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
29683["Sonnenaufgang"] <- strftime(29683$sr, format="%H:%M")
29683["Sonnenuntergang"] <- strftime(29683$ss, format="%H:%M")
write.table(29683, file="29683_SaSu.csv", dec=',', sep=';', row.names=FALSE)