library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
58285 <- sunrise.set(51.3370571,7.3522193, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 58285$sunrise
sunset <- 58285$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
58285["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
58285["ss"] <- as.POSIXct(sunset, format = "%H:%M")
58285["timestamp"] <- align.time(58285$sunrise, 60*10)
58285 <- 58285[c("timestamp", "sr", "ss")]
locsrss <- ggplot(58285, aes(x=58285$timestamp)) + geom_line(aes(y=58285$sr)) + geom_line(aes(y=58285$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 58285 2023", x = "Datum", y = "Zeit")
pdf("58285_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="58285_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
58285["Sonnenaufgang"] <- strftime(58285$sr, format="%H:%M")
58285["Sonnenuntergang"] <- strftime(58285$ss, format="%H:%M")
write.table(58285, file="58285_SaSu.csv", dec=',', sep=';', row.names=FALSE)