library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
26465 <- sunrise.set(53.74736919999999,7.492568200000001, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 26465$sunrise
sunset <- 26465$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
26465["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
26465["ss"] <- as.POSIXct(sunset, format = "%H:%M")
26465["timestamp"] <- align.time(26465$sunrise, 60*10)
26465 <- 26465[c("timestamp", "sr", "ss")]
locsrss <- ggplot(26465, aes(x=26465$timestamp)) + geom_line(aes(y=26465$sr)) + geom_line(aes(y=26465$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 26465 2023", x = "Datum", y = "Zeit")
pdf("26465_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="26465_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
26465["Sonnenaufgang"] <- strftime(26465$sr, format="%H:%M")
26465["Sonnenuntergang"] <- strftime(26465$ss, format="%H:%M")
write.table(26465, file="26465_SaSu.csv", dec=',', sep=';', row.names=FALSE)