library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
01855 <- sunrise.set(50.97021104712801,14.264459609985352, "2022/01/01", timezone="MET", num.days=370)
sunrise <- 01855$sunrise
sunset <- 01855$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
01855["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
01855["ss"] <- as.POSIXct(sunset, format = "%H:%M")
01855["timestamp"] <- align.time(01855$sunrise, 60*10)
01855 <- 01855[c("timestamp", "sr", "ss")]
locsrss <- ggplot(01855, aes(x=01855$timestamp)) + geom_line(aes(y=01855$sr)) + geom_line(aes(y=01855$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 01855 2022", x = "Datum", y = "Zeit")
pdf("01855_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="01855_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
01855["Sonnenaufgang"] <- strftime(01855$sr, format="%H:%M")
01855["Sonnenuntergang"] <- strftime(01855$ss, format="%H:%M")
write.table(01855, file="01855_SaSu.csv", dec=',', sep=';', row.names=FALSE)