library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
01900 <- sunrise.set(51.1435347,14.041176599999972, "2022/01/01", timezone="MET", num.days=370)
sunrise <- 01900$sunrise
sunset <- 01900$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
01900["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
01900["ss"] <- as.POSIXct(sunset, format = "%H:%M")
01900["timestamp"] <- align.time(01900$sunrise, 60*10)
01900 <- 01900[c("timestamp", "sr", "ss")]
locsrss <- ggplot(01900, aes(x=01900$timestamp)) + geom_line(aes(y=01900$sr)) + geom_line(aes(y=01900$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 01900 2022", x = "Datum", y = "Zeit")
pdf("01900_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="01900_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
01900["Sonnenaufgang"] <- strftime(01900$sr, format="%H:%M")
01900["Sonnenuntergang"] <- strftime(01900$ss, format="%H:%M")
write.table(01900, file="01900_SaSu.csv", dec=',', sep=';', row.names=FALSE)