library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
15848 <- sunrise.set(52.15110929999999,14.1489597, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 15848$sunrise
sunset <- 15848$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
15848["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
15848["ss"] <- as.POSIXct(sunset, format = "%H:%M")
15848["timestamp"] <- align.time(15848$sunrise, 60*10)
15848 <- 15848[c("timestamp", "sr", "ss")]
locsrss <- ggplot(15848, aes(x=15848$timestamp)) + geom_line(aes(y=15848$sr)) + geom_line(aes(y=15848$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 15848 2023", x = "Datum", y = "Zeit")
pdf("15848_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="15848_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
15848["Sonnenaufgang"] <- strftime(15848$sr, format="%H:%M")
15848["Sonnenuntergang"] <- strftime(15848$ss, format="%H:%M")
write.table(15848, file="15848_SaSu.csv", dec=',', sep=';', row.names=FALSE)