library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
76547_DE <- sunrise.set(48.7634291,8.1482637, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 76547_DE$sunrise
sunset <- 76547_DE$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
76547_DE["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
76547_DE["ss"] <- as.POSIXct(sunset, format = "%H:%M")
76547_DE["timestamp"] <- align.time(76547_DE$sunrise, 60*10)
76547_DE <- 76547_DE[c("timestamp", "sr", "ss")]
locsrss <- ggplot(76547_DE, aes(x=76547_DE$timestamp)) + geom_line(aes(y=76547_DE$sr)) + geom_line(aes(y=76547_DE$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 76547_DE 2023", x = "Datum", y = "Zeit")
pdf("76547_DE_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="76547_DE_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
76547_DE["Sonnenaufgang"] <- strftime(76547_DE$sr, format="%H:%M")
76547_DE["Sonnenuntergang"] <- strftime(76547_DE$ss, format="%H:%M")
write.table(76547_DE, file="76547_DE_SaSu.csv", dec=',', sep=';', row.names=FALSE)