library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
12355_de <- sunrise.set(52.40750329999999,13.497563899999932, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 12355_de$sunrise
sunset <- 12355_de$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
12355_de["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
12355_de["ss"] <- as.POSIXct(sunset, format = "%H:%M")
12355_de["timestamp"] <- align.time(12355_de$sunrise, 60*10)
12355_de <- 12355_de[c("timestamp", "sr", "ss")]
locsrss <- ggplot(12355_de, aes(x=12355_de$timestamp)) + geom_line(aes(y=12355_de$sr)) + geom_line(aes(y=12355_de$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 12355_de 2023", x = "Datum", y = "Zeit")
pdf("12355_de_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="12355_de_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
12355_de["Sonnenaufgang"] <- strftime(12355_de$sr, format="%H:%M")
12355_de["Sonnenuntergang"] <- strftime(12355_de$ss, format="%H:%M")
write.table(12355_de, file="12355_de_SaSu.csv", dec=',', sep=';', row.names=FALSE)