library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
16348_wandlitz <- sunrise.set(52.75422,13.472769999999969, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 16348_wandlitz$sunrise
sunset <- 16348_wandlitz$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
16348_wandlitz["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
16348_wandlitz["ss"] <- as.POSIXct(sunset, format = "%H:%M")
16348_wandlitz["timestamp"] <- align.time(16348_wandlitz$sunrise, 60*10)
16348_wandlitz <- 16348_wandlitz[c("timestamp", "sr", "ss")]
locsrss <- ggplot(16348_wandlitz, aes(x=16348_wandlitz$timestamp)) + geom_line(aes(y=16348_wandlitz$sr)) + geom_line(aes(y=16348_wandlitz$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 16348_wandlitz 2023", x = "Datum", y = "Zeit")
pdf("16348_wandlitz_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="16348_wandlitz_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
16348_wandlitz["Sonnenaufgang"] <- strftime(16348_wandlitz$sr, format="%H:%M")
16348_wandlitz["Sonnenuntergang"] <- strftime(16348_wandlitz$ss, format="%H:%M")
write.table(16348_wandlitz, file="16348_wandlitz_SaSu.csv", dec=',', sep=';', row.names=FALSE)