library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
10437 <- sunrise.set(52.5446671,13.414625, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 10437$sunrise
sunset <- 10437$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
10437["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
10437["ss"] <- as.POSIXct(sunset, format = "%H:%M")
10437["timestamp"] <- align.time(10437$sunrise, 60*10)
10437 <- 10437[c("timestamp", "sr", "ss")]
locsrss <- ggplot(10437, aes(x=10437$timestamp)) + geom_line(aes(y=10437$sr)) + geom_line(aes(y=10437$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 10437 2023", x = "Datum", y = "Zeit")
pdf("10437_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="10437_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
10437["Sonnenaufgang"] <- strftime(10437$sr, format="%H:%M")
10437["Sonnenuntergang"] <- strftime(10437$ss, format="%H:%M")
write.table(10437, file="10437_SaSu.csv", dec=',', sep=';', row.names=FALSE)