library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
88131 <- sunrise.set(47.56395951534652,9.702901840209961, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 88131$sunrise
sunset <- 88131$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
88131["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
88131["ss"] <- as.POSIXct(sunset, format = "%H:%M")
88131["timestamp"] <- align.time(88131$sunrise, 60*10)
88131 <- 88131[c("timestamp", "sr", "ss")]
locsrss <- ggplot(88131, aes(x=88131$timestamp)) + geom_line(aes(y=88131$sr)) + geom_line(aes(y=88131$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 88131 2023", x = "Datum", y = "Zeit")
pdf("88131_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="88131_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
88131["Sonnenaufgang"] <- strftime(88131$sr, format="%H:%M")
88131["Sonnenuntergang"] <- strftime(88131$ss, format="%H:%M")
write.table(88131, file="88131_SaSu.csv", dec=',', sep=';', row.names=FALSE)