library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
32052 <- sunrise.set(52.0919704,8.650065799999993, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 32052$sunrise
sunset <- 32052$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
32052["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
32052["ss"] <- as.POSIXct(sunset, format = "%H:%M")
32052["timestamp"] <- align.time(32052$sunrise, 60*10)
32052 <- 32052[c("timestamp", "sr", "ss")]
locsrss <- ggplot(32052, aes(x=32052$timestamp)) + geom_line(aes(y=32052$sr)) + geom_line(aes(y=32052$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 32052 2023", x = "Datum", y = "Zeit")
pdf("32052_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="32052_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
32052["Sonnenaufgang"] <- strftime(32052$sr, format="%H:%M")
32052["Sonnenuntergang"] <- strftime(32052$ss, format="%H:%M")
write.table(32052, file="32052_SaSu.csv", dec=',', sep=';', row.names=FALSE)