library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
38106 <- sunrise.set(52.28023109999999,10.5390217, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 38106$sunrise
sunset <- 38106$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
38106["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
38106["ss"] <- as.POSIXct(sunset, format = "%H:%M")
38106["timestamp"] <- align.time(38106$sunrise, 60*10)
38106 <- 38106[c("timestamp", "sr", "ss")]
locsrss <- ggplot(38106, aes(x=38106$timestamp)) + geom_line(aes(y=38106$sr)) + geom_line(aes(y=38106$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 38106 2023", x = "Datum", y = "Zeit")
pdf("38106_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="38106_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
38106["Sonnenaufgang"] <- strftime(38106$sr, format="%H:%M")
38106["Sonnenuntergang"] <- strftime(38106$ss, format="%H:%M")
write.table(38106, file="38106_SaSu.csv", dec=',', sep=';', row.names=FALSE)