classify_DayTime {RchivalTag}

Classifying the time period of the day

Description

Classifying the time period of the day based on the timing of sunrise, sunset (and twilight events) or alternatively, geolocation estimates, as specified in get_DayTimeLimits, that allow their internal estimation during the function call.

Usage

classify_DayTime(pos, twilight.set="ast")

Arguments

pos

A data.frame pos with the columns sunrise, sunset, dawn.ast,/dawn.naut and dawn.ast/dawn.naut in POSIXct-format. Note that the expected twilight vector (suffix "ast" for astronomical dawn and dusks; vs suffix "naut" for nautical twilight events) is defined by the function's second argument twilight.set (see description below).

twilight.set

character string, indicating the type of twilight used for the long daytime classifcation: "ast" (default) for astronomical and "naut" for nautical twilight events with sun angles of 18 vs 12 below the horizon, respectvely. Corresponding (expected) dawn and dusk vector names are dawn.ast & dusk.ast vs dawn.naut & dusk.naut).

Value

The input data.frame pos extended by the time vectors daytime and daytime.long. In the former case, "Day" and "Night" periods are distinguished. In the latter case, "Day", "Night", "Dawn" and "Dusk".

Author(s)

Robert K. Bauer

References

Meeus, J. (1991) Astronomical Algorithms. Willmann-Bell, Inc.

See Also

sunriset, crepuscule, get_DayTimeLimits

Examples

#### example 1) estimate current times of dawn, sunrise, dusk and sunset in Mainz, Germany:
pos <- data.frame(Lat=8.2667, Lon=50)
pos$datetime <- strptime(Sys.Date(),"%Y-%m-%d")
get_DayTimeLimits(pos)

#### example 1b) classify current ime of the day in Mainz, Germany:
classify_DayTime(get_DayTimeLimits(pos))

## convert 1c) back-to-back histogram showing day vs night TAD frequencies:
### load sample depth and temperature time series data from miniPAT:
ts_file <- system.file("example_files/104659-Series.csv",package="RchivalTag")
ts_df <- read.table(ts_file, header = TRUE, sep = ",")
tad_breaks <- c(0, 2, 5, 10, 20, 50, 100, 200, 300, 400, 600, 2000)

ts_df$Lat <- 4; ts_df$Lon=42.5 ## required geolocations to estimate daytime
ts_df$datetime <- strptime(paste(ts_df$Day,ts_df$Time),"%d-%B-%Y %H:%M:%S")
head(ts_df)
ts_df2 <- classify_DayTime(get_DayTimeLimits(ts_df)) # estimate daytime
head(ts_df2)

ts2histos(ts_df2, tad_breaks = tad_breaks,split_by = "daytime")
hist_tad(ts_df2, bin_breaks = tad_breaks,split_by = "daytime", do_mid.ticks = FALSE)