Get time related information

Source: R/timeInfo.R

Calculate the local time at the target location, as well as sunrise, sunset and solar noon times, and create several temporal masks.

If the timezone is provided it will be used. Otherwise, the MazamaSpatialUtils package will be used to determine the timezone from longitude and latitude.

The returned dataframe will have as many rows as the length of the incoming UTC time vector and will contain the following columns:

  • localStdTime_UTC -- UTC representation of local standard time

  • daylightSavings -- logical mask = TRUE if daylight savings is in effect

  • localTime -- local clock time

  • sunrise -- time of sunrise on each localTime day

  • sunset -- time of sunset on each localTime day

  • solarnoon -- time of solar noon on each localTime day

  • day -- logical mask = TRUE between sunrise and sunset

  • morning -- logical mask = TRUE between sunrise and solarnoon

  • afternoon -- logical mask = TRUE between solarnoon and sunset

  • night -- logical mask = opposite of day

timeInfo(time = NULL, longitude = NULL, latitude = NULL, timezone = NULL)

Arguments

time

POSIXct vector with specified timezone,
longitude

Longitude of the location of interest.
latitude

Latitude of the location of interest.
timezone

Olson timezone at the location of interest.

Value

A dataframe with times and masks.

Details

While the lubridate package makes it easy to work in local timezones, there is no easy way in R to work in "Local Standard Time" (LST) as is often required when working with air qualitiy data. EPA regulations mandate that daily averages be calculated based on LST.

The localStdTime_UTC column in the returned dataframe is primarily for internal use and provides an important tool for creating LST daily averages and LST axis labeling.

Examples

carmel <- monitor_subset(Carmel_Valley, tlim = c(20160801,20160810))

# Create timeInfo object for this monitor
ti <- timeInfo(
  carmel$data$datetime,
  carmel$meta$longitude,
  carmel$meta$latitude,
  carmel$meta$timezone
)

# Subset the data based on day/night masks
data_day <- carmel$data[ti$day,]
data_night <- carmel$data[ti$night,]

# Build two monitor objects
carmel_day <- list(meta = carmel$meta, data = data_day)
carmel_night <- list(meta = carmel$meta, data = data_night)

# Plot them
monitor_timeseriesPlot(carmel_day, shadedNight = TRUE, pch = 8, col = 'goldenrod')

monitor_timeseriesPlot(carmel_night, pch = 16, col = 'darkblue', add = TRUE)