# average-daily

The msdrought package is designed to analyze one year of data at a time. For the AGU ’23 conference, a graphic was made that showcased the trends of the average rainfall across a set of years. Each day’s rainfall data across multiple years were averaged, then analyzed. For example, in a data set with precipitation data from 1981 to 1985, every day of the year (from January 1st to December 31st) had its rainfall data averaged (example: the precipitation values of 1-1-81, 1-1-82, 1-1-83, 1-1-84, and 1-1-85 were averaged), then put into a dummy xts to be analyzed. This vignette shows how that graphic was created.

Packages used in this vignette: `terra`, `lubridate`, `dplyr`, `xts`, and `msdrought`. These should all be installed.

The first step is to extract the relevant data from a SpatRaster.

    data <- system.file("extdata", "prcp_cropped.tif", package = "msdrought") # This loads the data included in the package, but you would attach your own
    infile <- terra::rast(data)

    # Make a SpatRast for one point
    lon <- -86.2621555581 # El Bramadero lon (from NicaAgua)
    lat <- 13.3816217871 # El Bramadero lat (from NicaAgua)
    lonLat <- data.frame(lon = lon, lat = lat)

    # Set up precipitation data
    location <- terra::vect(lonLat, crs = "+proj=longlat +datum=WGS84")
    precip <- terra::extract(infile, location, method = "bilinear") |>
      subset(select = -ID) |>
      t()
    precip[precip < 0] <- 0 # replace any negative (errant) values with zeroes
    precip <- as.vector(precip)

Because we will be using the msdGraph function, a year value will need to be provided. We will create a dummy year that will be used for the sake of plotting. This can be any year, but it is easiest to just use the first year in the data set.

    # Find the average for each day of year
    day_of_year <- lubridate::yday(terra::time(infile))
    df <- data.frame(doy = day_of_year, precip = precip) |>
      dplyr::group_by(doy) |>
      dplyr::summarize(averagePrecip = mean(precip))

Now, an averaged xts object can be created. This will display the data set’s average rainfall over the dummy year.

    # Assemble the Timeseries, creating a dummy date column for the averaged year
    ddates <- as.Date("1980-12-31") + unique(day_of_year)
    x <- xts::xts(df$averagePrecip, order.by = ddates)

Construct key date sequences and filter time series.

    # Perform MSD calculations with the new xts
    keyDatesTS <- msdrought::msdDates(time(x))
    filterTS <- msdrought::msdFilter(x, window = 31, quantity = 2)

Perform the MSD calculations.

    duration <- msdrought::msdStats(filterTS, keyDatesTS, fcn = "duration")

    intensity <- msdrought::msdStats(filterTS, keyDatesTS, fcn = "intensity")
    firstMaxValue <- msdrought::msdStats(filterTS, keyDatesTS, fcn = "firstMaxValue")
    secondMaxValue <- msdrought::msdStats(filterTS, keyDatesTS, fcn = "secondMaxValue")
    min <- msdrought::msdStats(filterTS, keyDatesTS, fcn = "min")
    allStats <- msdrought::msdMain(x)

Create a plot of the MSD pattern for the year.

    suppressWarnings(msdrought::msdGraph(x, 1981))