Compute precipitation indicators from grouped data

The function computes precipitation indicators from grouped data. Expects precipitation in millimeters (mm).

Usage

summarise_precipitation(.x, value_var, normals_df)

Arguments

.x

grouped data, created with dplyr::group_by()

value_var

name of the variable with temperature values.

normals_df

normals data, created with summarise_normal()

Value

A tibble.

Details

The rain spells indicators are computed based on climatological normals, created with the summarise_normal() function and passed with the normals_df argument. Keys to join the normals data must be present (like id, year, and month) and use the same names. The variables rs3 and rs5 must be present in the dataset. Those variables can be computed with the add_wave() function. Plase follow this function example for the correct arguments.

The following indicators are computed for each group.

• count Count of data points

• normal_mean Climatological normal mean, from normals_df argument

• normal_p10 Climatological 10th percentile, from normals_df argument

• normal_p90 Climatological 90th percentile, from normals_df argument

• mean Average

• median Median

• sd Standard deviation

• se Standard error

• max Maximum value

• min Minimum value

• p10 10th percentile

• p25 25th percentile

• p75 75th percentile

• p90 90th percentile

• rs3 Count of rain spells occurences, with 3 or more consecutive days with rain above the climatological normal average value

• rs5 Count of rain spells occurences, with 5 or more consecutive days with rain above the climatological normal average value

• p_1 Count of days with precipitation above 1mm

• p_5 Count of days with precipitation above 5mm

• p_10 Count of days with precipitation above 10mm

• p_50 Count of days with precipitation above 50mm

• p_100 Count of days with precipitation above 100mm

• d_3 Count of sequences of 3 days or more without precipitation

• d_5 Count of sequences of 5 days or more without precipitation

• d_10 Count of sequences of 10 days or more without precipitation

• d_15 Count of sequences of 15 days or more without precipitation

• d_20 Count of sequences of 20 days or more without precipitation

• d_25 Count of sequences of 25 days or more without precipitation

Examples

# Compute monthly normals
normals <- precipitation_data |>
  # Identify month
  dplyr::mutate(month = lubridate::month(date)) |>
  # Group by id variable and month
  dplyr::group_by(code_muni, month) |>
  summarise_normal(date_var = date, value_var = value, year_start = 1981, year_end = 2010) |>
  dplyr::ungroup()

# Compute indicators
precipitation_data |>
dplyr::filter(date >= as.Date("2011-01-01")) |>
# Create wave variables
dplyr::group_by(code_muni) |>
   add_wave(
     normals_df = normals,
     threshold = 0,
     threshold_cond = "gte",
     size = 3,
     var_name = "rs3"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = 0,
     threshold_cond = "gte",
     size = 5,
     var_name = "rs5"
   ) |>
   dplyr::ungroup() |>
 # Identify year
 dplyr::mutate(year = lubridate::year(date)) |>
 # Identify month
 dplyr::mutate(month = lubridate::month(date)) |>
 # Group by id variable, year and month
 dplyr::group_by(code_muni, year, month) |>
 # Compute precipitation indicators
 summarise_precipitation(value_var = value, normals_df = normals) |>
 # Ungroup
 dplyr::ungroup()
#> Error in purrr::map(.x = res, .f = iden): ℹ In index: 1.
#> Caused by error in `nseq::trle_cond()`:
#> ! unused argument (pos = TRUE)