Compute relative humidity indicators from grouped data

The function computes relative humidity indicators from grouped data. Expects relative humidity in percentage.

Usage

summarise_rel_humidity(.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 dry and wet 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 ds3, ds5, ws3 and ws5 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

• ds3 Count of dry spells occurences, with 3 or more consecutive days with relative humidity bellow the climatological normal value minus 10 percent

• ds5 Count of dry spells occurences, with 5 or more consecutive days with relative humidity bellow the climatological normal value minus 10 percent

• ws3 Count of wet spells occurences, with 3 or more consecutive days with relative humidity above the climatological normal value plus 10 percent

• ws5 Count of wet spells occurences, with 5 or more consecutive days with relative humidity above the climatological normal value plus 10 percent

• dry_days Count of dry days, when the relative humidity is bellow the normal 10th percentile

• wet_days Count of wet days, when the relative humidity is above the normal 90th percentile

• h_21_30 Count of days with relative humidity between 21% and 30%. Attention level

• h_12_20 Count of days with relative humidity between 12% and 20%. Alert level

• h_11 Count of days with relative humidity bellow 12%. Emergence level

Examples

# Compute monthly normals
normals <- rel_humidity_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 = 1961, year_end = 1990) |>
  dplyr::ungroup()

# Compute indicators
rel_humidity_data |>
# Create wave variables
dplyr::group_by(code_muni) |>
   add_wave(
     normals_df = normals,
     threshold = -10,
     threshold_cond = "lte",
     size = 3,
     var_name = "ds3"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = -10,
     threshold_cond = "lte",
     size = 5,
     var_name = "ds5"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = 10,
     threshold_cond = "lte",
     size = 3,
     var_name = "ws3"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = 10,
     threshold_cond = "lte",
     size = 5,
     var_name = "ws5"
   ) |>
   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 relative humidity indicators
 summarise_rel_humidity(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)