Skip to contents

Compute relative humidity indicators from grouped data

Source: R/summarise_rel_humidity.R
summarise_rel_humidity.Rd

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 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

  • dry_spells_3d Count of dry spells occurences, with 3 or more consecutive days with relative humidity bellow the climatological normal value minus 10 percent

  • dry_spells_5d Count of dry spells occurences, with 5 or more consecutive days with relative humidity bellow the climatological normal value minus 10 percent

  • wet_spells_3d Count of wet spells occurences, with 3 or more consecutive days with relative humidity above the climatological normal value plus 10 percent

  • wet_spells_5d 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 |>
 # 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()
#> # A tibble: 3,024 × 26
#>    code_muni  year month count normal_mean normal_p10 normal_p90  mean median
#>        <int> <dbl> <dbl> <int>       <dbl>      <dbl>      <dbl> <dbl>  <dbl>
#>  1   3106200  1961     1    31        78.4       65.9       90.4  88.0   88.8
#>  2   3106200  1961     2    28        77.4       68.3       88.3  80.6   80.1
#>  3   3106200  1961     3    31        77.2       68.6       87.2  78.9   79.0
#>  4   3106200  1961     4    30        77.0       69.8       84.5  76.1   74.9
#>  5   3106200  1961     5    31        76.1       68.5       83.6  76.8   76.8
#>  6   3106200  1961     6    30        74.8       68.1       81.3  75.6   76.0
#>  7   3106200  1961     7    31        72.3       64.6       79.9  69.7   69.9
#>  8   3106200  1961     8    31        67.2       57.5       77.5  61.0   60.5
#>  9   3106200  1961     9    30        67.2       53.8       81.5  57.6   56.8
#> 10   3106200  1961    10    31        72.2       58.2       85.9  65.3   63.3
#> # ℹ 3,014 more rows
#> # ℹ 17 more variables: sd <dbl>, se <dbl>, max <dbl>, min <dbl>, p10 <dbl>,
#> #   p25 <dbl>, p75 <dbl>, p90 <dbl>, dry_spells_3d <int>, dry_spells_5d <int>,
#> #   wet_spells_3d <int>, wet_spells_5d <int>, dry_days <int>, wet_days <int>,
#> #   h_21_30 <int>, h_12_20 <int>, h_11 <int>