Skip to contents

Compute windspeed indicators from grouped data

Source: R/summarise_windspeed.R
summarise_windspeed.Rd

The function computes windspeed (u2) indicators from grouped data. Expects windspeed in kilometers per hour (km/h). 1 m/s equals to 3.6 km/h.

Usage

summarise_windspeed(.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 high and low u2 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 l_u2_3, l_u2_5, h_u2_3 and h_u2_5 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

  • l_eto_3 Count of sequences of 3 days or more with windspeed bellow the climatological normal

  • l_eto_5 Count of sequences of 5 days or more with windspeed bellow the climatological normal

  • h_eto_3 Count of sequences of 3 days or more with windspeed above the climatological normal

  • h_eto_5 Count of sequences of 5 days or more with windspeed above the climatological normal

Examples

# Compute monthly normals
normals <- windspeed_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) |>
  summarise_normal(date_var = date, value_var = value, year_start = 1961, year_end = 1990) |>
  dplyr::ungroup()

# Compute indicators
windspeed_data |>
# Create wave variables
dplyr::group_by(code_muni) |>
   add_wave(
     normals_df = normals,
     threshold = 0,
     threshold_cond = "lte",
     size = 3,
     var_name = "l_u2_3"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = 0,
     threshold_cond = "lte",
     size = 5,
     var_name = "l_u2_5"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = 0,
     threshold_cond = "gte",
     size = 3,
     var_name = "h_u2_3"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = 0,
     threshold_cond = "gte",
     size = 5,
     var_name = "h_u2_5"
   ) |>
   dplyr::ungroup() |>
 # Identify month
 dplyr::mutate(month = lubridate::month(date)) |>
 # Group by id variable and month
 dplyr::group_by(code_muni, month) |>
 # Compute windspeed indicators
 summarise_windspeed(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)