Compute minimum temperature indicators from grouped data

The function computes minimum temperature indicators from grouped data. Expects temperature in celsius degrees.

Usage

summarise_temp_min(.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 cold 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 cw3 and cw5 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

• cold_spells_3d Count of cold spells occurences, with 3 or more consecutive days with minimum temperature bellow the climatological normal value minus 5 celsius degrees

• cold_spells_5d Count of cold spells occurences, with 5 or more consecutive days with minimum temperature bellow the climatological normal value minus 5 celsius degrees

• cold_days Count of cold days, when the minimum temperature is bellow the normal 10th percentile

• t_0 Count of days with temperatures bellow or equal to 0 celsius degrees

• t_5 Count of days with temperatures bellow or equal to 5 celsius degrees

• t_10 Count of days with temperatures bellow or equal to 10 celsius degrees

• t_15 Count of days with temperatures bellow or equal to 15 celsius degrees

• t_20 Count of days with temperatures bellow or equal to 20 celsius degrees

Examples

# Compute monthly normals
normals <- temp_min_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
temp_min_data |>
# Create wave variables
dplyr::group_by(code_muni) |>
   add_wave(
     normals_df = normals,
     threshold = -5,
     threshold_cond = "lte",
     size = 3,
     var_name = "cw3"
   ) |>
   add_wave(
     normals_df = normals,
     threshold = -5,
     threshold_cond = "lte",
     size = 5,
     var_name = "cw5"
   ) |>
   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 minimum temperature indicators
 summarise_temp_min(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)