Summarize point source load estimates

Summarize point source load estimates

Usage

util_ps_summ(
  dat,
  summ = c("entity", "facility", "segment", "all"),
  summtime = c("month", "year")
)

Arguments

dat

Pre-processed data frame of point source load estimates, see examples

summ

chr string indicating how the returned data are summarized, see details

summtime

chr string indicating how the returned data are summarized temporally (month or year), see details

Value

Data frame with summarized loading data based on user-supplied arguments

Details

The data are summarized differently based on the summ and summtime arguments. All loading data are summed based on these arguments, e.g., by bay segment (summ = 'segment') and year (summtime = 'year').

Examples

fls <- list.files(system.file('extdata/', package = 'tbeploads'),
  pattern = 'ps_ind_', full.names = TRUE)

ipsbyfac <- anlz_ips_facility(fls)

# add bay segment and source, there should only be loads to hills, middle, and lower tampa bay
ipsld <- ipsbyfac  |>
  dplyr::arrange(coastco) |>
  dplyr::left_join(dbasing, by = "coastco") |>
  dplyr::mutate(
    segment = dplyr::case_when(
      bayseg == 1 ~ "Old Tampa Bay",
      bayseg == 2 ~ "Hillsborough Bay",
      bayseg == 3 ~ "Middle Tampa Bay",
      bayseg == 4 ~ "Lower Tampa Bay",
      TRUE ~ NA_character_
    ),
    source = 'IPS'
  ) |>
  dplyr::select(-basin, -hectare, -coastco, -name, -bayseg)

util_ps_summ(ipsld, summ = 'entity', summtime = 'year')
#> # A tibble: 5 × 9
#>    Year source entity         segment  tn_load tp_load tss_load bod_load hy_load
#>   <int> <chr>  <chr>          <chr>      <dbl>   <dbl>    <dbl>    <dbl>   <dbl>
#> 1  2020 IPS    Busch Gardens  Hillsbo…  0.437   0.0858   6.11       11.7  1.11  
#> 2  2021 IPS    Coronet        Hillsbo…  0.0305  0.0515   0.0662      0    0.0184
#> 3  2017 IPS    Lowry Park Zoo Hillsbo…  0.215   0.0612   0           0    0.188 
#> 4  2018 IPS    Lowry Park Zoo Hillsbo…  0.168   0.0456   0           0    0.140 
#> 5  2019 IPS    Lowry Park Zoo Hillsbo…  0.0950  0.0226   0           0    0.0763