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Calculate DPS reuse and end of pipe loads and summarize

Usage

anlz_dps(
  fls,
  summ = c("entity", "facility", "segment", "all"),
  summtime = c("month", "year")
)

Arguments

fls

vector of file paths to raw entity data, one to many

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 loading data for TP, TN, TSS, and BOD as tons per month/year and hydro load as million cubic meters per month/year

Details

Input data files in fls are first processed by anlz_dps_facility to calculate DPS reuse and end of pipe for each facility and outfall. 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_dom', full.names = TRUE)
anlz_dps(fls)
#> # A tibble: 108 × 10
#>     Year Month source   entity segment tn_load tp_load tss_load bod_load hy_load
#>    <int> <int> <chr>    <chr>  <chr>     <dbl>   <dbl>    <dbl>    <dbl>   <dbl>
#>  1  2017     1 DPS - e… Hills… Hillsb… 0.0924  1.32e-2 0.0388    0.0955  0.0704 
#>  2  2017     2 DPS - e… Hills… Hillsb… 0.105   1.79e-2 0.0497    0.182   0.0901 
#>  3  2017     3 DPS - e… Hills… Hillsb… 0.0167  2.46e-3 0.00647   0.0127  0.0117 
#>  4  2017     4 DPS - e… Hills… Hillsb… 0.00249 2.88e-4 0.000626  0.00175 0.00114
#>  5  2017     5 DPS - e… Hills… Hillsb… 0.0219  1.05e-2 0.0116    0.0396  0.0211 
#>  6  2017     6 DPS - e… Hills… Hillsb… 0.329   8.41e-2 0.134     0.281   0.238  
#>  7  2017     7 DPS - e… Hills… Hillsb… 0.215   5.95e-2 0.114     0.293   0.208  
#>  8  2017     8 DPS - e… Hills… Hillsb… 0.369   1.05e-1 0.188     0.493   0.341  
#>  9  2017     9 DPS - e… Hills… Hillsb… 0.603   1.51e-1 0.202     0.548   0.360  
#> 10  2017    10 DPS - e… Hills… Hillsb… 0.286   6.08e-2 0.113     0.371   0.204  
#> # ℹ 98 more rows