Converts jet fuel type gasoline into motor gasoline

This function converts flows of the "Gasoline type jet fuel" product into flows of "Motor gasoline excl. biofuels". The function then gathers those flows into a single new flow. Basically, it aggregates those two products and keeps the "Motor gasoline excl. biofuels" product name.

convert_fuel_gasoline_into_motor_gasoline(
  .tidy_iea_df,
  product = IEATools::iea_cols$product,
  flow = IEATools::iea_cols$flow,
  e_dot = IEATools::iea_cols$e_dot
)

Arguments

.tidy_iea_df

The .tidy_iea_df for which jet fuel type gasoline needs to be converted into motor gasoline.

product, e_dot, flow

See IEATools::iea_cols.

Value

A .tidy_iea_df for which jet fuel type gasoline is converted into motor gasoline.

Examples

# Here we add a flow of "Gasoline type jet fuel":
tidy_AB_data %>%
tibble::add_row(
 Country = "A",
 Method = "PCM",
 Energy.type = "E",
 Last.stage = "Final",
 Year = 2018,
 Product = "Gasoline type jet fuel",
 Ledger.side = "Consumption",
 Flow.aggregation.point = "Industry",
 Flow = "Iron and steel",
 Unit = "ktoe",
 E.dot = 20
) %>%
 dplyr::filter(Country == "A" & stringr::str_detect(Product, "(G|g)asoline")) %>%
 print()
#> # A tibble: 10 × 11
#>    Country Method Energy.type Last.stage  Year Ledger.side
#>    <chr>   <chr>  <chr>       <chr>      <dbl> <chr>      
#>  1 A       PCM    E           Final       2018 Consumption
#>  2 A       PCM    E           Final       2018 Consumption
#>  3 A       PCM    E           Final       2018 Consumption
#>  4 A       PCM    E           Final       2018 Supply     
#>  5 A       PCM    E           Final       2018 Supply     
#>  6 A       PCM    E           Final       2018 Supply     
#>  7 A       PCM    E           Final       2018 Supply     
#>  8 A       PCM    E           Final       2018 Supply     
#>  9 A       PCM    E           Final       2018 Supply     
#> 10 A       PCM    E           Final       2018 Consumption
#> # ℹ 5 more variables: Flow.aggregation.point <chr>, Flow <chr>, Product <chr>,
#> #   Unit <chr>, E.dot <dbl>
# Then we gather both flows:
tidy_AB_data %>%
tibble::add_row(
 Country = "A",
 Method = "PCM",
 Energy.type = "E",
 Last.stage = "Final",
 Year = 2018,
 Product = "Gasoline type jet fuel",
 Ledger.side = "Consumption",
 Flow.aggregation.point = "Industry",
 Flow = "Iron and steel",
 Unit = "ktoe",
 E.dot = 20
) %>%
 dplyr::filter(Country == "A" & stringr::str_detect(Product, "(G|g)asoline")) %>%
 convert_fuel_gasoline_into_motor_gasoline() %>%
 print()
#> # A tibble: 9 × 11
#> # Groups:   Country, Method, Energy.type, Last.stage, Year, Ledger.side,
#> #   Flow.aggregation.point, Flow, Product [9]
#>   Country Method Energy.type Last.stage  Year Ledger.side Flow.aggregation.point
#>   <chr>   <chr>  <chr>       <chr>      <dbl> <chr>       <chr>                 
#> 1 A       PCM    E           Final       2018 Consumption Industry              
#> 2 A       PCM    E           Final       2018 Consumption Other                 
#> 3 A       PCM    E           Final       2018 Consumption Transport             
#> 4 A       PCM    E           Final       2018 Supply      Energy industry own u…
#> 5 A       PCM    E           Final       2018 Supply      Energy industry own u…
#> 6 A       PCM    E           Final       2018 Supply      Energy industry own u…
#> 7 A       PCM    E           Final       2018 Supply      Energy industry own u…
#> 8 A       PCM    E           Final       2018 Supply      Transformation proces…
#> 9 A       PCM    E           Final       2018 Supply      Transformation proces…
#> # ℹ 4 more variables: Flow <chr>, Product <chr>, Unit <chr>, E.dot <dbl>