At product level, all functions now output profile_ranking

[maurolepore]

Closes #549
Closes #581

This PR adds the column profile_ranking to all outputs at product level. At company level I believe it makes no sense, since the values of profile_ranking are more granular (@AnneSchoenauer please confirm).

---

TODO

• Link related issue/PR.
• Describe the goal of the PR. Avoid details that are clear in the diff.
• Mark the PR as draft.
• Review your own PR in "Files changed".
• Ensure the PR branch is updated.
• Ensure the checks pass.
• Change the status from draft to ready.
• Polish the PR title and description.
• Assign a reviewer.

EXCEPTIONS

• Slide here any item that you intentionally choose to not do.

• Include a unit test. Note the existing tests "outputs expected columns at product level". They already test that the output has columns that ultimately come from the same function: cols_at_product_level().

reprex

devtools::load_all()
#> ℹ Loading tiltIndicator

companies <- example_companies()
products <- example_products()
inputs <- example_inputs()
scenarios <- example_scenarios()

emissions_profile(companies, products) |> unnest_product()
#> # A tibble: 6 × 7
#>   companies_id grouped_by       risk_category profile_ranking clustered
#>   <chr>        <chr>            <chr>                   <dbl> <chr>    
#> 1 a            all              high                        1 a        
#> 2 a            isic_4digit      high                        1 a        
#> 3 a            tilt_sector      high                        1 a        
#> 4 a            unit             high                        1 a        
#> 5 a            unit_isic_4digit high                        1 a        
#> 6 a            unit_tilt_sector high                        1 a        
#> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <chr>

emissions_profile_upstream(companies, inputs) |> unnest_product()
#> # A tibble: 6 × 8
#>   companies_id grouped_by                risk_category profile_ranking clustered
#>   <chr>        <chr>                     <chr>                   <dbl> <chr>    
#> 1 a            all                       high                        1 a        
#> 2 a            input_isic_4digit         high                        1 a        
#> 3 a            input_tilt_sector         high                        1 a        
#> 4 a            input_unit                high                        1 a        
#> 5 a            input_unit_input_isic_4d… high                        1 a        
#> 6 a            input_unit_input_tilt_se… high                        1 a        
#> # ℹ 3 more variables: activity_uuid_product_uuid <chr>,
#> #   input_activity_uuid_product_uuid <chr>, input_co2_footprint <chr>

sector_profile(companies, scenarios) |> unnest_product()
#> # A tibble: 1 × 11
#>   companies_id grouped_by risk_category profile_ranking clustered
#>   <chr>        <chr>      <chr>         <chr>           <chr>    
#> 1 a            ipr_a_2050 high          1               a        
#> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>,
#> #   scenario <chr>, year <chr>, type <chr>, tilt_subsector <chr>

sector_profile_upstream(companies, scenarios, inputs) |> unnest_product()
#> # A tibble: 1 × 13
#>   companies_id grouped_by risk_category profile_ranking clustered
#>   <chr>        <chr>      <chr>         <chr>           <chr>    
#> 1 a            ipr_a_2050 high          1               a        
#> # ℹ 8 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>,
#> #   scenario <chr>, year <chr>, type <chr>,
#> #   input_activity_uuid_product_uuid <chr>, input_tilt_sector <chr>,
#> #   input_tilt_subsector <chr>

^{Created on 2023-11-14 with reprex v2.0.2}

[AnneSchoenauer]

Dear @maurolepore,

First I think to add the profile_ranking in those csv files are fine. Please note that you don't need to do it for the company dataset as you said.

Some questions:

• you only now chose one clustered "a" however of course at a later stage there will be many clustered right? And they will then of course have a different profile_ranking than 1. This was just the example that you chose right?
• In the emission_profile_upstream I noticed that the benchmark 'all' is not called 'input_all' - this would be more precise and consistent with the others
• In the sector profiles there are also more groups right? I am missing scenario ipr_2030, scenario weo_2030 and weo_2050. Please note that it can be that the data that Kalash will give you that the variable is still called "reductions" as the profile_ranking is the same as the reductions column in my understanding.

[maurolepore]

Thanks @AnneSchoenauer for your feedback.

Yes, the reprex above was extremely minimal to clearly show the new colum profile_ranking.

To see how it varies across rows we need a more complex reprex. Here I use our toy datasets. I think it answers your questions 1 and 3.

RE the inconsistency across the values of grouped_by for emissions profile (i.e. benchmarks) -- note that grouped_by simply stores the names of the columns we we group by (i.e. benchmarks). If the columns don't use the annoying prefix "input_" then there is no inconsistency.

My suggestion is to remove the "input_" prefix. If anyone wonders where the columns come from (products or inputs), they can simply see the name of the dataset, e.g. products$isic_4digit versus inputs$isic_4digit. I estimate the "input_" prefix already costed at least 1 week of developer time (adding it, removing it, adding it again, discussing it, working around it, debugging it).

library(tiltToyData)
library(readr, warn.conflicts = FALSE)
options(readr.show_col_types = FALSE, width = 500)

# PR tiltIndicator #613
devtools::load_all()
#> ℹ Loading tiltIndicator

companies <- read_csv(toy_emissions_profile_any_companies())
products <- read_csv(toy_emissions_profile_products())
emissions_profile(companies, products) |> unnest_product()
#> # A tibble: 49 × 7
#>    companies_id                             grouped_by       risk_category profile_ranking clustered activity_uuid_product_uuid                                                co2_footprint
#>    <chr>                                    <chr>            <chr>                   <dbl> <chr>     <chr>                                                                             <dbl>
#>  1 fleischerei-stiefsohn_00000005219477-001 all              high                    1     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa         176. 
#>  2 fleischerei-stiefsohn_00000005219477-001 isic_4digit      high                    1     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa         176. 
#>  3 fleischerei-stiefsohn_00000005219477-001 tilt_sector      high                    1     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa         176. 
#>  4 fleischerei-stiefsohn_00000005219477-001 unit             high                    1     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa         176. 
#>  5 fleischerei-stiefsohn_00000005219477-001 unit_isic_4digit high                    1     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa         176. 
#>  6 fleischerei-stiefsohn_00000005219477-001 unit_tilt_sector high                    1     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa         176. 
#>  7 fleischerei-stiefsohn_00000005219477-001 all              high                    0.8   oven      be06d25c-73dc-55fb-965b-0f300453e380_98b48ff2-2200-4b08-9dec-9c7c0e3585bc          58.1
#>  8 fleischerei-stiefsohn_00000005219477-001 isic_4digit      medium                  0.5   oven      be06d25c-73dc-55fb-965b-0f300453e380_98b48ff2-2200-4b08-9dec-9c7c0e3585bc          58.1
#>  9 fleischerei-stiefsohn_00000005219477-001 tilt_sector      medium                  0.667 oven      be06d25c-73dc-55fb-965b-0f300453e380_98b48ff2-2200-4b08-9dec-9c7c0e3585bc          58.1
#> 10 fleischerei-stiefsohn_00000005219477-001 unit             medium                  0.5   oven      be06d25c-73dc-55fb-965b-0f300453e380_98b48ff2-2200-4b08-9dec-9c7c0e3585bc          58.1
#> # ℹ 39 more rows

companies <- read_csv(toy_emissions_profile_any_companies())
products <- read_csv(toy_emissions_profile_products())
inputs <- read_csv(toy_emissions_profile_upstream_products())
emissions_profile_upstream(companies, inputs) |> unnest_product()
#> # A tibble: 319 × 8
#>    companies_id                             grouped_by        risk_category profile_ranking clustered activity_uuid_product_uuid                                                input_activity_uuid_product_uuid                                     input_co2_footprint
#>    <chr>                                    <chr>             <chr>                   <dbl> <chr>     <chr>                                                                     <chr>                                                                              <dbl>
#>  1 fleischerei-stiefsohn_00000005219477-001 all               high                   0.909  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5             7.07e+0
#>  2 fleischerei-stiefsohn_00000005219477-001 all               high                   1      stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 1aeb18b9-8355-560f-82aa-543c771c4d61_a0e53510-b90b-43ba-80cc-7600f5d             3.99e+1
#>  3 fleischerei-stiefsohn_00000005219477-001 all               medium                 0.636  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 22704506-7707-5ae7-990d-ebf01ac04fb5_50c41012-3b00-429d-ace3-40d0                5.12e-1
#>  4 fleischerei-stiefsohn_00000005219477-001 all               high                   0.758  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 92078219-1ed3-5215-9f70-931cdefad520_5c21b18e-e32d-4c76-8d16-2238632             1.24e+0
#>  5 fleischerei-stiefsohn_00000005219477-001 all               high                   0.970  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 9d483329-b09a-5513-b1bc-722cb211e928_bd4dca-497e-bdd9-fcd343012087               2.12e+1
#>  6 fleischerei-stiefsohn_00000005219477-001 all               low                    0.0909 stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 8709b463-732e-592e-9b88-999ed17af48f_6b6b3a15-e0-baea-cda98afc61c2               1.24e-9
#>  7 fleischerei-stiefsohn_00000005219477-001 all               low                    0.121  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa d44e7db1-4dda-51ed2929a8f1a2_32e60fbc-4778-470c-9653-feb859a3418f                7   e-9
#>  8 fleischerei-stiefsohn_00000005219477-001 all               high                   0.697  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 7c7718bb-2372-5d04-a7ac-1ae5b12b05e3_61396bcb-bf35-411a-a6a6-85e8                1.04e+0
#>  9 fleischerei-stiefsohn_00000005219477-001 input_isic_4digit high                   0.857  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5             7.07e+0
#> 10 fleischerei-stiefsohn_00000005219477-001 input_isic_4digit high                   1      stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa 1aeb18b9-8355-560f-82aa-543c771c4d61_a0e53510-b90b-43ba-80cc-7600f5d             3.99e+1
#> # ℹ 309 more rows

companies <- read_csv(toy_sector_profile_companies())
scenarios <- read_csv(toy_sector_profile_any_scenarios())
sector_profile(companies, scenarios) |> unnest_product()
#> # A tibble: 196 × 11
#>    companies_id                             grouped_by                                   risk_category profile_ranking clustered activity_uuid_product_uuid                                   tilt_sector scenario                             year type  tilt_subsector
#>    <chr>                                    <chr>                                        <chr>                   <dbl> <chr>     <chr>                                                        <chr>       <chr>                               <dbl> <chr> <chr>         
#>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2030                            high                   0.23   steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        1.5c rps                             2030 ipr   <NA>          
#>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2050                            high                   0.96   steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        1.5c rps                             2050 ipr   <NA>          
#>  3 fleischerei-stiefsohn_00000005219477-001 weo_stated policies scenario_2020            low                    0      steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        stated policies scenario             2020 weo   <NA>          
#>  4 fleischerei-stiefsohn_00000005219477-001 weo_announced pledges scenario_2020          low                    0      steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        announced pledges scenario           2020 weo   <NA>          
#>  5 fleischerei-stiefsohn_00000005219477-001 weo_net zero emissions by 2050 scenario_2020 low                    0      steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        net zero emissions by 2050 scenario  2020 weo   <NA>          
#>  6 fleischerei-stiefsohn_00000005219477-001 weo_stated policies scenario_2030            low                   -0.0752 steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        stated policies scenario             2030 weo   <NA>          
#>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced pledges scenario_2030          low                    0.0781 steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        announced pledges scenario           2030 weo   <NA>          
#>  8 fleischerei-stiefsohn_00000005219477-001 weo_net zero emissions by 2050 scenario_2030 high                   0.233  steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        net zero emissions by 2050 scenario  2030 weo   <NA>          
#>  9 fleischerei-stiefsohn_00000005219477-001 weo_stated policies scenario_2040            low                   -0.0270 steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        stated policies scenario             2040 weo   <NA>          
#> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced pledges scenario_2040          medium                 0.336  steel     0faa7ecb-fef2-5117-8993-387c1236-001e-49b5-aa3d-810c0214f9ce <NA>        announced pledges scenario           2040 weo   <NA>          
#> # ℹ 186 more rows

companies <- read_csv(toy_sector_profile_upstream_companies())
scenarios <- read_csv(toy_sector_profile_any_scenarios())
inputs <- read_csv(toy_sector_profile_upstream_products())
sector_profile_upstream(companies, scenarios, inputs) |> unnest_product()
#> # A tibble: 704 × 13
#>    companies_id                             grouped_by                                   risk_category profile_ranking clustered activity_uuid_product_uuid                                                tilt_sector scenario                             year type  input_activity_uuid_product_uuid                                          input_tilt_sector     input_tilt_subsector
#>    <chr>                                    <chr>                                        <chr>                   <dbl> <chr>     <chr>                                                                     <chr>       <chr>                               <dbl> <chr> <chr>                                                                     <chr>                 <chr>               
#>  1 fleischerei-stiefsohn_00000005219477-001 weo_stated policies scenario_2020            low                     0     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      stated policies scenario             2020 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  2 fleischerei-stiefsohn_00000005219477-001 weo_stated policies scenario_2030            low                    -0.192 stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      stated policies scenario             2030 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  3 fleischerei-stiefsohn_00000005219477-001 weo_stated policies scenario_2040            low                    -0.517 stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      stated policies scenario             2040 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  4 fleischerei-stiefsohn_00000005219477-001 weo_stated policies scenario_2050            low                    -0.689 stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      stated policies scenario             2050 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  5 fleischerei-stiefsohn_00000005219477-001 weo_announced pledges scenario_2020          low                     0     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      announced pledges scenario           2020 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  6 fleischerei-stiefsohn_00000005219477-001 weo_announced pledges scenario_2030          high                    0.301 stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      announced pledges scenario           2030 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced pledges scenario_2040          high                    1.83  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      announced pledges scenario           2040 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced pledges scenario_2050          high                    3.17  stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      announced pledges scenario           2050 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#>  9 fleischerei-stiefsohn_00000005219477-001 weo_net zero emissions by 2050 scenario_2020 low                     0     stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      net zero emissions by 2050 scenario  2020 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#> 10 fleischerei-stiefsohn_00000005219477-001 weo_net zero emissions by 2050 scenario_2030 high                    0.909 stove     0a242b09-772a-5edf-8e82-9cb4ba52a258_ae39ee61-d4d0-4cce-93b4-0745344da5fa energy      net zero emissions by 2050 scenario  2030 weo   5de8c337-dea9-5c1f-9d90-002de27188be_8911bd8c-a96f-4440-9f8e-a7dacf5e79de non-metallic minerals raw minerals        
#> # ℹ 694 more rows

^{Created on 2023-11-16 with reprex v2.0.2}