The `emissions*()` functions preserve unmatched products and missing benchmarks #639

maurolepore · 2023-12-12T13:22:50Z

Relates to

NAs in benchmark-columns should yield NAs in risk_category (except "all") #638
emissions/sector_profile() at product level should preserve unmatched products, filling with NAs #657

Conflicts with

--

This PR focuses on emissions_profile*(). (For sector_profile*() see #738 (unmatched products) and PENDING (missing benchmarks).)

@Tilmon and @AnneSchoenauer please see the reprexes and let me know if this is what you expect or what needs to change. I'm aware one case should be impossible but that's something we can discuss later (#732). (Tilman, I believe you already saw this behaviour, Nothing new for you here.)

reprex `emissions_profile()`

library(tibble)
devtools::load_all()
#> ℹ Loading tiltIndicator

options(tibble.print_max = Inf, width = 500)

companies <- tribble(
    ~activity_uuid_product_uuid, ~clustered, ~companies_id,
                            "a",        "a",           "a",
                            "b",        "b",           "a",
                            "c",        "c",           "a"
)

co2 <- tibble::tribble(
    ~activity_uuid_product_uuid, ~co2_footprint, ~isic_4digit, ~tilt_sector, ~unit,
                            "a",              1,     "'1234'",          "a",   "a",
                            "b",              1,           NA,          "a",   "a"
)

result <- emissions_profile(companies, co2)

result |> unnest_product()
#> # A tibble: 13 × 7
#>    companies_id grouped_by       risk_category profile_ranking clustered activity_uuid_product_uuid co2_footprint
#>    <chr>        <chr>            <chr>                   <dbl> <chr>     <chr>                              <dbl>
#>  1 a            all              high                        1 a         a                                      1
#>  2 a            isic_4digit      high                        1 a         a                                      1
#>  3 a            tilt_sector      high                        1 a         a                                      1
#>  4 a            unit             high                        1 a         a                                      1
#>  5 a            unit_isic_4digit high                        1 a         a                                      1
#>  6 a            unit_tilt_sector high                        1 a         a                                      1
#>  7 a            all              high                        1 b         b                                      1
#>  8 a            isic_4digit      <NA>                       NA b         b                                      1
#>  9 a            tilt_sector      high                        1 b         b                                      1
#> 10 a            unit             high                        1 b         b                                      1
#> 11 a            unit_isic_4digit <NA>                       NA b         b                                      1
#> 12 a            unit_tilt_sector high                        1 b         b                                      1
#> 13 a            <NA>             <NA>                       NA c         c                                     NA

result |> unnest_company()
#> # A tibble: 24 × 4
#>    companies_id grouped_by       risk_category value
#>    <chr>        <chr>            <chr>         <dbl>
#>  1 a            all              high          0.667
#>  2 a            all              medium        0    
#>  3 a            all              low           0    
#>  4 a            all              <NA>          0.333
#>  5 a            isic_4digit      high          0.333
#>  6 a            isic_4digit      medium        0    
#>  7 a            isic_4digit      low           0    
#>  8 a            isic_4digit      <NA>          0.667
#>  9 a            tilt_sector      high          0.667
#> 10 a            tilt_sector      medium        0    
#> 11 a            tilt_sector      low           0    
#> 12 a            tilt_sector      <NA>          0.333
#> 13 a            unit             high          0.667
#> 14 a            unit             medium        0    
#> 15 a            unit             low           0    
#> 16 a            unit             <NA>          0.333
#> 17 a            unit_isic_4digit high          0.333
#> 18 a            unit_isic_4digit medium        0    
#> 19 a            unit_isic_4digit low           0    
#> 20 a            unit_isic_4digit <NA>          0.667
#> 21 a            unit_tilt_sector high          0.667
#> 22 a            unit_tilt_sector medium        0    
#> 23 a            unit_tilt_sector low           0    
#> 24 a            unit_tilt_sector <NA>          0.333

reprex: `emissions_profile_upstream()`

library(tibble)
devtools::load_all()
#> ℹ Loading tiltIndicator

options(tibble.print_max = Inf, width = 500)

companies <- tribble(
  ~activity_uuid_product_uuid, ~clustered, ~companies_id,
                          "a",        "a",           "a",
                          "b",        "a",           "a",
                  "unmatched",        "a",           "a"
)

co2 <- tribble(
  ~activity_uuid_product_uuid, ~input_activity_uuid_product_uuid, ~input_co2_footprint, ~input_isic_4digit, ~input_tilt_sector, ~input_tilt_subsector, ~input_unit,
                          "a",                               "a",                    1,           "'1234'",                "a",                   "a",         "a",
                          "b",                               "a",                    1,           "'1234'",                "a",                   "a",         "a"
)

result <- emissions_profile_upstream(companies, co2)

result |> unnest_product()
#> # A tibble: 13 × 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 a            all                          high                        1 a         a                          a                                                  1
#>  2 a            input_isic_4digit            high                        1 a         a                          a                                                  1
#>  3 a            input_tilt_sector            high                        1 a         a                          a                                                  1
#>  4 a            input_unit                   high                        1 a         a                          a                                                  1
#>  5 a            input_unit_input_isic_4digit high                        1 a         a                          a                                                  1
#>  6 a            input_unit_input_tilt_sector high                        1 a         a                          a                                                  1
#>  7 a            all                          high                        1 a         b                          a                                                  1
#>  8 a            input_isic_4digit            high                        1 a         b                          a                                                  1
#>  9 a            input_tilt_sector            high                        1 a         b                          a                                                  1
#> 10 a            input_unit                   high                        1 a         b                          a                                                  1
#> 11 a            input_unit_input_isic_4digit high                        1 a         b                          a                                                  1
#> 12 a            input_unit_input_tilt_sector high                        1 a         b                          a                                                  1
#> 13 a            <NA>                         <NA>                       NA a         unmatched                  <NA>                                              NA

result |> unnest_company()
#> # A tibble: 24 × 4
#>    companies_id grouped_by                   risk_category value
#>    <chr>        <chr>                        <chr>         <dbl>
#>  1 a            all                          high          0.667
#>  2 a            all                          medium        0    
#>  3 a            all                          low           0    
#>  4 a            all                          <NA>          0.333
#>  5 a            input_isic_4digit            high          0.667
#>  6 a            input_isic_4digit            medium        0    
#>  7 a            input_isic_4digit            low           0    
#>  8 a            input_isic_4digit            <NA>          0.333
#>  9 a            input_tilt_sector            high          0.667
#> 10 a            input_tilt_sector            medium        0    
#> 11 a            input_tilt_sector            low           0    
#> 12 a            input_tilt_sector            <NA>          0.333
#> 13 a            input_unit                   high          0.667
#> 14 a            input_unit                   medium        0    
#> 15 a            input_unit                   low           0    
#> 16 a            input_unit                   <NA>          0.333
#> 17 a            input_unit_input_isic_4digit high          0.667
#> 18 a            input_unit_input_isic_4digit medium        0    
#> 19 a            input_unit_input_isic_4digit low           0    
#> 20 a            input_unit_input_isic_4digit <NA>          0.333
#> 21 a            input_unit_input_tilt_sector high          0.667
#> 22 a            input_unit_input_tilt_sector medium        0    
#> 23 a            input_unit_input_tilt_sector low           0    
#> 24 a            input_unit_input_tilt_sector <NA>          0.333

.

TODO

EXCEPTIONS

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

maurolepore

@AnneSchoenauer,

I believe I'm close to what you want for emissions_profile() and emissions_profile_upstream() at product level (see reprex in the top comment of this PR).

But at company level the output is weird. I get different things with different bechmarks and I'm quite lost about what to actually expect.

Please see the comments below where I @mention you.

maurolepore · 2024-02-12T19:12:08Z

tests/testthat/_snaps/emissions_profile.md

@@ -157,3 +157,58 @@
      18     0


+# FIXME? at company level, `NA` in a benchmark yields the expected `value`s (#638)


@AnneSchoenauer,

Note that these three different benchmarks with similar inputs behave differently. Is this a bug? What do you actually expect in each case?

maurolepore · 2024-02-12T19:13:02Z

tests/testthat/_snaps/emissions_profile_upstream.md

@@ -166,3 +166,58 @@
      18     0


+# FIXME? at company level, `NA` in a benchmark yields the expected `value`s (#638)


@AnneSchoenauer,

Note that these three different benchmarks with similar inputs behave differently. Is this a bug? What do you actually expect in each case?

AnneSchoenauer · 2024-02-13T08:08:12Z

Hi @maurolepore - thanks a lot for this. I am not 100% sure how to calculate it correctly but I provide to you the following data on product_level and company_level:

library(dplyr)
#> 
#> Attache Paket: 'dplyr'
#> Die folgenden Objekte sind maskiert von 'package:stats':
#> 
#>     filter, lag
#> Die folgenden Objekte sind maskiert von 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tibble)

# Directly create a toy dataset with specified columns and values
companies_co2 <- tibble(
  companies_id = rep("a", 13),
  grouped_by = c("all", "isic_4digit", "tilt_sector", "unit", "unit_isic_4digit", "unit_tilt_sector", "NA", 
                 "all", "isic_4digit", "tilt_sector", "unit", "unit_isic_4digit", "unit_tilt_sector"),
  risk_category = c("high", NA, "high", "high", NA, "high", NA, "medium", "medium", "medium", "medium", "medium", "medium"),
  profile_ranking = c(1, NA, 1, 1, NA, 1, NA, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
  clustered = c(rep("a", 6), "b", rep("c", 6)),
  activity_uuid_product_uuid = c(rep("a", 6), NA, rep("c", 6)),
  co2_footprint = c(rep(1, 6), NA, rep(0.5, 6))
)

# View the created toy dataset
print(companies_co2)
#> # A tibble: 13 × 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      <NA>                     NA   a        
#>  3 a            tilt_sector      high                      1   a        
#>  4 a            unit             high                      1   a        
#>  5 a            unit_isic_4digit <NA>                     NA   a        
#>  6 a            unit_tilt_sector high                      1   a        
#>  7 a            NA               <NA>                     NA   b        
#>  8 a            all              medium                    0.5 c        
#>  9 a            isic_4digit      medium                    0.5 c        
#> 10 a            tilt_sector      medium                    0.5 c        
#> 11 a            unit             medium                    0.5 c        
#> 12 a            unit_isic_4digit medium                    0.5 c        
#> 13 a            unit_tilt_sector medium                    0.5 c        
#> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>

# Intended final dataset format
companies <- tibble(
  companies_id = rep("a", 24),
  grouped_by = c("all", "all", "all", "NA", "isic_4digit", "isic_4digit", "isic_4digit", "NA", "tilt_sector", "tilt_sector", "tilt_sector", "NA", "unit", "unit", "unit", "NA", "unit_isic_4digit", "unit_isic_4digit", "unit_isic_4digit", "NA", "unit_tilt_sector", "unit_tilt_sector", "unit_tilt_sector", "NA"),
  risk_category = c("high", "medium", "low", NA, "high", "medium", "low", NA, "high", "medium", "low", NA, "high", "medium", "low", NA, "high", "medium", "low", NA, "high", "medium", "low", NA),
  risk_share = c(0.33, 0.33, 0, 0.33, 0, 0.33, 0, 0.67, 0.33, 0.33, 0, 0.33, 0.33, 0.33, 0, 0.33, 0, 0.33, 0, 0.67, 0.33, 0.33, 0, 0.33)
)

# View the intended final dataset
print(companies)
#> # A tibble: 24 × 4
#>    companies_id grouped_by  risk_category risk_share
#>    <chr>        <chr>       <chr>              <dbl>
#>  1 a            all         high                0.33
#>  2 a            all         medium              0.33
#>  3 a            all         low                 0   
#>  4 a            NA          <NA>                0.33
#>  5 a            isic_4digit high                0   
#>  6 a            isic_4digit medium              0.33
#>  7 a            isic_4digit low                 0   
#>  8 a            NA          <NA>                0.67
#>  9 a            tilt_sector high                0.33
#> 10 a            tilt_sector medium              0.33
#> # ℹ 14 more rows

^{Created on 2024-02-13 with reprex v2.0.2}

I created 3 products as it is easier to see the shares on company level. As you can see one product 'clustered' 'b' was not been matched with ecoinvent - therefore it is NA. Plus we have a product namely 'clustered' 'a' which was not beeing able to be grouped in the 'isic' benchmarks. Therefore on company level we always have one third in the NA category as 'b' was not been able to be matched. If we look at the isic benchmarks, then two out of three products have no risk_value so 2/3 are 'NA'. For the other benchmarks we are able to match and benchmark two products 'a' and 'c'. As 'a' is a high product it and 'c' has a medium product we have 1/3 in 'high', 1/3 in 'medium' and 1/3 in 'NA'. Does this make sense?

maurolepore · 2024-02-20T23:17:11Z

@Tilmon FYI I already fixed the output at company level for the remaining case (preserve unmatched products) using the 6-benchmarks approach. Or so I think.

See #729. We can have a focus conversation there.

Tilmon · 2024-02-21T09:18:46Z

@maurolepore thanks a lot for your detailed response to my comment. This really helps to clarify the issues. As discussed in yesterday's sprint, I reviewed your proposed solutions carefully and will now respond to your questions here:

ml01: Do you agree with my interpretation of the reprex in the four bullets immediately above?

Yes, I agree. The code does what it is supposed to do for three cases (missing benchmark on product- and company-level, unmatched product on product-level). I will respond to whether the latest changes to the company-level output in case of unmatched products fix the problem with that case here #729.

ml02: Considering that adding the 7th benchmark would be relatively hard, shall we a) wait for Anne's input b) explore an easier alternative with the existing 6-benchmarks, or c) something else?

Summarizing our conclusion from yesterday's tech meeting: As 7th benchmark requires significant extra effort, we'll stick to a solution with six benchmarks for now to get the issue fixed. When @AnneSchoenauer is back, we can discuss, if we want to move to 7 benchmarks in the mid- or long-term for improved usability.

ml03: Can you help me create toy datasets that passed to our real code would generate an output at company level with value different than 0 in two of these: "high", "medium", and "low"? For example, I would want value to be c(1/3, 1/3, 1/3) where risk_category is c("high", "medium", NA), respectively. Conceptually it seems possible as you and Anne "draw" such results but in practice I fail to do that -- even if I create inputs that look almost identical to what you draw. That is a crucial motivation for you and Anne to write real reprexes -- instead of conceptual examples that only pretend to have used our code. That would put your real knowledge of the data and methodology in contact with the real code and tell us for sure if the code behaves as you expect.

I'll put it on my list and try to find some time later today!

maurolepore · 2024-02-21T12:29:08Z

Thanks @Tilmon

RE ml03: I realize this item stands alone in its own issue: #730

… in all levels of 'risk_catrgory' (#737)

… non-zero value

AnneSchoenauer · 2024-02-26T11:30:06Z

Dear @maurolepore,

Thanks a lot for this! It is such a substantial change and good to see that we get our heads around this.
Here my review of the reprexes:

This PR focuses on emissions_profile*(). (For sector_profile*() see #738 (unmatched products) and PENDING (missing benchmarks).)

@Tilmon and @AnneSchoenauer please see the reprexes and let me know if this is what you expect or what needs to change. I'm aware one case should be impossible but that's something we can discuss later (#732). (Tilman, I believe you already saw this behaviour, Nothing new for you here.)

reprex emissions_profile()

library(tibble)
devtools::load_all()
#> ℹ Loading tiltIndicator

options(tibble.print_max = Inf, width = 500)

companies <- tribble(
    ~activity_uuid_product_uuid, ~clustered, ~companies_id,
                            "a",        "a",           "a",
                            "b",        "b",           "a",
                            "c",        "c",           "a"
)

co2 <- tibble::tribble(
    ~activity_uuid_product_uuid, ~co2_footprint, ~isic_4digit, ~tilt_sector, ~unit,
                            "a",              1,     "'1234'",          "a",   "a",
                            "b",              1,           NA,          "a",   "a"
)

result <- emissions_profile(companies, co2)
result |> unnest_product()
#> # A tibble: 13 × 7
#>    companies_id grouped_by       risk_category profile_ranking clustered activity_uuid_product_uuid co2_footprint
#>    <chr>        <chr>            <chr>                   <dbl> <chr>     <chr>                              <dbl>
#>  1 a            all              high                        1 a         a                                      1
#>  2 a            isic_4digit      high                        1 a         a                                      1
#>  3 a            tilt_sector      high                        1 a         a                                      1
#>  4 a            unit             high                        1 a         a                                      1
#>  5 a            unit_isic_4digit high                        1 a         a                                      1
#>  6 a            unit_tilt_sector high                        1 a         a                                      1
#>  7 a            all              high                        1 b         b                                      1
#>  8 a            isic_4digit      <NA>                       NA b         b                                      1
#>  9 a            tilt_sector      high                        1 b         b                                      1
#> 10 a            unit             high                        1 b         b                                      1
#> 11 a            unit_isic_4digit <NA>                       NA b         b                                      1
#> 12 a            unit_tilt_sector high                        1 b         b                                      1
#> 13 a            <NA>             <NA>                       NA c         c                                     NA

result |> unnest_company()
#> # A tibble: 24 × 4
#>    companies_id grouped_by       risk_category value
#>    <chr>        <chr>            <chr>         <dbl>
#>  1 a            all              high          0.667
#>  2 a            all              medium        0    
#>  3 a            all              low           0    
#>  4 a            all              <NA>          0.333
#>  5 a            isic_4digit      high          0.333
#>  6 a            isic_4digit      medium        0    
#>  7 a            isic_4digit      low           0    
#>  8 a            isic_4digit      <NA>          0.667
#>  9 a            tilt_sector      high          0.667
#> 10 a            tilt_sector      medium        0    
#> 11 a            tilt_sector      low           0    
#> 12 a            tilt_sector      <NA>          0.333
#> 13 a            unit             high          0.667
#> 14 a            unit             medium        0    
#> 15 a            unit             low           0    
#> 16 a            unit             <NA>          0.333
#> 17 a            unit_isic_4digit high          0.333
#> 18 a            unit_isic_4digit medium        0    
#> 19 a            unit_isic_4digit low           0    
#> 20 a            unit_isic_4digit <NA>          0.667
#> 21 a            unit_tilt_sector high          0.667
#> 22 a            unit_tilt_sector medium        0    
#> 23 a            unit_tilt_sector low           0    
#> 24 a            unit_tilt_sector <NA>          0.333

This is exactly what I expect! So case 1 in which there are missing benchmarks is done!

reprex: emissions_profile_upstream()

library(tibble)
devtools::load_all()
#> ℹ Loading tiltIndicator

options(tibble.print_max = Inf, width = 500)

companies <- tribble(
  ~activity_uuid_product_uuid, ~clustered, ~companies_id,
                          "a",        "a",           "a",
                          "b",        "a",           "a",
                  "unmatched",        "a",           "a"
)

co2 <- tribble(
  ~activity_uuid_product_uuid, ~input_activity_uuid_product_uuid, ~input_co2_footprint, ~input_isic_4digit, ~input_tilt_sector, ~input_tilt_subsector, ~input_unit,
                          "a",                               "a",                    1,           "'1234'",                "a",                   "a",         "a",
                          "b",                               "a",                    1,           "'1234'",                "a",                   "a",         "a"
)

result <- emissions_profile_upstream(companies, co2)

result |> unnest_product()
#> # A tibble: 13 × 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 a            all                          high                        1 a         a                          a                                                  1
#>  2 a            input_isic_4digit            high                        1 a         a                          a                                                  1
#>  3 a            input_tilt_sector            high                        1 a         a                          a                                                  1
#>  4 a            input_unit                   high                        1 a         a                          a                                                  1
#>  5 a            input_unit_input_isic_4digit high                        1 a         a                          a                                                  1
#>  6 a            input_unit_input_tilt_sector high                        1 a         a                          a                                                  1
#>  7 a            all                          high                        1 a         b                          a                                                  1
#>  8 a            input_isic_4digit            high                        1 a         b                          a                                                  1
#>  9 a            input_tilt_sector            high                        1 a         b                          a                                                  1
#> 10 a            input_unit                   high                        1 a         b                          a                                                  1
#> 11 a            input_unit_input_isic_4digit high                        1 a         b                          a                                                  1
#> 12 a            input_unit_input_tilt_sector high                        1 a         b                          a                                                  1
#> 13 a            <NA>                         <NA>                       NA a         unmatched                  <NA>                                              NA

result |> unnest_company()
#> # A tibble: 24 × 4
#>    companies_id grouped_by                   risk_category value
#>    <chr>        <chr>                        <chr>         <dbl>
#>  1 a            all                          high          0.667
#>  2 a            all                          medium        0    
#>  3 a            all                          low           0    
#>  4 a            all                          <NA>          0.333
#>  5 a            input_isic_4digit            high          0.667
#>  6 a            input_isic_4digit            medium        0    
#>  7 a            input_isic_4digit            low           0    
#>  8 a            input_isic_4digit            <NA>          0.333
#>  9 a            input_tilt_sector            high          0.667
#> 10 a            input_tilt_sector            medium        0    
#> 11 a            input_tilt_sector            low           0    
#> 12 a            input_tilt_sector            <NA>          0.333
#> 13 a            input_unit                   high          0.667
#> 14 a            input_unit                   medium        0    
#> 15 a            input_unit                   low           0    
#> 16 a            input_unit                   <NA>          0.333
#> 17 a            input_unit_input_isic_4digit high          0.667
#> 18 a            input_unit_input_isic_4digit medium        0    
#> 19 a            input_unit_input_isic_4digit low           0    
#> 20 a            input_unit_input_isic_4digit <NA>          0.333
#> 21 a            input_unit_input_tilt_sector high          0.667
#> 22 a            input_unit_input_tilt_sector medium        0    
#> 23 a            input_unit_input_tilt_sector low           0    
#> 24 a            input_unit_input_tilt_sector <NA>          0.333

I think @Tilmon you now created a new benchmarked called "not matched" right? If this is the case the behaviour here would be not what you expect as this case would include the benchmark "not matched" as you indicated here. I like the idea about adding another benchmark a lot but I think for now it would be fine with the status that we have - so for the Bundesbank the above reprexes would be enough and they picture how I expect the code to behave. Is this fine for you @Tilmon? I would then leave this for an enhancement at a later stage to add another benchmark? What do you think?

maurolepore · 2024-02-26T16:31:43Z

Thanks both for your input.

@AnneSchoenauer RE

This is exactly what I expect! So case 1 in which there are missing benchmarks is done!

Note the reprex shows not only the case of a missing benchmark but also the case of an unmatched product. Here I reproduce just the relevant bit. Focus on the value of activity_uuid_product_uuid and note:

"a" is matched.
"b" is matched but has a missing benchmark.
"c" is unmatched.

# ... more code
companies <- tribble(
    ~activity_uuid_product_uuid, ~clustered, ~companies_id,
                            "a",        "a",           "a",
                            "b",        "b",           "a",
                            "c",        "c",           "a"
)

co2 <- tibble::tribble(
    ~activity_uuid_product_uuid, ~co2_footprint, ~isic_4digit, ~tilt_sector, ~unit,
                            "a",              1,     "'1234'",          "a",   "a",
                            "b",              1,           NA,          "a",   "a"
)
# ... more code

I'll assume this case is also handled as you expect -- since otherwise Tilman and you would have likely noticed.

As discussed today during sprint planning, I'll go ahead and merge this PR.

maurolepore force-pushed the 638_risk_category-must-be-NA branch from 875b228 to ff513ad Compare February 5, 2024 22:51

maurolepore changed the title ~~Leave a failing test~~ At product level, NA in a benchmark column yields NA in the corresponding risk_category and profile_ranking Feb 5, 2024

maurolepore force-pushed the 638_risk_category-must-be-NA branch from 87bd2db to b30a95f Compare February 9, 2024 20:52

maurolepore changed the title ~~NA in a benchmarks yield NA in risk_category / profile_ranking (#638), and preserve unmatched products (#567)~~ In emissions_profile*() preserve NA in benchmarks (#638) and unmatched products (#567) Feb 9, 2024

maurolepore changed the title ~~In emissions_profile*() preserve NA in benchmarks (#638) and unmatched products (#567)~~ In emissions_profile*() preserve missing benchmarks (#638) and unmatched products (#567) Feb 9, 2024

maurolepore force-pushed the 638_risk_category-must-be-NA branch 3 times, most recently from aa70209 to a6a2440 Compare February 10, 2024 18:07

Preserve unmatched products and missing benchmarks

a6a2440

maurolepore changed the title ~~In emissions_profile*() preserve missing benchmarks (#638) and unmatched products (#567)~~ emissions_profile*() preserves missing benchmarks & unmatched products Feb 10, 2024

maurolepore changed the title ~~emissions_profile*() preserves missing benchmarks & unmatched products~~ emissions*() preserves missing benchmarks & unmatched products Feb 10, 2024

maurolepore added 12 commits February 10, 2024 18:15

Merge branch 'main' into 638_risk_category-must-be-NA

f3dbd45

Merge branch 'main' into 638_risk_category-must-be-NA

73e65b5

Merge branch 'main' into 638_risk_category-must-be-NA

4b5143b

Merge branch 'main' into 638_risk_category-must-be-NA

cdda815

Merge branch 'main' into 638_risk_category-must-be-NA

9f5c915

At company level (#718)

014f2d9

wip

232947d

refactor

64f36e7

wip

269b9a7

wip

a813402

wip

171d45c

wip

0ebe0db

maurolepore commented Feb 12, 2024

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maurolepore requested review from AnneSchoenauer and Tilmon February 12, 2024 19:19

Update snapshots

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maurolepore mentioned this pull request Feb 21, 2024

Test that the output at company level can have value other than 0 in "high" AND ("medium" OR "low") #730

Closed

maurolepore changed the title ~~emissions*() preserves missing benchmarks & unmatched products~~ emissions*() preserves missing benchmarks Feb 21, 2024

maurolepore changed the title ~~emissions*() preserves missing benchmarks~~ emissions*() preserves missing benchmarks & unmatched products Feb 21, 2024

At company level perserve unmatched products via 6 benchmarks (#729)

d99e96f

maurolepore mentioned this pull request Feb 22, 2024

sector*() should preserve missing "benchmarks" and unmatched products #733

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maurolepore added 6 commits February 23, 2024 15:30

Increment version number to 0.0.0.9207

1d5275c

Test that the company-level output can actually have a non-zero value…

91fc00c

… in all levels of 'risk_catrgory' (#737)

Increment version number to 0.0.0.9208

185b39d

For emissions_profile_upstrem(), test that all risk_category can have…

0c02fca

… non-zero value

wip

7b6af03

Refactor

2e25771

maurolepore mentioned this pull request Feb 25, 2024

The sector*() functions now preserve unmatched products #738

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maurolepore changed the title ~~emissions*() preserves missing benchmarks & unmatched products~~ The emissions*() functions preserve unmatched products and missing benchmarks Feb 25, 2024

AnneSchoenauer approved these changes Feb 26, 2024

View reviewed changes

Revert NEWS

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maurolepore mentioned this pull request Feb 27, 2024

Anticipate new output from tiltIndicator 2DegreesInvesting/tiltIndicatorAfter#167

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maurolepore marked this pull request as ready for review February 27, 2024 12:12

maurolepore marked this pull request as draft February 27, 2024 12:15

maurolepore mentioned this pull request Feb 27, 2024

Anticipate that tiltIndicator::emissions*() at company-level will soon have 4 levels of risk_category 2DegreesInvesting/tiltIndicatorAfter#169

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maurolepore marked this pull request as ready for review February 27, 2024 12:51

maurolepore merged commit 657d275 into main Feb 27, 2024
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maurolepore deleted the 638_risk_category-must-be-NA branch February 27, 2024 13:03

maurolepore mentioned this pull request Feb 27, 2024

sector_profile() now accounts for unmatched type, sector or subsector #739

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maurolepore mentioned this pull request Mar 13, 2024

profile_emissions() now preserves unmatched products 2DegreesInvesting/tiltIndicatorAfter#193

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maurolepore mentioned this pull request May 8, 2024

emissions_profile_upstream() with unmatched products should throw an error #732

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The `emissions*()` functions preserve unmatched products and missing benchmarks #639

The `emissions*()` functions preserve unmatched products and missing benchmarks #639

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		@@ -157,3 +157,58 @@
		18 0


		# FIXME? at company level, `NA` in a benchmark yields the expected `value`s (#638)

		@@ -166,3 +166,58 @@
		18 0


		# FIXME? at company level, `NA` in a benchmark yields the expected `value`s (#638)

The emissions*() functions preserve unmatched products and missing benchmarks #639

The emissions*() functions preserve unmatched products and missing benchmarks #639

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The `emissions*()` functions preserve unmatched products and missing benchmarks #639

The `emissions*()` functions preserve unmatched products and missing benchmarks #639

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