From 2261b2f6b58969f8d6c8c26cb87af9928bb9cb55 Mon Sep 17 00:00:00 2001 From: Jackson Hoffart Date: Mon, 15 Apr 2024 14:05:51 +0200 Subject: [PATCH] first commit --- .Rbuildignore | 10 + .github/.gitignore | 1 + .github/CODEOWNERS | 5 + .github/dependabot.yml | 6 + .github/workflows/R-CMD-check.yaml | 52 +++ .github/workflows/add-issues-to-ADO.yml | 26 ++ .../add-prs-and-issues-to-project.yml | 30 ++ .github/workflows/pkgdown.yaml | 46 ++ .github/workflows/pr-check-file-encodings.yml | 23 + .gitignore | 64 +++ DESCRIPTION | 65 +++ LICENSE | 2 + LICENSE.md | 21 + NAMESPACE | 68 +++ NEWS.md | 65 +++ R/apply_fair_share.R | 61 +++ R/calc_shares_all_classes.R | 33 ++ R/check_crucial_names.R | 35 ++ R/conversion_functions.R | 37 ++ R/dataprep_abcd_scen_connection.R | 407 ++++++++++++++++++ R/determine_relevant_years.R | 17 + R/do_calcs_within_id.R | 111 +++++ R/expand_tech_rows.R | 20 + R/filter_most_recent_timestamp.R | 43 ++ R/import_ar_advanced_company_indicators.R | 169 ++++++++ R/override_sector_by_factset_entity_id.R | 45 ++ R/pacta.data.preparation-package.R | 47 ++ R/prepare_abcd_flags_bonds.R | 75 ++++ R/prepare_abcd_flags_equity.R | 58 +++ ...epare_ar_company_id__country_of_domicile.R | 24 ++ ..._company_id__credit_parent_ar_company_id.R | 24 ++ ...ar_company_id__sectors_with_assets__debt.R | 37 ++ ...mpany_id__sectors_with_assets__ownership.R | 37 ++ R/prepare_company_id__creditor_company_id.R | 30 ++ R/prepare_entity_info.R | 78 ++++ R/prepare_factset_entity_id__ar_company_id.R | 37 ++ ...pare_factset_entity_id__credit_parent_id.R | 32 ++ ...actset_entity_id__security_mapped_sector.R | 32 ++ R/prepare_financial_data.R | 44 ++ R/prepare_fund_data.R | 51 +++ R/prepare_isin_to_fund_table.R | 36 ++ ..._iss_average_sector_emission_intensities.R | 87 ++++ R/prepare_iss_company_emissions.R | 22 + R/prepare_iss_entity_emission_intensities.R | 71 +++ R/prepare_masterdata.R | 83 ++++ R/prepare_masterdata_debt.R | 61 +++ R/prepare_total_fund_list.R | 17 + R/quarter_to_factset_timestamp.R | 16 + R/quarter_to_imf_timestamp.R | 15 + R/standardize_asset_type_names.R | 23 + R/write_manifest.R | 142 ++++++ README.md | 29 ++ _pkgdown.yml | 3 + man/calc_shares_all_classes.Rd | 40 ++ man/dataprep_abcd_scen_connection.Rd | 61 +++ man/determine_relevant_years.Rd | 21 + man/figures/logo.png | Bin 0 -> 28666 bytes man/import_ar_advanced_company_indicators.Rd | 36 ++ man/import_ar_masterdata.Rd | 28 ++ man/pacta.data.preparation-package.Rd | 37 ++ man/prepare_abcd_flags_bonds.Rd | 35 ++ man/prepare_abcd_flags_equity.Rd | 31 ++ ...pare_ar_company_id__country_of_domicile.Rd | 19 + ...company_id__credit_parent_ar_company_id.Rd | 20 + ...r_company_id__sectors_with_assets__debt.Rd | 27 ++ ...pany_id__sectors_with_assets__ownership.Rd | 27 ++ ...prepare_company_id__creditor_company_id.Rd | 20 + man/prepare_entity_info.Rd | 38 ++ ...repare_factset_entity_id__ar_company_id.Rd | 20 + ...are_factset_entity_id__credit_parent_id.Rd | 19 + ...ctset_entity_id__security_mapped_sector.Rd | 20 + man/prepare_financial_data.Rd | 24 ++ man/prepare_fund_data.Rd | 23 + man/prepare_isin_to_fund_table.Rd | 21 + ...iss_average_sector_emission_intensities.Rd | 36 ++ man/prepare_iss_company_emissions.Rd | 17 + ...prepare_iss_entity_emission_intensities.Rd | 35 ++ man/prepare_masterdata.Rd | 36 ++ man/prepare_masterdata_debt.Rd | 36 ++ man/prepare_total_fund_list.Rd | 17 + man/quarter_to_factset_timestamp.Rd | 21 + man/quarter_to_imf_timestamp.Rd | 19 + man/standardize_asset_type_names.Rd | 19 + man/write_manifest.Rd | 26 ++ pacta.data.preparation.Rproj | 22 + tests/testthat.R | 12 + tests/testthat/helper-fake_data.R | 104 +++++ tests/testthat/test-calc_shares_all_classes.R | 94 ++++ tests/testthat/test-check_crucial_names.R | 24 ++ tests/testthat/test-expand_tech_rows.R | 35 ++ .../test-filter_most_recent_timestamp.R | 48 +++ ...est-override_sector_by_factset_entity_id.R | 47 ++ tests/testthat/test-prepare_masterdata.R | 15 + .../test-quarter_to_factset_timestamp.R | 21 + .../testthat/test-quarter_to_imf_timestamp.R | 21 + vignettes/.gitignore | 2 + vignettes/Airbus_1.png | Bin 0 -> 126458 bytes vignettes/Airbus_2.png | Bin 0 -> 135596 bytes vignettes/BHP.png | Bin 0 -> 184427 bytes vignettes/Duke_Energy.png | Bin 0 -> 148061 bytes vignettes/Stora_Enso.png | Bin 0 -> 224174 bytes vignettes/share_ownership_methodology.Rmd | 156 +++++++ 102 files changed, 4103 insertions(+) create mode 100644 .Rbuildignore create mode 100644 .github/.gitignore create mode 100644 .github/CODEOWNERS create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/R-CMD-check.yaml create mode 100644 .github/workflows/add-issues-to-ADO.yml create mode 100644 .github/workflows/add-prs-and-issues-to-project.yml create mode 100644 .github/workflows/pkgdown.yaml create mode 100644 .github/workflows/pr-check-file-encodings.yml create mode 100644 .gitignore create mode 100644 DESCRIPTION create mode 100644 LICENSE create mode 100644 LICENSE.md create mode 100644 NAMESPACE create mode 100644 NEWS.md create mode 100644 R/apply_fair_share.R create mode 100644 R/calc_shares_all_classes.R create mode 100644 R/check_crucial_names.R create mode 100644 R/conversion_functions.R create mode 100644 R/dataprep_abcd_scen_connection.R create mode 100644 R/determine_relevant_years.R create mode 100644 R/do_calcs_within_id.R create mode 100644 R/expand_tech_rows.R create mode 100644 R/filter_most_recent_timestamp.R create mode 100644 R/import_ar_advanced_company_indicators.R create mode 100644 R/override_sector_by_factset_entity_id.R create mode 100644 R/pacta.data.preparation-package.R create mode 100644 R/prepare_abcd_flags_bonds.R create mode 100644 R/prepare_abcd_flags_equity.R create mode 100644 R/prepare_ar_company_id__country_of_domicile.R create mode 100644 R/prepare_ar_company_id__credit_parent_ar_company_id.R create mode 100644 R/prepare_ar_company_id__sectors_with_assets__debt.R create mode 100644 R/prepare_ar_company_id__sectors_with_assets__ownership.R create mode 100644 R/prepare_company_id__creditor_company_id.R create mode 100644 R/prepare_entity_info.R create mode 100644 R/prepare_factset_entity_id__ar_company_id.R create mode 100644 R/prepare_factset_entity_id__credit_parent_id.R create mode 100644 R/prepare_factset_entity_id__security_mapped_sector.R create mode 100644 R/prepare_financial_data.R create mode 100644 R/prepare_fund_data.R create mode 100644 R/prepare_isin_to_fund_table.R create mode 100644 R/prepare_iss_average_sector_emission_intensities.R create mode 100644 R/prepare_iss_company_emissions.R create mode 100644 R/prepare_iss_entity_emission_intensities.R create mode 100644 R/prepare_masterdata.R create mode 100644 R/prepare_masterdata_debt.R create mode 100644 R/prepare_total_fund_list.R create mode 100644 R/quarter_to_factset_timestamp.R create mode 100644 R/quarter_to_imf_timestamp.R create mode 100644 R/standardize_asset_type_names.R create mode 100644 R/write_manifest.R create mode 100644 README.md create mode 100644 _pkgdown.yml create mode 100644 man/calc_shares_all_classes.Rd create mode 100644 man/dataprep_abcd_scen_connection.Rd create mode 100644 man/determine_relevant_years.Rd create mode 100644 man/figures/logo.png create mode 100644 man/import_ar_advanced_company_indicators.Rd create mode 100644 man/import_ar_masterdata.Rd create mode 100644 man/pacta.data.preparation-package.Rd create mode 100644 man/prepare_abcd_flags_bonds.Rd create mode 100644 man/prepare_abcd_flags_equity.Rd create mode 100644 man/prepare_ar_company_id__country_of_domicile.Rd create mode 100644 man/prepare_ar_company_id__credit_parent_ar_company_id.Rd create mode 100644 man/prepare_ar_company_id__sectors_with_assets__debt.Rd create mode 100644 man/prepare_ar_company_id__sectors_with_assets__ownership.Rd create mode 100644 man/prepare_company_id__creditor_company_id.Rd create mode 100644 man/prepare_entity_info.Rd create mode 100644 man/prepare_factset_entity_id__ar_company_id.Rd create mode 100644 man/prepare_factset_entity_id__credit_parent_id.Rd create mode 100644 man/prepare_factset_entity_id__security_mapped_sector.Rd create mode 100644 man/prepare_financial_data.Rd create mode 100644 man/prepare_fund_data.Rd create mode 100644 man/prepare_isin_to_fund_table.Rd create mode 100644 man/prepare_iss_average_sector_emission_intensities.Rd create mode 100644 man/prepare_iss_company_emissions.Rd create mode 100644 man/prepare_iss_entity_emission_intensities.Rd create mode 100644 man/prepare_masterdata.Rd create mode 100644 man/prepare_masterdata_debt.Rd create mode 100644 man/prepare_total_fund_list.Rd create mode 100644 man/quarter_to_factset_timestamp.Rd create mode 100644 man/quarter_to_imf_timestamp.Rd create mode 100644 man/standardize_asset_type_names.Rd create mode 100644 man/write_manifest.Rd create mode 100644 pacta.data.preparation.Rproj create mode 100644 tests/testthat.R create mode 100644 tests/testthat/helper-fake_data.R create mode 100644 tests/testthat/test-calc_shares_all_classes.R create mode 100644 tests/testthat/test-check_crucial_names.R create mode 100644 tests/testthat/test-expand_tech_rows.R create mode 100644 tests/testthat/test-filter_most_recent_timestamp.R create mode 100644 tests/testthat/test-override_sector_by_factset_entity_id.R create mode 100644 tests/testthat/test-prepare_masterdata.R create mode 100644 tests/testthat/test-quarter_to_factset_timestamp.R create mode 100644 tests/testthat/test-quarter_to_imf_timestamp.R create mode 100644 vignettes/.gitignore create mode 100644 vignettes/Airbus_1.png create mode 100644 vignettes/Airbus_2.png create mode 100644 vignettes/BHP.png create mode 100644 vignettes/Duke_Energy.png create mode 100644 vignettes/Stora_Enso.png create mode 100644 vignettes/share_ownership_methodology.Rmd diff --git a/.Rbuildignore b/.Rbuildignore new file mode 100644 index 0000000..f9e3f62 --- /dev/null +++ b/.Rbuildignore @@ -0,0 +1,10 @@ +^pacta\.data\.preparation\.Rproj$ +^\.Rproj\.user$ +^data-raw$ +^LICENSE\.md$ +^\.DS_Store$ +^\.github$ +^README\.Rmd$ +^doc$ +^Meta$ +^_pkgdown\.yml$ diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 0000000..2d19fc7 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000..5559d3c --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,5 @@ +# These owners will be the default owners for everything in +# the repo. Unless a later match takes precedence, +# @cjyetman will be requested for review when someone opens +# a pull request. +* @cjyetman diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..5ace460 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,6 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml new file mode 100644 index 0000000..bb6824c --- /dev/null +++ b/.github/workflows/R-CMD-check.yaml @@ -0,0 +1,52 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + schedule: + - cron: '0 5 * * 1' + +name: R-CMD-check + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + timeout-minutes: 25 + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: false + matrix: + config: + - {os: macOS-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + - {os: ubuntu-latest, r: 'oldrel-1'} + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes + + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::rcmdcheck + needs: check + + - uses: r-lib/actions/check-r-package@v2 + with: + upload-snapshots: true + error-on: '"note"' diff --git a/.github/workflows/add-issues-to-ADO.yml b/.github/workflows/add-issues-to-ADO.yml new file mode 100644 index 0000000..a6f095f --- /dev/null +++ b/.github/workflows/add-issues-to-ADO.yml @@ -0,0 +1,26 @@ +--- +# This example file will enable actions that trigger on created or modified GitHub issues. +# +# Note the @main in `uses:` on the last line. This will call the latest version of the workflow from the `main` branch in the RMI-PACTA/actions repo. +# You can also specify a tag from that repo, or a commit SHA to pin action versions. +on: + issues: + types: + [opened, edited, deleted, closed, reopened, labeled, unlabeled, assigned] + issue_comment: + types: [created, edited, deleted] + +name: GH issues + +permissions: + issues: write + pull-requests: write + +jobs: + issues: + name: Run issues workflows + uses: RMI-PACTA/actions/.github/workflows/issues.yml@main + with: + ado_area_path: "2DegreesInvesting\\Repositories that cjyetman maintains" + secrets: + ADO_TOKEN: ${{ secrets.ADO_PERSONAL_ACCESS_TOKEN }} diff --git a/.github/workflows/add-prs-and-issues-to-project.yml b/.github/workflows/add-prs-and-issues-to-project.yml new file mode 100644 index 0000000..8dd5076 --- /dev/null +++ b/.github/workflows/add-prs-and-issues-to-project.yml @@ -0,0 +1,30 @@ +name: Adds all new issues and PRs to the Transition Monitor project + +on: + issues: + types: + - opened + - transferred + pull_request: + branches: [main] + types: + - opened + +jobs: + add-to-tm-project: + name: Add issue/PR to Transition Monitor project + runs-on: ubuntu-latest + steps: + - uses: actions/add-to-project@v1.0.1 + with: + project-url: https://github.com/orgs/RMI-PACTA/projects/8 + github-token: ${{ secrets.PAT_ADD_ISSUES_TO_PROJECT }} + + add-to-maintainer-project: + name: Add issue/PR to @cjyetman's maintainer project + runs-on: ubuntu-latest + steps: + - uses: actions/add-to-project@v1.0.1 + with: + project-url: https://github.com/orgs/RMI-PACTA/projects/13 + github-token: ${{ secrets.PAT_ADD_ISSUES_TO_PROJECT }} diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 0000000..e9132e9 --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,46 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main] + pull_request: + branches: [main] + release: + types: [published] + workflow_dispatch: + +name: pkgdown + +jobs: + pkgdown: + runs-on: ubuntu-latest + # Only restrict concurrency for non-PR jobs + concurrency: + group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::pkgdown, local::. + needs: website + + - name: Build site + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} + + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@v4.5.0 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/.github/workflows/pr-check-file-encodings.yml b/.github/workflows/pr-check-file-encodings.yml new file mode 100644 index 0000000..bafc538 --- /dev/null +++ b/.github/workflows/pr-check-file-encodings.yml @@ -0,0 +1,23 @@ +name: check file encodings in PR +on: [pull_request] +jobs: + file-encoding: + name: file encoding check + runs-on: ubuntu-latest + timeout-minutes: 5 + + steps: + - name: run the checkout action + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: list all changed files + run: | + files=$(git diff --name-only origin/$GITHUB_BASE_REF...${{ github.sha }}) + IFS=$'\n'; files=($files); unset IFS; # split the string into an array + file --mime "${files[@]}" + - name: list all changed files with the wrong encoding + run: | + files=$(git diff --name-only origin/$GITHUB_BASE_REF...${{ github.sha }}) + IFS=$'\n'; files=($files); unset IFS; # split the string into an array + ! file --mime "${files[@]}" | grep -v "charset=utf-8\|charset=us-ascii\|charset=binary\| (No such file or directory)$" diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1538ed5 --- /dev/null +++ b/.gitignore @@ -0,0 +1,64 @@ +### R ### +# History files +.Rhistory +.Rapp.history + +# Session Data files +.RData +.RDataTmp + +# User-specific files +.Ruserdata + +# Example code in package build process +*-Ex.R + +# Output files from R CMD build +/*.tar.gz + +# Output files from R CMD check +/*.Rcheck/ + +# RStudio files +.Rproj.user/ + +# produced vignettes +vignettes/*.html +vignettes/*.pdf + +# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 +.httr-oauth + +# knitr and R markdown default cache directories +*_cache/ +/cache/ + +# Temporary files created by R markdown +*.utf8.md +*.knit.md + +# R Environment Variables +.Renviron + +# pkgdown site +docs/ + +# translation temp files +po/*~ + +# RStudio Connect folder +rsconnect/ + +### R.Bookdown Stack ### +# R package: bookdown caching files +/*_files/ + +# End of https://www.toptal.com/developers/gitignore/api/r + +# macOS +.DS_Store + +# stuff added automatically for vignette knitting +inst/doc +/doc/ +/Meta/ diff --git a/DESCRIPTION b/DESCRIPTION new file mode 100644 index 0000000..e3a5f80 --- /dev/null +++ b/DESCRIPTION @@ -0,0 +1,65 @@ +Package: pacta.data.preparation +Title: Prepare Data for PACTA for Investors +Version: 0.1.0.9003 +Authors@R: + c(person(given = "CJ", + family = "Yetman", + role = c("aut", "cre", "ctr"), + email = "cj@cjyetman.com", + comment = c(ORCID = "0000-0001-5099-9500")), + person(given = "Jackson", + family = "Hoffart", + email = "jackson.hoffart@gmail.com", + role = c("aut", "ctr"), + comment = c(ORCID = "0000-0002-8600-5042")), + person(given = "Jacob", + family = "Kastl", + role = c("aut", "ctr"), + email = "jacob.kastl@gmail.com"), + person(given = "Alex", + family = "Axthelm", + role = c("aut", "ctr"), + email = "aaxthelm@rmi.org", + comment = c(ORCID = "0000-0001-8579-8565")), + person(given = "RMI", + role = c("cph", "fnd"), + email = "PACTA4investors@rmi.org")) +Description: This package provides tools to prepare input datasets to be run in + the PACTA_analysis tool. +License: MIT + file LICENSE +Encoding: UTF-8 +Language: en-US +Roxygen: list(markdown = TRUE) +RoxygenNote: 7.3.1 +Imports: + cli (>= 3.5.0), + countrycode, + data.table, + devtools, + dplyr (>= 1.1.0), + dtplyr, + jsonlite, + lubridate, + rlang, + stats, + stringr, + tidyr, + tidyselect (>= 1.2.0), + withr +Depends: + R (>= 3.4.0) +LazyData: true +Suggests: + here, + knitr, + pkgdown, + readr, + readxl, + rmarkdown, + rstudioapi, + testthat (>= 3.0.0) +Config/testthat/edition: 3 +Config/Needs/website: rmi-pacta/pacta.pkgdown.rmitemplate +URL: https://github.com/RMI-PACTA/pacta.data.preparation +BugReports: https://github.com/RMI-PACTA/pacta.data.preparation/issues +VignetteBuilder: knitr diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d574414 --- /dev/null +++ b/LICENSE @@ -0,0 +1,2 @@ +YEAR: 2022 +COPYRIGHT HOLDER: pacta.data.preparation authors diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..eac66ab --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,21 @@ +# MIT License + +Copyright (c) 2022 pacta.data.preparation authors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/NAMESPACE b/NAMESPACE new file mode 100644 index 0000000..b55763f --- /dev/null +++ b/NAMESPACE @@ -0,0 +1,68 @@ +# Generated by roxygen2: do not edit by hand + +export(calc_shares_all_classes) +export(dataprep_abcd_scen_connection) +export(determine_relevant_years) +export(import_ar_advanced_company_indicators) +export(import_ar_masterdata) +export(prepare_abcd_flags_bonds) +export(prepare_abcd_flags_equity) +export(prepare_ar_company_id__country_of_domicile) +export(prepare_ar_company_id__credit_parent_ar_company_id) +export(prepare_ar_company_id__sectors_with_assets__debt) +export(prepare_ar_company_id__sectors_with_assets__ownership) +export(prepare_company_id__creditor_company_id) +export(prepare_entity_info) +export(prepare_factset_entity_id__ar_company_id) +export(prepare_factset_entity_id__credit_parent_id) +export(prepare_factset_entity_id__security_mapped_sector) +export(prepare_financial_data) +export(prepare_fund_data) +export(prepare_isin_to_fund_table) +export(prepare_iss_average_sector_emission_intensities) +export(prepare_iss_company_emissions) +export(prepare_iss_entity_emission_intensities) +export(prepare_masterdata) +export(prepare_masterdata_debt) +export(prepare_total_fund_list) +export(quarter_to_factset_timestamp) +export(quarter_to_imf_timestamp) +export(standardize_asset_type_names) +export(write_manifest) +importFrom(cli,cli_abort) +importFrom(cli,cli_warn) +importFrom(countrycode,countrycode) +importFrom(data.table,":=") +importFrom(dplyr,"%>%") +importFrom(dplyr,across) +importFrom(dplyr,arrange) +importFrom(dplyr,as_tibble) +importFrom(dplyr,collect) +importFrom(dplyr,distinct) +importFrom(dplyr,filter) +importFrom(dplyr,group_by) +importFrom(dplyr,if_else) +importFrom(dplyr,inner_join) +importFrom(dplyr,left_join) +importFrom(dplyr,mutate) +importFrom(dplyr,n_distinct) +importFrom(dplyr,rename) +importFrom(dplyr,select) +importFrom(dplyr,summarise) +importFrom(dplyr,tbl) +importFrom(dplyr,transmute) +importFrom(dplyr,ungroup) +importFrom(dtplyr,lazy_dt) +importFrom(jsonlite,fromJSON) +importFrom(lubridate,quarter) +importFrom(lubridate,year) +importFrom(lubridate,ymd) +importFrom(rlang,is_installed) +importFrom(rlang,syms) +importFrom(stats,setNames) +importFrom(tidyr,complete) +importFrom(tidyr,nesting) +importFrom(tidyr,pivot_longer) +importFrom(tidyr,unnest) +importFrom(tidyselect,where) +importFrom(withr,defer) diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 0000000..c199ba7 --- /dev/null +++ b/NEWS.md @@ -0,0 +1,65 @@ +# pacta.data.preparation 0.1.0.9003 + +* Gained functions `prepare_abcd_flags_equity()` and `prepare_abcd_flags_debt()` to prepare (#353). + +* Gained function `prepare_isin_to_fund_table()` to prepare an isin-to-fund lookup table, filtering out fsyms that have more than 1 row and either no fund data or fund data for both rows (#352). + +* Gained function `prepare_fund_data()` to prepare fund data, filtering to funds with data according to a given threshold and adding a `MISSINGWEIGHT` holding for the difference (#325). + +* added numerous, minor `prepare_*()` functions (#348). + +* `import_ar_advanced_company_indicators()` output now includes all parental attributes from the "Company Ownership" tab (#344). + +* `import_ar_advanced_company_indicators()` gains an `as_factor` arg to specify whether or not character columns should be auto-converted to factor (default `TRUE`, maintaining previous behavior) (#345). + +* *breaking change* - `write_manifest()` now takes a pair of vectors with filepaths to input and output files, rather than a set of directories (#340). + +# pacta.data.preparation 0.1.0.9002 + +* *breaking change* - `prepare_entity_info()` has a new mandatory argument `factset_manual_pacta_sector_override` because the internally accessible `factset_manual_pacta_sector_override` has been removed (#338). + +* *breaking change* - `factset_manual_pacta_sector_override` has been removed and is now exported by [workflow.factset](https://github.com/RMI-PACTA/workflow.factset) (#338). + +* *breaking change* - `prepare_entity_info()` has a new mandatory argument `factset_industry_map_bridge` because the internally accessible `factset_industry_map_bridge` has been removed (#336). + +* *breaking change* - `factset_industry_map_bridge` has been removed and is now exported by [workflow.factset](https://github.com/RMI-PACTA/workflow.factset) (#336). + +* `factset_issue_code_bridge` has been removed and is now exported by [workflow.factset](https://github.com/RMI-PACTA/workflow.factset) (#335). + +* *breaking change* - `write_manifest()` arguments have changed to allow passing an input directory specifically for FactSet data + +* FactSet database connection functions have been removed and migrated to https://github.com/RMI-PACTA/workflow.factset + +* release version of {dbplyr} 2.4.0 or greater is now required + +* `import_ar_advanced_company_indicators()` now also imports the `is_ultimate_listed_parent` column (only for individual companies, not the entire ownership tree, e.g. `` `Ownership Level` == 0``) from the somewhat new "Company Ownership" tab. This breaks compatibility with older PAMS datasets that do not have this tab. + +# pacta.data.preparation 0.1.0 + +* Gained functions `quarter_to_factset_timestamp()` and `quarter_to_imf_timestamp()`, that convert PACTA style quarter string references (e.g. "2021Q4") to the equivalent FactSet and IMF references (e.g. "2021-12-31" and "2021-Q4" respectively) + +* Data scraping functions (`get_currency_exchange_rates()`, `get_ishares_index_data()`, `process_ishares_index_data()`, and `index_regions`) have been removed and extracted to a new [{pacta.data.scraping} package](https://github.com/RMI-PACTA/pacta.data.scraping) + +* Gained functions: (`get_factset_entity_financing_data()`, `get_factset_entity_info()`, `get_factset_financial_data()`, `get_factset_fund_data()`, `get_factset_isin_to_fund_table()`, `get_factset_iss_emissions_data()`, `write_manifest()`, and `dataprep_abcd_scen_connection()`) + +* `prepare_masterdata` gained functionality to handle Power technologies both with and without `"Cap` suffix (#285). + +* `factset_issue_code_bridge` gained updated mapping of FactSet `asset_type_code` to PACTA `asset_type` (#261). + +* Several changes to facilitate a new share ownership methodology: (#268) + * `one_adr_eq` value from the `own_v5_own_sec_adr_ord_ratio` table is included in the `get_factset_financial_data()` output + * new function `calc_shares_all_classes()` added + * `prepare_financial_data()` now calculates and exports `current_shares_outstanding_all_classes` column according to the new share ownership methodology, summing the outstanding shares of all relevant equity classes for a given entity + * now depends on dplyr (>= 1.1.0) + * {knitr} and {rmarkdown} have been added to Suggests to support vignette rendering + * new "Share ownership weight attribution method" vignette. see `vignette("share_ownership_methodology")` + +* Workflow script and Dockerfile removed/extracted into a new [workflow.data.preparation](https://github.com/RMI-PACTA/workflow.data.preparation) repo. + +* Bond roll-up is now calculated. Holdings are roll-ed up with priority to the FactSet defined credit parent, followed by the AI defined credit parent (#243). + +* Gained functions `prepare_masterdata`, `prepare_financial_data` and `prepare_entity_info` (#235, #236, #237). + +# pacta.data.preparation 0.0.1 + +* Added a `NEWS.md` file to track changes to the package. diff --git a/R/apply_fair_share.R b/R/apply_fair_share.R new file mode 100644 index 0000000..f4416b1 --- /dev/null +++ b/R/apply_fair_share.R @@ -0,0 +1,61 @@ +apply_fair_share <- function(data, other_sector_list) { + out <- + data %>% + lazy_dt() %>% + group_by( + .data$scenario_source, + .data$scenario, + .data$id, + .data$equity_market, + .data$scenario_geography, + .data$year, + .data$ald_sector, + .data$technology + ) %>% + mutate( + scen_tech_prod = if_else(.data$direction == "increasing", + max(.data$ref_plan_tech_prod + (.data$fair_share_perc * .data$ref_plan_sector_prod), 0), + max(.data$ref_plan_tech_prod + (.data$fair_share_perc * .data$ref_plan_tech_prod), 0) + ), + scen_tech_prod = if_else(.data$ald_sector %in% .env$other_sector_list, .data$ref_plan_tech_prod, .data$scen_tech_prod), + scen_emission_factor = .data$ref_emission_factor + (.data$fair_share_perc * .data$ref_emission_factor) + ) %>% + ungroup("technology") %>% + mutate(scen_sec_prod = sum(.data$scen_tech_prod, na.rm = TRUE)) %>% + mutate( + scen_tech_share = if_else(.data$scen_sec_prod != 0, .data$scen_tech_prod / .data$scen_sec_prod, NA_real_), + scen_br_wt_factor = .data$plan_br_wt_factor, + scen_br_wt_techshare = .data$scen_br_wt_factor * .data$scen_tech_share + ) %>% + ungroup() + + out <- + out %>% + group_by(.data$scenario_source, .data$scenario, .data$id, .data$year, .data$ald_sector) %>% + mutate( + scen_global_sec_prod = sum(if_else(.data$scenario_geography == "Global" & .data$equity_market == "GlobalMarket", .data$scen_tech_prod, 0), na.rm = TRUE), + scenario_exists = if_else(!is.na(.data$scenario), 1, 0) + ) %>% + ungroup() + + out <- + out %>% + group_by( + .data$scenario_source, + .data$scenario, + .data$id, + .data$equity_market, + .data$scenario_geography, + .data$year, + .data$ald_sector + ) %>% + mutate( + plan_build_out = .data$plan_tech_prod - .data$ref_plan_tech_prod, + plan_build_out_tech_share = .data$plan_build_out / sum(.data$plan_build_out, na.rm = TRUE), + scen_build_out = .data$scen_tech_prod - .data$ref_plan_tech_prod + ) %>% + ungroup() %>% + as_tibble() + + return(out) +} diff --git a/R/calc_shares_all_classes.R b/R/calc_shares_all_classes.R new file mode 100644 index 0000000..b9f441a --- /dev/null +++ b/R/calc_shares_all_classes.R @@ -0,0 +1,33 @@ +#' Calculate the number of shares across all equity asset classes +#' +#' @description `calc_shares_all_classes()` calculates the number of shares +#' across all equity asset classes. The asset class is defined by the value in +#' `asset_type_id`, and only asset classes included in `include_types` are +#' summed. +#' +#' `calc_shares_all_classes()` assumes that all input values are from the same +#' entity/company, so a single summed value is returned. If it is desired to +#' calculate the shares in all classes for multiple entities at the same time, +#' a typical usage would be to apply this function "by" some entity id, e.g. +#' +#' `mutate(data, shares_all_classes = +#' calc_shares_all_classes(shares_outstanding, asset_type_id), .by = +#' "entity_id")` +#' +#' For more details see `vignette("share_ownership_methodology")`. +#' +#' @param shares_outstanding (numeric vector) contains the number of shares +#' outstanding +#' @param asset_type_id (character vector) contains an the asset type identifier +#' @param include_types (character vector) defines which of the `asset_type_id` +#' values should be included in the sum +#' +#' @return a numeric value containing the number of shares across all equity +#' asset classes +#' +#' @export + +calc_shares_all_classes <- + function(shares_outstanding, asset_type_id, include_types = c("CP", "EQ", "PF")) { + sum(shares_outstanding[asset_type_id %in% include_types], na.rm = TRUE) + } diff --git a/R/check_crucial_names.R b/R/check_crucial_names.R new file mode 100644 index 0000000..9e99fe5 --- /dev/null +++ b/R/check_crucial_names.R @@ -0,0 +1,35 @@ +#' Check if a named object contains expected names +#' +#' Based on r2dii.analysis::check_crucial_names() +#' +#' @param x A named object. +#' @param expected_names String; expected names of `x`. +#' +#' @return Invisible `x`, or an error with informative message. +#' +#' @examples +#' x <- c(a = 1) +#' check_crucial_names(x, "a") +#' try(check_crucial_names(x, "bad")) +#' @noRd +check_crucial_names <- function(x, expected_names) { + stopifnot(rlang::is_named(x)) + stopifnot(is.character(expected_names)) + + ok <- all(unique(expected_names) %in% names(x)) + if (!ok) { + abort_missing_names(sort(setdiff(expected_names, names(x)))) + } + + invisible(x) +} + +abort_missing_names <- function(missing_names) { + rlang::abort( + "missing_names", + message = paste0( + "Must have missing names: ", + paste0("`", missing_names, "`", collapse = ", ") + ) + ) +} diff --git a/R/conversion_functions.R b/R/conversion_functions.R new file mode 100644 index 0000000..dc65da2 --- /dev/null +++ b/R/conversion_functions.R @@ -0,0 +1,37 @@ +expand_by_scenario_geography <- + function(data, scen_geos, scenario_regions, .default = "Global", .iso2c = "ald_location") { + stopifnot(.iso2c %in% names(data)) + stopifnot(all(scen_geos %in% scenario_regions$scenario_geography)) + + dict <- + scenario_regions %>% + filter(.data$scenario_geography %in% .env$scen_geos) %>% + select("country_iso", "scenario_geography") %>% + distinct() + + data %>% + left_join(dict, by = stats::setNames("country_iso", .iso2c)) %>% + mutate(scenario_geography = dplyr::case_when( + is.na(.data$scenario_geography) ~ .default, + .data$scenario_geography == "" ~ .default, + TRUE ~ .data$scenario_geography + )) + } + + +expand_by_country_of_domicile <- + function(data, index_regions, .default = "GlobalMarket", .iso2c = "country_of_domicile") { + stopifnot(.iso2c %in% names(data)) + + dict <- index_regions %>% + select("equity_market", "country_iso") %>% + distinct() + + data %>% + left_join(dict, by = stats::setNames("country_iso", .iso2c)) %>% + mutate(equity_market = dplyr::case_when( + is.na(.data$equity_market) ~ .env$.default, + .data$equity_market == "" ~ .env$.default, + TRUE ~ .data$equity_market + )) + } diff --git a/R/dataprep_abcd_scen_connection.R b/R/dataprep_abcd_scen_connection.R new file mode 100644 index 0000000..cf58900 --- /dev/null +++ b/R/dataprep_abcd_scen_connection.R @@ -0,0 +1,407 @@ +#' Combine ABCD and scenario data into the `[equity/bonds]_abcd_scenario.rds` +#' format that is used by portfolio.analysis +#' +#' @param abcd_data A tibble containing the ABCD data +#' @param scenario_data A tibble containing the scenario data +#' @param reference_year A single numeric specifying the market share target +#' reference year +#' @param relevant_years A numeric vector containing all relevant years to be +#' calculated +#' @param tech_exclude A character vector containing the technologies to be +#' excluded +#' @param scenario_geographies_list A character vector containing the scenario +#' geographies to be used +#' @param sector_list A character vector containing the sectors to be included +#' @param other_sector_list A character vector containing the sectors considered +#' "other" +#' @param global_aggregate_scenario_sources_list A character vector containing +#' the scenario sources to be included in the global aggreagte +#' @param global_aggregate_sector_list A character vector containing the sectors +#' to be included in the global aggregate +#' @param scenario_regions A character vector containing the scenario regions +#' @param index_regions A character vector containing the index regions +#' +#' @return A tibble with the combined ABCD and scenario data +#' +#' @export + +dataprep_abcd_scen_connection <- function(abcd_data, + scenario_data, + reference_year, + relevant_years, + tech_exclude, + scenario_geographies_list, + sector_list, + other_sector_list, + global_aggregate_scenario_sources_list, + global_aggregate_sector_list, + scenario_regions, + index_regions) { + abcd_scenario <- dataprep_connect_abcd_with_scenario( + abcd_data, + scenario_data, + global_aggregate = FALSE, + reference_year = reference_year, + relevant_years = relevant_years, + tech_exclude = tech_exclude, + scenario_geographies_list = scenario_geographies_list, + sector_list = sector_list, + other_sector_list = other_sector_list, + global_aggregate_scenario_sources_list = global_aggregate_scenario_sources_list, + global_aggregate_sector_list = global_aggregate_sector_list, + scenario_regions, + index_regions + ) + + abcd_scenario_global_aggregate <- dataprep_connect_abcd_with_scenario( + abcd_data, + scenario_data, + global_aggregate = TRUE, + reference_year = reference_year, + relevant_years = relevant_years, + tech_exclude = tech_exclude, + scenario_geographies_list = scenario_geographies_list, + sector_list = sector_list, + other_sector_list = other_sector_list, + global_aggregate_scenario_sources_list = global_aggregate_scenario_sources_list, + global_aggregate_sector_list = global_aggregate_sector_list, + scenario_regions, + index_regions + ) + + + # Combine Global aggregate dataset with regional dataset: + abcd_scenario_short <- dplyr::bind_rows( + abcd_scenario, + abcd_scenario_global_aggregate + ) + + return(abcd_scenario_short) +} + +dataprep_connect_abcd_with_scenario <- function(abcd_data, + scenario_data, + global_aggregate = FALSE, + reference_year, + relevant_years, + tech_exclude, + scenario_geographies_list, + sector_list, + other_sector_list, + global_aggregate_scenario_sources_list, + global_aggregate_sector_list, + scenario_regions, + index_regions) { + scenario_data <- scenario_data[!(grepl("Cap", scenario_data$technology) & scenario_data$ald_sector == "Demand"), ] + + ald <- abcd_data %>% + select( + "ald_sector", + "technology", + "year", + "id", + "country_of_domicile", + "ald_location", + "ald_production", + "ald_production_unit", + "ald_emissions_factor", + "id_name" + ) %>% + filter( + .data$year %in% relevant_years, + !is.na(.data$id) & !.data$id %in% c("NonListedProduction", "NoMatchingCorpBondTicker"), + !.data$technology %in% .env$tech_exclude + ) %>% + mutate( + ald_sector = dplyr::case_when( + .data$technology == "Coal" ~ "Coal", + .data$technology %in% c("Gas", "Oil") ~ "Oil&Gas", + TRUE ~ .data$ald_sector + ) + ) + + if (global_aggregate == TRUE) { + scenario_data <- + scenario_data %>% + filter(.data$scenario_source %in% .env$global_aggregate_scenario_sources_list) + + ald_ga_input <- + scenario_regions %>% + select("scenario_geography", "country_iso", "reg_count") %>% + unique() %>% + left_join( + unique( + select( + scenario_data, + "scenario_source", + "scenario", + "scenario_geography", + "ald_sector" + ) + ), + by = "scenario_geography" + ) %>% + group_by(.data$scenario_source, .data$scenario, .data$ald_sector, .data$country_iso) %>% + mutate(rank = rank(.data$reg_count, ties.method = "first")) %>% + filter( + .data$rank == 1, + .data$scenario_source %in% .env$global_aggregate_scenario_sources_list + ) %>% + select(-"reg_count", -"rank") + + ald_sr <- ald %>% + filter( + .data$ald_sector %in% .env$global_aggregate_sector_list + ) %>% + left_join( + ald_ga_input, + by = c("ald_location" = "country_iso", "ald_sector" = "ald_sector") + ) %>% + mutate( + scenario_geography = ifelse( + is.na(.data$scenario_geography) | .data$scenario_geography == "", + "Global", + .data$scenario_geography + ) + ) + } else { + ald_sr <- expand_by_scenario_geography(ald, scenario_geographies_list, scenario_regions) + } + + if (unique(ald_sr$id_name) == "ar_company_id") { + ald_sr_ir <- expand_by_country_of_domicile(ald_sr, index_regions) + } else { + ald_sr_ir <- ald_sr %>% mutate(equity_market = "GlobalMarket") + } + + if (global_aggregate == TRUE) { + ald_sr_ir_agg <- + ald_sr_ir %>% + group_by( + .data$scenario_source, + .data$scenario, + .data$id_name, + .data$id, + .data$equity_market, + .data$scenario_geography, + .data$ald_sector, + .data$technology, + .data$year + ) %>% + summarise( + plan_emission_factor = stats::weighted.mean( + .data$ald_emissions_factor, + .data$ald_production, + na.rm = TRUE + ), + plan_tech_prod = sum(.data$ald_production, na.rm = TRUE), + .groups = "drop" + ) %>% + mutate( + current_plan_row = 1 + ) %>% + ungroup() + } else { + ald_sr_ir_agg <- + ald_sr_ir %>% + group_by( + .data$id_name, + .data$id, + .data$equity_market, + .data$scenario_geography, + .data$ald_sector, + .data$technology, + .data$year + ) %>% + summarise( + plan_emission_factor = stats::weighted.mean( + .data$ald_emissions_factor, + .data$ald_production, + na.rm = TRUE + ), + plan_tech_prod = sum(.data$ald_production, na.rm = TRUE), + .groups = "drop" + ) %>% + mutate(current_plan_row = 1) + } + + + ### ########################################################################## # + ### ADD THE EXTRA TECHNOLOGY LINES ----- + ### ########################################################################## # + + ald_full <- ald_sr_ir_agg + ald_full <- expand_tech_rows(ald_full, global_aggregate = global_aggregate) + + + ### ########################################################################## # + ### DO CALCS WITHIN id, equity_market, SCENARIO GEOGRAPHY ---- + ### ########################################################################## # + + ald_full_calcs <- do_calcs_within_id(ald_full, reference_year = reference_year, global_aggregate = global_aggregate) + + + ### ########################################################################## # + ### ADD REFERENCE YEAR PRODUCTION PLANS ---- + ### ########################################################################## # + if (global_aggregate == TRUE) { + ald_full_calcs <- ald_full_calcs %>% + group_by( + .data$scenario_source, + .data$scenario, + .data$id, + .data$ald_sector, + .data$equity_market, + .data$scenario_geography, + .data$technology + ) + } else { + ald_full_calcs <- ald_full_calcs %>% + group_by( + .data$id, + .data$ald_sector, + .data$equity_market, + .data$scenario_geography, + .data$technology + ) + } + ald_full_calcs <- ald_full_calcs %>% + mutate( + ref_plan_tech_prod = sum( + if_else( + .data$year == .env$reference_year, + .data$plan_tech_prod, + 0 + ), + na.rm = TRUE + ), + ref_emission_factor = sum( + if_else( + .data$year == .env$reference_year, + .data$plan_emission_factor, + 0 + ), + na.rm = TRUE + ) + ) %>% + ungroup() + + ### ########################################################################## # + ### ADD SCENARIOS #### + ### ########################################################################## # + if (global_aggregate == TRUE) { + ald_scen <- + ald_full_calcs %>% + filter(.data$ald_sector %in% .env$sector_list) %>% + left_join( + scenario_data, + by = c("scenario_source", "scenario", "scenario_geography", "year", "ald_sector", "technology") + ) + + ald_scen_other <- + ald_full_calcs %>% + filter(.data$ald_sector %in% .env$other_sector_list) %>% + left_join( + select(scenario_data, -"technology"), + by = c("scenario_source", "scenario", "scenario_geography", "year", "ald_sector") + ) + } else { + ald_scen <- + ald_full_calcs %>% + filter(.data$ald_sector %in% .env$sector_list) %>% + left_join( + distinct(scenario_data), + by = c("scenario_geography", "year", "ald_sector", "technology") + ) + + ald_scen_other <- + ald_full_calcs %>% + filter(.data$ald_sector %in% .env$other_sector_list) %>% + left_join( + select(scenario_data, -"technology"), + by = c("scenario_geography", "year", "ald_sector") + ) + } + ald_scen <- rbind(ald_scen, ald_scen_other) + + + ### apply fair share + + ald_scen_calcs <- apply_fair_share(ald_scen, other_sector_list) + + + ### ########################################################################## # + ### AGGREGATION FOR GLOBAL AGGREGATE (IF SLECETED)#### + ### ########################################################################## # + if (global_aggregate == TRUE) { + ald_scen_calcs <- ald_scen_calcs %>% + group_by( + .data$scenario_source, + .data$scenario, + .data$id_name, + .data$id, + .data$equity_market, + .data$ald_sector, + .data$technology, + .data$year, + .data$scenario_exists + ) %>% + summarise( + plan_emission_factor = stats::weighted.mean(.data$plan_emission_factor, .data$plan_tech_prod, na.rm = TRUE), + scen_emission_factor = stats::weighted.mean(.data$scen_emission_factor, .data$scen_tech_prod, na.rm = TRUE), + plan_tech_prod = sum(.data$plan_tech_prod, na.rm = TRUE), + plan_br_wt_factor = sum(.data$plan_br_wt_factor, na.rm = TRUE), + plan_br_wt_techshare = sum(.data$plan_br_wt_techshare, na.rm = TRUE), + scen_tech_prod = sum(.data$scen_tech_prod, na.rm = TRUE), + scen_br_wt_factor = sum(.data$scen_br_wt_factor, na.rm = TRUE), + scen_br_wt_techshare = sum(.data$scen_br_wt_techshare, na.rm = TRUE), + ref_plan_tech_prod = sum(.data$ref_plan_tech_prod, na.rm = TRUE), + ref_plan_sector_prod = sum(.data$ref_plan_sector_prod, na.rm = TRUE) + ) %>% + mutate( + scenario_geography = "GlobalAggregate", + fair_share_perc = NA, + direction = NA, + current_plan_row = if_else(.data$ref_plan_tech_prod > 0, 1, 0) + ) %>% + ungroup() + } + + + ### ########################################################################## # + ### ADD ID COLS AND REORDER ##### + ### ########################################################################## # + ald_scen_calcs_with_id <- ald_scen_calcs %>% + group_by(.data$id, .data$ald_sector) %>% + mutate(ald_company_sector_id = dplyr::cur_group_id()) %>% + ungroup() + + ald_scen_calcs_short <- + ald_scen_calcs_with_id %>% + select( + "scenario_source", + "scenario", + "id_name", + "id", + "ald_company_sector_id", + "equity_market", + "scenario_geography", + "ald_sector", + "technology", + "year", + "plan_tech_prod", + "plan_br_wt_factor", + "plan_br_wt_techshare", + "plan_emission_factor", + "scen_tech_prod", + "scen_br_wt_factor", + "scen_br_wt_techshare", + "scen_emission_factor", + "current_plan_row", + "scenario_exists" + ) %>% + filter(.data$scenario_exists == 1) %>% + ungroup() + + return(ald_scen_calcs_short) +} diff --git a/R/determine_relevant_years.R b/R/determine_relevant_years.R new file mode 100644 index 0000000..662a080 --- /dev/null +++ b/R/determine_relevant_years.R @@ -0,0 +1,17 @@ +#' Determine relevant years +#' +#' @param market_share_target_reference_year A single numeric value determining +#' the Market Share target reference year +#' @param time_horizon A single numeric value determining the number of forward +#' looking years +#' +#' @return A numeric vector containg all of the relevant years +#' +#' @export + +determine_relevant_years <- function(market_share_target_reference_year, + time_horizon) { + sort(unique( + market_share_target_reference_year:(market_share_target_reference_year + time_horizon) + )) +} diff --git a/R/do_calcs_within_id.R b/R/do_calcs_within_id.R new file mode 100644 index 0000000..7dbb185 --- /dev/null +++ b/R/do_calcs_within_id.R @@ -0,0 +1,111 @@ +do_calcs_within_id <- function(ald_full, + reference_year, + global_aggregate) { + if (global_aggregate == TRUE) { + ald_full <- ald_full %>% + group_by( + .data$scenario_source, + .data$scenario, + .data$id, + .data$equity_market, + .data$scenario_geography, + .data$year, + .data$ald_sector + ) + } else { + ald_full <- ald_full %>% + group_by( + .data$id, + .data$equity_market, + .data$scenario_geography, + .data$year, + .data$ald_sector + ) + } + + ald_full_calcs <- ald_full %>% + mutate( + plan_sector_prod = sum(.data$plan_tech_prod, na.rm = TRUE), + plan_tech_share = if_else( + .data$plan_sector_prod != 0, + .data$plan_tech_prod / .data$plan_sector_prod, + NA_real_ + ) + ) %>% + ungroup() + + if (global_aggregate == TRUE) { + ald_full_calcs <- + ald_full_calcs %>% + group_by( + .data$id, + .data$ald_sector, + .data$equity_market, + .data$scenario, + .data$scenario_geography + ) %>% + mutate( + ref_plan_sector_prod = sum( + if_else( + .data$year == .env$reference_year, + .data$plan_tech_prod, + 0 + ), + na.rm = TRUE + ) + ) %>% + group_by(.data$id, .data$ald_sector, .data$scenario) %>% + mutate( + plan_global_sec_prod_ref = sum( + if_else( + .data$equity_market == "GlobalMarket" & .data$year == .env$reference_year, + .data$plan_tech_prod, + 0 + ), + na.rm = TRUE + ), + plan_br_wt_factor = if_else( + .data$plan_global_sec_prod_ref != 0, + .data$ref_plan_sector_prod / .data$plan_global_sec_prod_ref, + NA_real_ + ), + plan_br_wt_techshare = .data$plan_br_wt_factor * .data$plan_tech_share + ) %>% + ungroup() + } else { + ald_full_calcs <- + ald_full_calcs %>% + group_by(.data$id, .data$ald_sector) %>% + mutate( + plan_global_sec_prod_ref = sum( + if_else( + .data$scenario_geography == "Global" & .data$equity_market == "GlobalMarket" & .data$year == .env$reference_year, + .data$plan_tech_prod, + 0 + ), + na.rm = TRUE + ) + ) %>% + group_by( + .data$id, + .data$ald_sector, + .data$equity_market, + .data$scenario_geography + ) %>% + mutate( + ref_plan_sector_prod = sum( + if_else(.data$year == .env$reference_year, .data$plan_tech_prod, 0), + na.rm = TRUE + ) + ) %>% + ungroup() %>% + mutate( + plan_br_wt_factor = if_else( + .data$plan_global_sec_prod_ref != 0, + .data$ref_plan_sector_prod / .data$plan_global_sec_prod_ref, + NA_real_ + ), + plan_br_wt_techshare = .data$plan_br_wt_factor * .data$plan_tech_share + ) + } +} diff --git a/R/expand_tech_rows.R b/R/expand_tech_rows.R new file mode 100644 index 0000000..40b72e5 --- /dev/null +++ b/R/expand_tech_rows.R @@ -0,0 +1,20 @@ +expand_tech_rows <- function(data, global_aggregate = FALSE) { + na_list <- list( + plan_tech_prod = 0, + current_plan_row = 0 + ) + + group_names <- c("id_name", "id", "equity_market", "scenario_geography") + if (global_aggregate) group_names <- c("scenario_source", "scenario", group_names) + + data <- dplyr::group_by(data, .data$ald_sector) + data <- + tidyr::complete( + data, + tidyr::nesting(!!!rlang::syms(group_names)), + tidyr::nesting(!!rlang::sym("technology")), + year, + fill = na_list + ) + dplyr::ungroup(data) +} diff --git a/R/filter_most_recent_timestamp.R b/R/filter_most_recent_timestamp.R new file mode 100644 index 0000000..9bfcb7a --- /dev/null +++ b/R/filter_most_recent_timestamp.R @@ -0,0 +1,43 @@ +#' Pick the most recent value of `asset_level_timestamp` relative to `timestamp` +#' +#' Filter a dataset for values of `asset_level_timestamp`, that are most recent +#' relative to some input `timestamp`. +#' +#' @param data A dataframe containing the column `asset_level_timestamp`. +#' @param timestamp A character indicating the desired timestamp, by year and +#' quarter, "YYYYQQ" (e.g. "2020Q1"). +#' @param ... Variables to group by. +#' +#' @return A dataframe where the value of `asset_level_timestamp` is filtered to +#' the most recent value, relative to `timestamp` (by group, if provided). +#' +#' @examples +#' data <- tibble::tribble( +#' ~sector, ~asset_level_timestamp, +#' "aviation", "2021Q4", +#' "power", "2021Q1", +#' "power", "2021Q2", +#' "automotive", "2020Q4", +#' "automotive", "2022Q1" +#' ) +#' +#' timestamp <- "2021Q4" +#' +#' filter_most_recent_timstamp(data, timestamp, sector) +#' @noRd + +filter_most_recent_timestamp <- function(data, timestamp, ...) { + stopifnot( + is.data.frame(data), + is.character(timestamp), + "asset_level_timestamp" %in% names(data) + ) + + data %>% + mutate(.asset_level_timestamp = lubridate::yq(.data$asset_level_timestamp)) %>% + group_by(...) %>% + filter(.data$.asset_level_timestamp <= lubridate::yq(.env$timestamp)) %>% + filter(.data$.asset_level_timestamp == max(.data$.asset_level_timestamp)) %>% + select(-".asset_level_timestamp") %>% + ungroup() +} diff --git a/R/import_ar_advanced_company_indicators.R b/R/import_ar_advanced_company_indicators.R new file mode 100644 index 0000000..2f12bdb --- /dev/null +++ b/R/import_ar_advanced_company_indicators.R @@ -0,0 +1,169 @@ +#' Import the data from a version of Asset Resolution's proprietary Advanced +#' Company Indicators XLSX into a tidy data frame. +#' +#' @param filepath Path to the XLSX file. +#' @param drop_nas A logical indicating whether rows with an `NA` value after +#' pivoting to long-format should be dropped (default is `TRUE`). +#' @param fix_names A logical indicating whether the column names should be +#' fixed to snakecase format. (e.g. `Company Name` becomes `company_name`). By +#' default, column names are not changed (i.e. `FALSE`). +#' @param as_factor A logical indicating whether the character columns should be +#' converted to factors(default is `TRUE`). +#' +#' @return A tibble including all the data from the "Company Information", +#' "Company ISINs", "Company Emissions", and "Company Activities" tabs +#' combined into one tidy tibble. +#' +#' @export + +import_ar_advanced_company_indicators <- + function(filepath, drop_nas = TRUE, fix_names = FALSE, as_factor = TRUE) { + stopifnot( + is.character(filepath), + file.exists(filepath), + file.access(filepath, mode = 4) == 0, + is.logical(drop_nas), + is.logical(fix_names) + ) + + stopifnot( + requireNamespace("readxl", quietly = TRUE), + !is.na(readxl::excel_format(filepath)) + ) + + sheet_names <- readxl::excel_sheets(path = filepath) + + stopifnot( + "Company Information" %in% sheet_names, + "Company Ownership" %in% sheet_names, + "Company ISINs" %in% sheet_names, + "Company Emissions" %in% sheet_names, + "Company Activities" %in% sheet_names + ) + + company_info <- + readxl::read_excel(path = filepath, sheet = "Company Information") %>% + select(-"Is Ultimate Parent") # also included in "Company Ownership" tab + + company_ownership <- + readxl::read_excel(path = filepath, sheet = "Company Ownership") %>% + filter(.data$`Ownership Level` == 0) %>% + select( + "Company ID", + dplyr::any_of(c( + "Is Parent", + "Is Credit Parent", + "Is Ultimate Parent", + "Is Ultimate Listed Parent" + )) + ) + + company_isins <- + readxl::read_excel(path = filepath, sheet = "Company ISINs") %>% + group_by(.data$`Company ID`) %>% + summarise(ISINs = list(.data$ISIN)) + + company_emissions <- + readxl::read_excel(path = filepath, sheet = "Company Emissions") %>% + pivot_longer( + cols = dplyr::matches(" [0-9]{4}$"), + names_to = c("consolidation_method", "year"), + names_pattern = "^(.*) (20[0-9]{2})$", + names_transform = list("year" = as.integer), + values_to = "value", + values_ptypes = numeric(), + values_drop_na = drop_nas + ) %>% + mutate(value_type = "emission_intensity", .before = "year") %>% + select(-"Company Name") + + company_activities <- + readxl::read_excel(path = filepath, sheet = "Company Activities") %>% + pivot_longer( + cols = dplyr::matches(" [0-9]{4}$"), + names_to = c("consolidation_method", "year"), + names_pattern = "^(.*) (20[0-9]{2})$", + names_transform = list("year" = as.integer), + values_to = "value", + values_ptypes = numeric(), + values_drop_na = drop_nas + ) %>% + mutate(value_type = "production", .before = "year") %>% + select(-"Company Name") + + output <- + company_info %>% + dplyr::full_join(company_ownership, by = "Company ID") %>% + dplyr::full_join(company_isins, by = "Company ID") %>% + dplyr::right_join( + dplyr::bind_rows(company_emissions, company_activities), + by = "Company ID" + ) + + if (fix_names) { + names(output) <- gsub(" ", "_", tolower(names(output))) + } + + if (as_factor) { + output <- mutate(output, across(where(is.character), as.factor)) + } + + output + } + + +#' Import the data from a version of Asset Resolution's bespoke +#' `masterdata_*.csv` files into a tidy data frame. +#' +#' @param filepath Path to the CSV file. +#' @param drop_nas A logical indicating whether rows with an `NA` value after +#' pivoting to long-format should be dropped (deafult is `TRUE`). +#' @param id_as_string A logical indicating whether the `company_id` column +#' should be imported as a character vector. By default, the `company_id` +#' column is imported as a numeric (i.e. `FALSE`). +#' +#' @return A tidy, long-format tibble of all the data in the `masterdata_*.csv` +#' file with an added `consolidation_method` column to record which of +#' "ownership" or "debt" file was imported. +#' +#' @export + +import_ar_masterdata <- + function(filepath, drop_nas = TRUE, id_as_string = FALSE) { + stopifnot( + is.character(filepath), + file.exists(filepath), + file.access(filepath, mode = 4) == 0, + is.logical(drop_nas), + is.logical(id_as_string) + ) + + stopifnot( + requireNamespace("readr", quietly = TRUE) + ) + + col_spec <- + readr::cols( + company_id = if_else(id_as_string, "c", "d"), + .default = "?" + ) + + readr::read_csv( + file = filepath, + na = "", + col_types = col_spec + ) %>% + pivot_longer( + cols = dplyr::matches("^_20[0-9]{2}$"), + names_to = "year", + names_prefix = "_", + names_transform = list(year = as.integer), + values_to = "value", + values_ptypes = numeric(), + values_drop_na = drop_nas + ) %>% + mutate( + consolidation_method = sub(pattern = "^.*masterdata_(.*)_.*$", "\\1", basename(filepath)), + .before = "year" + ) + } diff --git a/R/override_sector_by_factset_entity_id.R b/R/override_sector_by_factset_entity_id.R new file mode 100644 index 0000000..efa46b8 --- /dev/null +++ b/R/override_sector_by_factset_entity_id.R @@ -0,0 +1,45 @@ +#' Overwrite the value of `security_mapped_sector` by `factset_entity_id` +#' +#' This function will overwrite the value of the column `security_mapped_sector` +#' for every `factset_entity_id`. If no over-write value isn't present, it will +#' return the original value. Can be used, for example, to manually override +#' these values in the `entity_info` dataset. +#' +#' @param data A data-frame with the crucial columns `factset_entity_id` and +#' `security_mapped_sector`. +#' @param sector_overrides A data-frame of sector overrides, with crucial columns +#' `factset_entity_id` and `pacta_sector_override`. +#' +#' @return A data-frame where the input column `security_mapped_sector` has been +#' overwritten with values from the column `pacta_sector_override`. +#' +#' @noRd +override_sector_by_factset_entity_id <- function(data, sector_overrides) { + stopifnot( + is.data.frame(data), + is.data.frame(sector_overrides) + ) + + crucial_names <- c("factset_entity_id", "security_mapped_sector") + check_crucial_names(data, crucial_names) + + crucial_names_sector_overrides <- c("factset_entity_id", "pacta_sector_override") + check_crucial_names(sector_overrides, crucial_names_sector_overrides) + + sector_overrides <- select( + sector_overrides, + dplyr::all_of(crucial_names_sector_overrides) + ) + + original_names <- names(data) + + data %>% + left_join(sector_overrides, by = "factset_entity_id") %>% + mutate( + security_mapped_sector = dplyr::case_when( + !is.na(.data$pacta_sector_override) ~ .data$pacta_sector_override, + TRUE ~ .data$security_mapped_sector + ) + ) %>% + select(dplyr::all_of(original_names)) +} diff --git a/R/pacta.data.preparation-package.R b/R/pacta.data.preparation-package.R new file mode 100644 index 0000000..c48a997 --- /dev/null +++ b/R/pacta.data.preparation-package.R @@ -0,0 +1,47 @@ +#' @keywords internal +"_PACKAGE" + +## usethis namespace: start +#' @importFrom cli cli_abort +#' @importFrom cli cli_warn +#' @importFrom countrycode countrycode +#' @importFrom data.table := +#' @importFrom dplyr %>% +#' @importFrom dplyr across +#' @importFrom dplyr arrange +#' @importFrom dplyr as_tibble +#' @importFrom dplyr collect +#' @importFrom dplyr distinct +#' @importFrom dplyr filter +#' @importFrom dplyr group_by +#' @importFrom dplyr if_else +#' @importFrom dplyr inner_join +#' @importFrom dplyr left_join +#' @importFrom dplyr mutate +#' @importFrom dplyr n_distinct +#' @importFrom dplyr rename +#' @importFrom dplyr select +#' @importFrom dplyr summarise +#' @importFrom dplyr tbl +#' @importFrom dplyr transmute +#' @importFrom dplyr ungroup +#' @importFrom dtplyr lazy_dt +#' @importFrom jsonlite fromJSON +#' @importFrom lubridate quarter +#' @importFrom lubridate year +#' @importFrom lubridate ymd +#' @importFrom rlang is_installed +#' @importFrom rlang syms +#' @importFrom stats setNames +#' @importFrom tidyr complete +#' @importFrom tidyr nesting +#' @importFrom tidyr pivot_longer +#' @importFrom tidyr unnest +#' @importFrom tidyselect where +#' @importFrom withr defer +## usethis namespace: end +NULL + +globalVariables(".") +globalVariables(".data") +globalVariables(".env") diff --git a/R/prepare_abcd_flags_bonds.R b/R/prepare_abcd_flags_bonds.R new file mode 100644 index 0000000..a74ddf6 --- /dev/null +++ b/R/prepare_abcd_flags_bonds.R @@ -0,0 +1,75 @@ +#' Title +#' +#' @param financial_data A data frame containing financial data +#' @param factset_entity_id__ar_company_id A data frame containing a +#' factset_entity_id to ar_company_id look up table +#' @param factset_entity_id__security_mapped_sector A data frame containing a +#' factset_entity_id to security_mapped_sector look up table +#' @param ar_company_id__sectors_with_assets__debt A data frame containing +#' a ar_company_id to sectors_with_assets look up table for debt +#' @param factset_entity_id__credit_parent_id A data frame containing a +#' factset_entity_id to credit_parent_id look up table +#' +#' @return A data frame +#' +#' @export + +prepare_abcd_flags_bonds <- + function(financial_data, + factset_entity_id__ar_company_id, + factset_entity_id__security_mapped_sector, + ar_company_id__sectors_with_assets__debt, + factset_entity_id__credit_parent_id) { + stopifnot(is.data.frame(financial_data)) + stopifnot("factset_entity_id" %in% names(financial_data)) + + stopifnot(is.data.frame(factset_entity_id__ar_company_id)) + stopifnot( + all(c("factset_entity_id", "ar_company_id") %in% names(factset_entity_id__ar_company_id)) + ) + + stopifnot(is.data.frame(factset_entity_id__security_mapped_sector)) + stopifnot( + all(c("factset_entity_id", "security_mapped_sector") %in% names(factset_entity_id__security_mapped_sector)) + ) + + stopifnot(is.data.frame(ar_company_id__sectors_with_assets__debt)) + stopifnot( + all(c("ar_company_id", "sectors_with_assets") %in% names(ar_company_id__sectors_with_assets__debt)) + ) + + stopifnot(is.data.frame(factset_entity_id__credit_parent_id)) + stopifnot( + all(c("factset_entity_id", "credit_parent_id") %in% names(factset_entity_id__credit_parent_id)) + ) + + financial_data %>% + dplyr::left_join(factset_entity_id__ar_company_id, by = "factset_entity_id") %>% + dplyr::left_join(factset_entity_id__security_mapped_sector, by = "factset_entity_id") %>% + dplyr::left_join(ar_company_id__sectors_with_assets__debt, by = "ar_company_id") %>% + dplyr::mutate(has_asset_level_data = dplyr::if_else( + is.na(.data$sectors_with_assets) | .data$sectors_with_assets == "", + FALSE, + TRUE + )) %>% + dplyr::mutate(has_ald_in_fin_sector = dplyr::if_else( + stringr::str_detect(.data$sectors_with_assets, .data$security_mapped_sector), + TRUE, + FALSE + )) %>% + dplyr::left_join(factset_entity_id__credit_parent_id, by = "factset_entity_id") %>% + dplyr::mutate( + # If FactSet has no credit_parent, we define the company as it's own parent + credit_parent_id = dplyr::if_else( + is.na(.data$credit_parent_id), + .data$factset_entity_id, + .data$credit_parent_id + ) + ) %>% + dplyr::summarise( + has_asset_level_data = sum(.data$has_asset_level_data, na.rm = TRUE) > 0, + has_ald_in_fin_sector = sum(.data$has_ald_in_fin_sector, na.rm = TRUE) > 0, + sectors_with_assets = paste(sort(unique(stats::na.omit(unlist(stringr::str_split(.data$sectors_with_assets, pattern = " [+] "))))), collapse = " + "), + .by = "credit_parent_id" + ) + } diff --git a/R/prepare_abcd_flags_equity.R b/R/prepare_abcd_flags_equity.R new file mode 100644 index 0000000..3943ae0 --- /dev/null +++ b/R/prepare_abcd_flags_equity.R @@ -0,0 +1,58 @@ +#' Title +#' +#' @param financial_data A data frame containing financial data +#' @param factset_entity_id__ar_company_id A data frame containing a +#' factset_entity_id to ar_company_id look up table +#' @param factset_entity_id__security_mapped_sector A data frame containing a +#' factset_entity_id to security_mapped_sector look up table +#' @param ar_company_id__sectors_with_assets__ownership A data frame containing +#' a ar_company_id to sectors_with_assets look up table for ownership +#' +#' @return A data frame +#' +#' @export + +prepare_abcd_flags_equity <- + function(financial_data, + factset_entity_id__ar_company_id, + factset_entity_id__security_mapped_sector, + ar_company_id__sectors_with_assets__ownership) { + stopifnot(is.data.frame(financial_data)) + stopifnot("factset_entity_id" %in% names(financial_data)) + + stopifnot(is.data.frame(factset_entity_id__ar_company_id)) + stopifnot( + all(c("factset_entity_id", "ar_company_id") %in% names(factset_entity_id__ar_company_id)) + ) + + stopifnot(is.data.frame(factset_entity_id__security_mapped_sector)) + stopifnot( + all(c("factset_entity_id", "security_mapped_sector") %in% names(factset_entity_id__security_mapped_sector)) + ) + + stopifnot(is.data.frame(ar_company_id__sectors_with_assets__ownership)) + stopifnot( + all(c("ar_company_id", "sectors_with_assets") %in% names(ar_company_id__sectors_with_assets__ownership)) + ) + + financial_data %>% + dplyr::left_join(factset_entity_id__ar_company_id, by = "factset_entity_id") %>% + dplyr::left_join(factset_entity_id__security_mapped_sector, by = "factset_entity_id") %>% + dplyr::left_join(ar_company_id__sectors_with_assets__ownership, by = "ar_company_id") %>% + dplyr::mutate(has_asset_level_data = dplyr::if_else( + is.na(.data$sectors_with_assets) | .data$sectors_with_assets == "", + FALSE, + TRUE + )) %>% + dplyr::mutate(has_ald_in_fin_sector = dplyr::if_else( + stringr::str_detect(.data$sectors_with_assets, .data$security_mapped_sector), + TRUE, + FALSE + )) %>% + dplyr::select( + "isin", + "has_asset_level_data", + "has_ald_in_fin_sector", + "sectors_with_assets" + ) + } diff --git a/R/prepare_ar_company_id__country_of_domicile.R b/R/prepare_ar_company_id__country_of_domicile.R new file mode 100644 index 0000000..c8f9d9d --- /dev/null +++ b/R/prepare_ar_company_id__country_of_domicile.R @@ -0,0 +1,24 @@ +#' Prepare an `ar_company_id__country_of_domicile` lookup table from the +#' `entity_info` data +#' +#' @param entity_info A data frame containing the entity info +#' +#' @return A tibble +#' +#' @export + +prepare_ar_company_id__country_of_domicile <- function(entity_info) { + stopifnot(is.data.frame(entity_info)) + stopifnot( + all(c("ar_company_id", "country_of_domicile") %in% names(entity_info)) + ) + + out <- + entity_info %>% + dplyr::select("ar_company_id", "country_of_domicile") %>% + dplyr::filter(!is.na(.data$ar_company_id)) %>% + dplyr::distinct() + + stopifnot(all(!duplicated(out[["ar_company_id"]]))) + out +} diff --git a/R/prepare_ar_company_id__credit_parent_ar_company_id.R b/R/prepare_ar_company_id__credit_parent_ar_company_id.R new file mode 100644 index 0000000..5b4a593 --- /dev/null +++ b/R/prepare_ar_company_id__credit_parent_ar_company_id.R @@ -0,0 +1,24 @@ +#' Prepare an `ar_company_id__credit_parent_ar_company_id` lookup table from the +#' `entity_info` data +#' +#' @param entity_info A data frame containing the entity info +#' +#' @return A tibble +#' +#' @export + +prepare_ar_company_id__credit_parent_ar_company_id <- function(entity_info) { + stopifnot(is.data.frame(entity_info)) + stopifnot( + all(c("ar_company_id", "credit_parent_ar_company_id") %in% names(entity_info)) + ) + + out <- + entity_info %>% + dplyr::select("ar_company_id", "credit_parent_ar_company_id") %>% + dplyr::filter(!is.na(.data$ar_company_id)) %>% + dplyr::distinct() + + stopifnot(all(!duplicated(out[["ar_company_id"]]))) + out +} diff --git a/R/prepare_ar_company_id__sectors_with_assets__debt.R b/R/prepare_ar_company_id__sectors_with_assets__debt.R new file mode 100644 index 0000000..2a1f71b --- /dev/null +++ b/R/prepare_ar_company_id__sectors_with_assets__debt.R @@ -0,0 +1,37 @@ +#' Prepare an `ar_company_id__sectors_with_assets__debt` lookup table from the +#' `masterdata_debt_datastore` data +#' +#' @param masterdata_debt_datastore A data frame containing processed +#' production data from Asset Impact's masterdata_ownership CSV +#' @param relevant_years A numeric vector containing the relevant years of data +#' to include +#' +#' @return A tibble +#' +#' @export + +prepare_ar_company_id__sectors_with_assets__debt <- function( + masterdata_debt_datastore, + relevant_years) { + stopifnot(is.data.frame(masterdata_debt_datastore)) + stopifnot( + all(c("year", "id", "ald_sector") %in% names(masterdata_debt_datastore)) + ) + stopifnot(is.numeric(relevant_years)) + stopifnot(length(relevant_years) > 0L) + stopifnot(all(grepl("20[0-9]{2}", relevant_years))) + + out <- + masterdata_debt_datastore %>% + dplyr::filter(.data$year %in% .env$relevant_years) %>% + dplyr::select(ar_company_id = "id", "ald_sector") %>% + dplyr::filter(!is.na(.data$ar_company_id)) %>% + dplyr::distinct() %>% + dplyr::summarise( + sectors_with_assets = paste(unique(.data$ald_sector), collapse = " + "), + .by = "ar_company_id" + ) + + stopifnot(all(!duplicated(out[["ar_company_id"]]))) + out +} diff --git a/R/prepare_ar_company_id__sectors_with_assets__ownership.R b/R/prepare_ar_company_id__sectors_with_assets__ownership.R new file mode 100644 index 0000000..c2d5ba3 --- /dev/null +++ b/R/prepare_ar_company_id__sectors_with_assets__ownership.R @@ -0,0 +1,37 @@ +#' Prepare an `ar_company_id__sectors_with_assets__ownership` lookup table from +#' the `masterdata_ownership_datastore` data +#' +#' @param masterdata_ownership_datastore A data frame containing processed +#' production data from Asset Impact's masterdata_ownership CSV +#' @param relevant_years A numeric vector containing the relevant years of data +#' to include +#' +#' @return A tibble +#' +#' @export + +prepare_ar_company_id__sectors_with_assets__ownership <- function( + masterdata_ownership_datastore, + relevant_years) { + stopifnot(is.data.frame(masterdata_ownership_datastore)) + stopifnot( + all(c("year", "id", "ald_sector") %in% names(masterdata_ownership_datastore)) + ) + stopifnot(is.numeric(relevant_years)) + stopifnot(length(relevant_years) > 0L) + stopifnot(all(grepl("20[0-9]{2}", relevant_years))) + + out <- + masterdata_ownership_datastore %>% + dplyr::filter(.data$year %in% .env$relevant_years) %>% + dplyr::select(ar_company_id = "id", "ald_sector") %>% + dplyr::filter(!is.na(.data$ar_company_id)) %>% + dplyr::distinct() %>% + dplyr::summarise( + sectors_with_assets = paste(unique(.data$ald_sector), collapse = " + "), + .by = "ar_company_id" + ) + + stopifnot(all(!duplicated(out[["ar_company_id"]]))) + out +} diff --git a/R/prepare_company_id__creditor_company_id.R b/R/prepare_company_id__creditor_company_id.R new file mode 100644 index 0000000..41c3f5b --- /dev/null +++ b/R/prepare_company_id__creditor_company_id.R @@ -0,0 +1,30 @@ +#' Prepare a `company_id__creditor_company_id` lookup table from Asset Impact's +#' `masterdata_debt` data +#' +#' @param masterdata_debt A data frame containing raw production data from Asset +#' Impact's masterdata_debt CSV +#' +#' @return A tibble +#' +#' @export + +prepare_company_id__creditor_company_id <- function(masterdata_debt) { + stopifnot(is.data.frame(masterdata_debt)) + stopifnot( + all(c("company_id", "creditor_company_id") %in% names(masterdata_debt)) + ) + + out <- + masterdata_debt %>% + dplyr::select("company_id", "creditor_company_id") %>% + dplyr::filter(!is.na(.data$company_id)) %>% + dplyr::distinct() %>% + dplyr::mutate( + dplyr::across(.cols = dplyr::everything(), .fns = as.character) + ) + + stopifnot(all(!duplicated(out[["company_id"]]))) + stopifnot(is.character(out[["company_id"]])) + stopifnot(is.character(out[["creditor_company_id"]])) + out +} diff --git a/R/prepare_entity_info.R b/R/prepare_entity_info.R new file mode 100644 index 0000000..15bf1b5 --- /dev/null +++ b/R/prepare_entity_info.R @@ -0,0 +1,78 @@ +#' Prepare the `entity_info` output data frame from data frames imported from +#' the `factset_entity_info.rds` and `ar_company_id__factset_entity_id.rds` +#' files +#' +#' @param data A data frame containing the imported `factset_entity_info.rds` +#' file +#' @param factset_entity_id__ar_company_id A data frame containing the imported +#' `ar_company_id__factset_entity_id.rds` file with columns +#' `factset_entity_id` and `ar_company_id` +#' @param factset_industry_map_bridge A data frame containing the imported +#' `factset_industry_map_bridge.rds` +#' @param factset_manual_pacta_sector_override A data frame containing the imported +#' `factset_manual_pacta_sector_override` +#' @return A tibble properly prepared to be saved as the `entity_info.rds` +#' output file +#' +#' @export + +prepare_entity_info <- function( + data, + factset_entity_id__ar_company_id, + factset_industry_map_bridge, + factset_manual_pacta_sector_override) { + crucial_names <- c( + "factset_entity_id", + "entity_proper_name", + "iso_country", + "sector_code", + "factset_sector_desc", # + "credit_parent_id", + "industry_code" + ) + + check_crucial_names(data, crucial_names) + + crucial_names_factset_industry_map_bridge <- c( + "factset_industry_code", + "pacta_sector" + ) + + check_crucial_names( + factset_industry_map_bridge, + crucial_names_factset_industry_map_bridge + ) + + factset_industry_map_bridge_crucial <- select( + factset_industry_map_bridge, + dplyr::all_of(crucial_names_factset_industry_map_bridge) + ) + + data %>% + left_join(factset_entity_id__ar_company_id, by = "factset_entity_id") %>% + left_join( + rename(factset_entity_id__ar_company_id, credit_parent_ar_company_id = "ar_company_id"), + by = c("credit_parent_id" = "factset_entity_id") + ) %>% + left_join( + factset_industry_map_bridge_crucial, + by = c(industry_code = "factset_industry_code") + ) %>% + mutate(pacta_sector = if_else(is.na(.data$pacta_sector), "Other", .data$pacta_sector)) %>% + select( + "factset_entity_id", + company_name = "entity_proper_name", + country_of_domicile = "iso_country", + bics_sector_code = "sector_code", + bics_sector = "factset_sector_desc", + security_bics_subgroup_code = "industry_code", + security_bics_subgroup = "factset_industry_desc", + security_mapped_sector = "pacta_sector", + "ar_company_id", + "credit_parent_id", + "credit_parent_ar_company_id" + ) %>% + override_sector_by_factset_entity_id( + factset_manual_pacta_sector_override + ) +} diff --git a/R/prepare_factset_entity_id__ar_company_id.R b/R/prepare_factset_entity_id__ar_company_id.R new file mode 100644 index 0000000..8a5e7b8 --- /dev/null +++ b/R/prepare_factset_entity_id__ar_company_id.R @@ -0,0 +1,37 @@ +#' Prepare a `factset_entity_id__ar_company_id` lookup table from Asset Impact's +#' `ar_company_id__factset_entity_id` crosswalk +#' +#' @param ar_company_id__factset_entity_id A data frame containing a production +#' data company ID to financial data entity lookup table +#' +#' @return A tibble +#' +#' @export + +prepare_factset_entity_id__ar_company_id <- + function(ar_company_id__factset_entity_id) { + stopifnot(is.data.frame(ar_company_id__factset_entity_id)) + stopifnot( + all(c("factset_id", "company_id") %in% names(ar_company_id__factset_entity_id)) + ) + + out <- + ar_company_id__factset_entity_id %>% + dplyr::select( + factset_entity_id = "factset_id", + ar_company_id = "company_id" + ) %>% + dplyr::filter(!is.na(.data$factset_entity_id)) %>% + dplyr::filter(!is.na(.data$ar_company_id)) %>% + dplyr::distinct() %>% + dplyr::mutate( + dplyr::across(.cols = dplyr::everything(), .fns = as.character) + ) + + stopifnot(all(!duplicated(out[["factset_entity_id"]]))) + stopifnot(all(!is.na(out[["factset_entity_id"]]))) + stopifnot(all(!is.na(out[["ar_company_id"]]))) + stopifnot(is.character(out[["factset_entity_id"]])) + stopifnot(is.character(out[["ar_company_id"]])) + out + } diff --git a/R/prepare_factset_entity_id__credit_parent_id.R b/R/prepare_factset_entity_id__credit_parent_id.R new file mode 100644 index 0000000..f36cd80 --- /dev/null +++ b/R/prepare_factset_entity_id__credit_parent_id.R @@ -0,0 +1,32 @@ +#' Prepare a `factset_entity_id__credit_parent_id` lookup table from +#' `entity_info` +#' +#' @param entity_info A data frame containing the entity info +#' +#' @return A tibble +#' +#' @export + +prepare_factset_entity_id__credit_parent_id <- function(entity_info) { + stopifnot(is.data.frame(entity_info)) + stopifnot( + all(c("factset_entity_id", "credit_parent_id") %in% names(entity_info)) + ) + + out <- + entity_info %>% + dplyr::select("factset_entity_id", "credit_parent_id") %>% + dplyr::filter(!is.na(.data$factset_entity_id)) %>% + dplyr::filter(!is.na(.data$credit_parent_id)) %>% + dplyr::distinct() %>% + dplyr::mutate( + dplyr::across(.cols = dplyr::everything(), .fns = as.character) + ) + + stopifnot(all(!duplicated(out[["factset_entity_id"]]))) + stopifnot(all(!is.na(out[["factset_entity_id"]]))) + stopifnot(all(!is.na(out[["credit_parent_id"]]))) + stopifnot(is.character(out[["factset_entity_id"]])) + stopifnot(is.character(out[["credit_parent_id"]])) + out +} diff --git a/R/prepare_factset_entity_id__security_mapped_sector.R b/R/prepare_factset_entity_id__security_mapped_sector.R new file mode 100644 index 0000000..894c8ed --- /dev/null +++ b/R/prepare_factset_entity_id__security_mapped_sector.R @@ -0,0 +1,32 @@ +#' Prepare a `factset_entity_id__security_mapped_sector` lookup table from +#' `entity_info` +#' +#' @param entity_info A data frame containing the entity info +#' +#' @return A tibble +#' +#' @export + +prepare_factset_entity_id__security_mapped_sector <- function(entity_info) { + stopifnot(is.data.frame(entity_info)) + stopifnot( + all(c("factset_entity_id", "security_mapped_sector") %in% names(entity_info)) + ) + + out <- + entity_info %>% + dplyr::select("factset_entity_id", "security_mapped_sector") %>% + dplyr::filter(!is.na(.data$factset_entity_id)) %>% + dplyr::filter(!is.na(.data$security_mapped_sector)) %>% + dplyr::distinct() %>% + dplyr::mutate( + dplyr::across(.cols = dplyr::everything(), .fns = as.character) + ) + + stopifnot(all(!duplicated(out[["factset_entity_id"]]))) + stopifnot(all(!is.na(out[["factset_entity_id"]]))) + stopifnot(all(!is.na(out[["security_mapped_sector"]]))) + stopifnot(is.character(out[["factset_entity_id"]])) + stopifnot(is.character(out[["security_mapped_sector"]])) + out +} diff --git a/R/prepare_financial_data.R b/R/prepare_financial_data.R new file mode 100644 index 0000000..69e6ead --- /dev/null +++ b/R/prepare_financial_data.R @@ -0,0 +1,44 @@ +#' Prepare the `financial_data` output data frame from the imported +#' `factset_financial_data.rds` file +#' +#' @param data A data frame containing the imported `factset_financial_data.rds` +#' file +#' @param issue_code_bridge A data frame containing data that bridges from +#' factset issue codes to one of `c("Listed Equity", "Corporate Bond", +#' "Fund", "Other")` +#' +#' @return A tibble properly prepared to be saved as the `financial_data.rds` +#' output file +#' +#' @export + +prepare_financial_data <- + function(data, issue_code_bridge) { + data %>% + filter(!is.na(.data$issue_type)) %>% + left_join( + issue_code_bridge, + by = c(issue_type = "issue_type_code") + ) %>% + mutate( + shares_all_classes = calc_shares_all_classes( + shares_outstanding = .data$adj_shares_outstanding, + asset_type_id = .data$issue_type, + include_types = c("CP", "EQ", "PF") + ), + .by = "factset_entity_id" + ) %>% + filter( + .data$asset_type %in% c("Bonds", "Others") | + (.data$asset_type == "Funds" & !is.na(.data$adj_price)) | + (.data$asset_type == "Equity" & !is.na(.data$adj_price) & .data$adj_shares_outstanding != 0) | + (.data$asset_type == "Equity" & .data$adj_price != 0 & .data$adj_shares_outstanding != 0) + ) %>% # remove Equity and Fund rows that don't have crucial price data + select( + isin = "isin", + unit_share_price = "adj_price", + current_shares_outstanding_all_classes = "shares_all_classes", + asset_type = "asset_type", + factset_entity_id = "factset_entity_id" + ) + } diff --git a/R/prepare_fund_data.R b/R/prepare_fund_data.R new file mode 100644 index 0000000..e1e0bfe --- /dev/null +++ b/R/prepare_fund_data.R @@ -0,0 +1,51 @@ +#' Prepare fund data, filtering to funds with data according to a given +#' threshold and adding a `MISSINGWEIGHT` holding for the difference +#' +#' @param fund_data A data frame containing fund data +#' @param threshold A numeric value between 0 and 1 (inclusive) indicating the +#' allowable percentage of the total fund value that the summed values of its +#' component holdings should be equal to or greater than +#' +#' @return A tibble +#' +#' @export + +prepare_fund_data <- function(fund_data, threshold = 0) { + stopifnot(is.data.frame(fund_data)) + stopifnot( + all(c("factset_fund_id", "holding_isin", "fund_reported_mv", "holding_reported_mv") %in% names(fund_data)) + ) + + stopifnot(threshold >= 0 && threshold <= 1) + + # filter out funds where component holdings summed value is significantly + # greater than the reported total fund value + fund_data <- + fund_data %>% + dplyr::filter( + (.data$fund_reported_mv[[1]] - sum(.data$holding_reported_mv)) / .data$fund_reported_mv[[1]] > -1e-5, + .by = c("factset_fund_id", "fund_reported_mv") + ) + + # filter out funds where component holdings summed value is less than the + # threshold percentage of the reported total fund value + fund_data <- + fund_data %>% + dplyr::filter( + sum(.data$holding_reported_mv) / .data$fund_reported_mv[[1]] >= .env$threshold, + .by = c("factset_fund_id", "fund_reported_mv") + ) + + # calculate missing weight values for each fund + fund_missing_mv <- + fund_data %>% + dplyr::summarise( + holding_isin = "MISSINGWEIGHT", + holding_reported_mv = .data$fund_reported_mv[[1]] - sum(.data$holding_reported_mv), + .by = c("factset_fund_id", "fund_reported_mv") + ) %>% + dplyr::filter(.data$holding_reported_mv != 0) + + # add missing weight holdings for each fund + dplyr::bind_rows(fund_data, fund_missing_mv) +} diff --git a/R/prepare_isin_to_fund_table.R b/R/prepare_isin_to_fund_table.R new file mode 100644 index 0000000..9bef691 --- /dev/null +++ b/R/prepare_isin_to_fund_table.R @@ -0,0 +1,36 @@ +#' Prepare `isin_to_fund_table`, filtering out fsyms that have more than 1 row +#' and either no fund data or fund data for both rows +#' +#' @param isin_to_fund_table A data frame containing isin_to_fund_table data +#' @param fund_data A data frame containing fund data +#' +#' @return A tibble +#' +#' @export + +prepare_isin_to_fund_table <- function(isin_to_fund_table, fund_data) { + stopifnot(is.data.frame(isin_to_fund_table)) + stopifnot( + all(c("factset_fund_id", "fsym_id") %in% names(isin_to_fund_table)) + ) + + stopifnot(is.data.frame(fund_data)) + stopifnot( + all(c("factset_fund_id") %in% names(fund_data)) + ) + + # filter out fsyms that have more than 1 row and no fund data + isin_to_fund_table <- + isin_to_fund_table %>% + dplyr::mutate(has_fund_data = .data$factset_fund_id %in% fund_data$factset_fund_id) %>% + dplyr::mutate(n = dplyr::n(), .by = "fsym_id") %>% + dplyr::filter(.data$n == 1 | (.data$n > 1 & .data$has_fund_data), .by = "fsym_id") %>% + dplyr::select(-"n", -"has_fund_data") + + # filter out fsyms that have more than 1 row and have fund data for both rows + isin_to_fund_table %>% + dplyr::mutate(has_fund_data = .data$factset_fund_id %in% fund_data$factset_fund_id) %>% + dplyr::mutate(n = dplyr::n(), .by = "fsym_id") %>% + dplyr::filter(!(all(.data$has_fund_data) & .data$n > 1), .by = "fsym_id") %>% + dplyr::select(-"n", -"has_fund_data") +} diff --git a/R/prepare_iss_average_sector_emission_intensities.R b/R/prepare_iss_average_sector_emission_intensities.R new file mode 100644 index 0000000..f260d29 --- /dev/null +++ b/R/prepare_iss_average_sector_emission_intensities.R @@ -0,0 +1,87 @@ +#' Prepare a `iss_average_sector_emission_intensities` object +#' +#' @param iss_company_emissions A data frame containing `iss_company_emissions` +#' data +#' @param factset_financial_data A data frame containing +#' `factset_financial_data` data +#' @param factset_entity_info A data frame containing `factset_entity_info` data +#' @param factset_entity_financing_data A data frame containing +#' `factset_entity_financing_data` data +#' @param currencies A data frame containing currency exchange rate data +#' +#' @return A data frame containing the prepared +#' `iss_average_sector_emission_intensities` object +#' +#' @export + +prepare_iss_average_sector_emission_intensities <- function(iss_company_emissions, + factset_financial_data, + factset_entity_info, + factset_entity_financing_data, + currencies) { + factset_financial_data <- + factset_financial_data %>% + dplyr::select("factset_entity_id", "issue_type", "adj_price", "adj_shares_outstanding") %>% + dplyr::filter(!is.na(.data[["factset_entity_id"]]) & !is.na(.data[["adj_shares_outstanding"]])) %>% + dplyr::filter(.data[["issue_type"]] %in% c("EQ", "PF", "CP")) %>% + dplyr::summarize( + mkt_val = sum(.data[["adj_price"]] * .data[["adj_shares_outstanding"]], na.rm = TRUE), + .by = "factset_entity_id" + ) + + factset_entity_info <- + factset_entity_info %>% + dplyr::select("factset_entity_id", "iso_country", "sector_code", "factset_sector_desc") %>% + dplyr::left_join(factset_financial_data, by = "factset_entity_id") + + iss_entity_emission_intensities <- + factset_entity_financing_data %>% + dplyr::left_join(factset_entity_info, by = "factset_entity_id") %>% + dplyr::filter( + countrycode::countrycode(.data[["iso_country"]], "iso2c", "iso4217c") == .data[["currency"]] + ) %>% + dplyr::left_join(currencies, by = "currency") %>% + dplyr::mutate( + ff_mkt_val = if_else(!is.na(.data[["mkt_val"]]), .data[["mkt_val"]], NA_real_), + ff_debt = if_else(!is.na(.data[["ff_debt"]]), .data[["ff_debt"]] * .data[["exchange_rate"]], NA_real_), + currency = "USD" + ) %>% + dplyr::summarise( + ff_mkt_val = mean(.data[["ff_mkt_val"]], na.rm = TRUE), + ff_debt = mean(.data[["ff_debt"]], na.rm = TRUE), + .by = "factset_entity_id" + ) %>% + dplyr::inner_join(iss_company_emissions, by = "factset_entity_id") %>% + dplyr::transmute( + factset_entity_id = .data[["factset_entity_id"]], + emission_intensity_per_mkt_val = if_else( + .data[["ff_mkt_val"]] == 0, + NA_real_, + .data[["icc_total_emissions"]] / .data[["ff_mkt_val"]] + ), + emission_intensity_per_debt = if_else( + .data[["ff_debt"]] == 0, + NA_real_, + .data[["icc_total_emissions"]] / .data[["ff_debt"]] + ), + ff_mkt_val = .data[["ff_mkt_val"]], + ff_debt = .data[["ff_debt"]], + units = paste0(.data[["icc_total_emissions_units"]], " / ", "$ USD") + ) + + iss_entity_emission_intensities %>% + dplyr::inner_join(factset_entity_info, by = "factset_entity_id") %>% + dplyr::summarise( + emission_intensity_per_mkt_val = stats::weighted.mean( + .data[["emission_intensity_per_mkt_val"]], + .data[["ff_mkt_val"]], + na.rm = TRUE + ), + emission_intensity_per_debt = stats::weighted.mean( + .data[["emission_intensity_per_debt"]], + .data[["ff_debt"]], + na.rm = TRUE + ), + .by = c("sector_code", "factset_sector_desc", "units") + ) +} diff --git a/R/prepare_iss_company_emissions.R b/R/prepare_iss_company_emissions.R new file mode 100644 index 0000000..0bf4f2a --- /dev/null +++ b/R/prepare_iss_company_emissions.R @@ -0,0 +1,22 @@ +#' Prepare an `iss_company_emissions` object from `factset_iss_emissions_data` +#' +#' @param factset_iss_emissions_data A data frame containing ISS emissions data +#' +#' @return A tibble +#' +#' @export + +prepare_iss_company_emissions <- function(factset_iss_emissions_data) { + stopifnot(is.data.frame(factset_iss_emissions_data)) + stopifnot( + all(c("factset_entity_id", "icc_total_emissions", "icc_scope_3_emissions") %in% names(factset_iss_emissions_data)) + ) + + factset_iss_emissions_data %>% + dplyr::summarise( + icc_total_emissions = sum(.data$icc_total_emissions + .data$icc_scope_3_emissions, na.rm = TRUE), + .by = "factset_entity_id" + ) %>% + # units are defined in the ISS/FactSet documentation + dplyr::mutate(icc_total_emissions_units = "tCO2e") +} diff --git a/R/prepare_iss_entity_emission_intensities.R b/R/prepare_iss_entity_emission_intensities.R new file mode 100644 index 0000000..3b72b30 --- /dev/null +++ b/R/prepare_iss_entity_emission_intensities.R @@ -0,0 +1,71 @@ +#' Prepare a `iss_entity_emission_intensities` object +#' +#' @param iss_company_emissions A data frame containing `iss_company_emissions` +#' data +#' @param factset_financial_data A data frame containing +#' `factset_financial_data` data +#' @param factset_entity_info A data frame containing `factset_entity_info` data +#' @param factset_entity_financing_data A data frame containing +#' `factset_entity_financing_data` data +#' @param currencies A data frame containing currency exchange rate data +#' +#' @return A data frame containing the prepared +#' `iss_entity_emission_intensities` object +#' +#' @export + +prepare_iss_entity_emission_intensities <- function(iss_company_emissions, + factset_financial_data, + factset_entity_info, + factset_entity_financing_data, + currencies) { + factset_financial_data <- + factset_financial_data %>% + dplyr::select("factset_entity_id", "issue_type", "adj_price", "adj_shares_outstanding") %>% + dplyr::filter(!is.na(.data[["factset_entity_id"]]) & !is.na(.data[["adj_shares_outstanding"]])) %>% + dplyr::filter(.data[["issue_type"]] %in% c("EQ", "PF", "CP")) %>% + dplyr::summarize( + mkt_val = sum(.data[["adj_price"]] * .data[["adj_shares_outstanding"]], na.rm = TRUE), + .by = "factset_entity_id" + ) + + factset_entity_info <- + factset_entity_info %>% + dplyr::select("factset_entity_id", "iso_country") %>% + dplyr::left_join(factset_financial_data, by = "factset_entity_id") + + factset_entity_financing_data %>% + dplyr::left_join(factset_entity_info, by = "factset_entity_id") %>% + dplyr::filter( + countrycode::countrycode(.data[["iso_country"]], "iso2c", "iso4217c") == .data[["currency"]] + ) %>% + dplyr::left_join(currencies, by = "currency") %>% + dplyr::mutate( + ff_mkt_val = if_else(!is.na(.data[["mkt_val"]]), .data[["mkt_val"]], NA_real_), + ff_debt = if_else(!is.na(.data[["ff_debt"]]), .data[["ff_debt"]] * .data[["exchange_rate"]], NA_real_), + currency = "USD" + ) %>% + dplyr::summarise( + ff_mkt_val = mean(.data[["ff_mkt_val"]], na.rm = TRUE), + ff_debt = mean(.data[["ff_debt"]], na.rm = TRUE), + .by = "factset_entity_id" + ) %>% + dplyr::inner_join(iss_company_emissions, by = "factset_entity_id") %>% + dplyr::transmute( + factset_entity_id = .data[["factset_entity_id"]], + emission_intensity_per_mkt_val = if_else( + .data[["ff_mkt_val"]] == 0, + NA_real_, + .data[["icc_total_emissions"]] / .data[["ff_mkt_val"]] + ), + emission_intensity_per_debt = if_else( + .data[["ff_debt"]] == 0, + NA_real_, + .data[["icc_total_emissions"]] / .data[["ff_debt"]] + ), + ff_mkt_val = .data[["ff_mkt_val"]], + ff_debt = .data[["ff_debt"]], + units = paste0(.data[["icc_total_emissions_units"]], " / ", "$ USD") + ) %>% + dplyr::select(-c("ff_mkt_val", "ff_debt")) +} diff --git a/R/prepare_masterdata.R b/R/prepare_masterdata.R new file mode 100644 index 0000000..ff39f18 --- /dev/null +++ b/R/prepare_masterdata.R @@ -0,0 +1,83 @@ +#' Prepare the `masterdata_ownership_datastore` or `masterdata_debt_datastore` +#' output data frame from an import of a raw AR masterdata_* CSV +#' +#' @param data A dataframe containing the raw input of an AR masterdata_* CSV +#' files +#' @param ar_company_id__country_of_domicile A data frame with two columns +#' mapping `ar_company_id` to `country_of_domicile` +#' @param pacta_financial_timestamp A single element character vector specifying +#' the timestamp in the PACTA format, e.g. "2021Q4" +#' @param zero_emission_factor_techs A character vector listing technologies +#' that will have emission factors manually forced to 0 +#' +#' @return A tibble properly prepared to be saved as the +#' `masterdata_ownership_datastore.rds` or `masterdata_debt_datastore.rds` +#' output file +#' +#' @export + +prepare_masterdata <- + function(data, + ar_company_id__country_of_domicile, + pacta_financial_timestamp, + zero_emission_factor_techs) { + data %>% + filter_most_recent_timestamp(pacta_financial_timestamp, .data$sector) %>% + pivot_longer( + cols = dplyr::matches("^_20[0-9]{2}$"), + names_to = "year", + names_prefix = "_", + names_transform = list(year = as.integer), + values_to = "ald_production", + values_ptypes = numeric(), + values_drop_na = TRUE + ) %>% + mutate(sector = if_else(.data$sector == "LDV", "Automotive", .data$sector)) %>% + mutate( + technology = dplyr::case_when( + .data$sector == "Coal" ~ "Coal", + .data$sector == "HDV" ~ paste0(.data$technology, "_HDV"), + .data$technology %in% c("ICE Gasoline", "ICE Diesel", "ICE E85+", "ICE Propane", "ICE CNG", "Hybrid No-Plug") ~ "ICE", + .data$technology == "Electric" ~ "Electric", + .data$technology == "Hybrid Plug-In" ~ "Hybrid", + .data$technology == "Fuel Cell" ~ "FuelCell", + .data$technology == "Oil and Condensate" ~ "Oil", + .data$sector == "Power" ~ sub("([^(Cap)]$)", "\\1Cap", .data$technology), + TRUE ~ .data$technology + ) + ) %>% + rename(dplyr::any_of(c( + "emission_factor" = "emissions_factor", + "emission_factor_unit" = "emissions_factor_unit" + ))) %>% + mutate( + emission_factor = if_else( + .data$technology %in% .env$zero_emission_factor_techs & .data$ald_production > 0, + 0, + .data$emission_factor + ) + ) %>% + group_by( + .data$company_id, .data$sector, .data$asset_country, .data$technology, + .data$year, .data$unit, .data$emission_factor_unit + ) %>% + summarise( + emission_factor = stats::weighted.mean(.data$emission_factor, .data$ald_production, na.rm = TRUE), + ald_production = sum(.data$ald_production, na.rm = TRUE), + .groups = "drop" + ) %>% + transmute( + id = as.character(.data$company_id), + id_name = "ar_company_id", + ald_sector = as.character(.data$sector), + ald_location = as.character(.data$asset_country), + technology = as.character(.data$technology), + year = as.numeric(.data$year), + ald_production = as.numeric(.data$ald_production), + ald_production = if_else(.data$ald_production <= 0, 0, .data$ald_production), + ald_production_unit = as.character(.data$unit), + ald_emissions_factor = as.numeric(.data$emission_factor), + ald_emissions_factor_unit = as.character(.data$emission_factor_unit) + ) %>% + left_join(ar_company_id__country_of_domicile, by = c(id = "ar_company_id")) + } diff --git a/R/prepare_masterdata_debt.R b/R/prepare_masterdata_debt.R new file mode 100644 index 0000000..f4d7f60 --- /dev/null +++ b/R/prepare_masterdata_debt.R @@ -0,0 +1,61 @@ +#' Prepare the `masterdata_debt_datastore` object from a raw masterdata_debt CSV +#' +#' @param masterdata_debt_raw A data frame containing the raw data from a +#' masterdata_debt CSV +#' @param ar_company_id__country_of_domicile A data frame containing an +#' `ar_company_id` to `country_of_domicile` lookup +#' @param ar_company_id__credit_parent_ar_company_id A data frame containing an +#' `ar_company_id` to `credit_parent_ar_company_id` lookup +#' @param pacta_financial_timestamp A single character vector containing the +#' PACTA financial timestamp, e.g. `2023Q4` +#' @param zero_emission_factor_techs A character vector containing the zero +#' emission factor technologies +#' +#' @return A data frame containing the prepared masterdata_debt_datastore +#' +#' @export + +prepare_masterdata_debt <- function(masterdata_debt_raw, + ar_company_id__country_of_domicile, + ar_company_id__credit_parent_ar_company_id, + pacta_financial_timestamp, + zero_emission_factor_techs) { + + company_id__creditor_company_id <- + prepare_company_id__creditor_company_id(masterdata_debt_raw) + + masterdata_debt_raw %>% + prepare_masterdata( + ar_company_id__country_of_domicile, + pacta_financial_timestamp, + zero_emission_factor_techs + ) %>% + dplyr::left_join(company_id__creditor_company_id, by = c(id = "company_id")) %>% + dplyr::left_join(ar_company_id__credit_parent_ar_company_id, by = c(id = "ar_company_id")) %>% + dplyr::mutate(id = dplyr::if_else( + !is.na(.data$credit_parent_ar_company_id), + .data$credit_parent_ar_company_id, + .data$id + )) %>% + dplyr::mutate(id = dplyr::if_else( + !is.na(.data$creditor_company_id), + .data$creditor_company_id, + .data$id + )) %>% + dplyr::mutate(id_name = "credit_parent_ar_company_id") %>% + dplyr::summarise( + ald_emissions_factor = stats::weighted.mean(.data$ald_emissions_factor, .data$ald_production, na.rm = TRUE), + ald_production = sum(.data$ald_production, na.rm = TRUE), + .by = c( + "id", + "id_name", + "ald_sector", + "ald_location", + "technology", + "year", + "country_of_domicile", + "ald_production_unit", + "ald_emissions_factor_unit" + ) + ) +} diff --git a/R/prepare_total_fund_list.R b/R/prepare_total_fund_list.R new file mode 100644 index 0000000..c640963 --- /dev/null +++ b/R/prepare_total_fund_list.R @@ -0,0 +1,17 @@ +#' Prepare a `total_fund_list` object from `fund_data` +#' +#' @param fund_data A data frame containing fund data +#' +#' @return A tibble +#' +#' @export + +prepare_total_fund_list <- function(fund_data) { + stopifnot(is.data.frame(fund_data)) + stopifnot("factset_fund_id" %in% names(fund_data)) + + fund_data %>% + dplyr::select("factset_fund_id") %>% + dplyr::filter(!is.na(.data$factset_fund_id)) %>% + dplyr::distinct() +} diff --git a/R/quarter_to_factset_timestamp.R b/R/quarter_to_factset_timestamp.R new file mode 100644 index 0000000..38338d5 --- /dev/null +++ b/R/quarter_to_factset_timestamp.R @@ -0,0 +1,16 @@ +#' Convert a PACTA style quarter string to a FactSet style date string for the +#' last day of that quarter +#' +#' @param quarter A character vector containing PACTA style quarter strings in +#' the form e.g. "2021Q4" +#' +#' @return A character vector containing the equivalent FactSet style date +#' strings for the last day of the quarter e.g. "2021-12-31" +#' +#' @export + +quarter_to_factset_timestamp <- + function(quarter) { + stopifnot(grepl(pattern = "20[0-9]{2}Q[1-4]", x = quarter)) + as.character(quarter(lubridate::yq(quarter), type = "date_last")) + } diff --git a/R/quarter_to_imf_timestamp.R b/R/quarter_to_imf_timestamp.R new file mode 100644 index 0000000..4edea62 --- /dev/null +++ b/R/quarter_to_imf_timestamp.R @@ -0,0 +1,15 @@ +#' Convert a PACTA style quarter string to an IMF style quarter string +#' +#' @param quarter A character vector containing PACTA style quarter strings in +#' the form e.g. "2021Q4"X +#' +#' @return A character vector containing the equivalent IMF style quarter +#' strings e.g. "2021-Q4" +#' +#' @export + +quarter_to_imf_timestamp <- + function(quarter) { + stopifnot(all(grepl(pattern = "20[0-9]{2}Q[1-4]", x = quarter))) + sub("(20[0-9]{2})(Q[1-4])", "\\1-\\2", quarter) + } diff --git a/R/standardize_asset_type_names.R b/R/standardize_asset_type_names.R new file mode 100644 index 0000000..a4a393f --- /dev/null +++ b/R/standardize_asset_type_names.R @@ -0,0 +1,23 @@ +#' Standardize asset type names +#' +#' @param factset_issue_code_bridge A data frame containing the FactSet issue +#' code bridge +#' +#' @return A data frame containing a FactSet `issue_type_code` to `asset_type` +#' lookup +#' +#' @export + +standardize_asset_type_names <- function(factset_issue_code_bridge) { + factset_issue_code_bridge %>% + dplyr::select("issue_type_code", "asset_type") %>% + dplyr::mutate( + asset_type = dplyr::case_when( + .data$asset_type == "Listed Equity" ~ "Equity", + .data$asset_type == "Corporate Bond" ~ "Bonds", + .data$asset_type == "Fund" ~ "Funds", + .data$asset_type == "Other" ~ "Others", + TRUE ~ "Others" + ) + ) +} diff --git a/R/write_manifest.R b/R/write_manifest.R new file mode 100644 index 0000000..64a31ac --- /dev/null +++ b/R/write_manifest.R @@ -0,0 +1,142 @@ +#' Write a manifest.json file to the specified path including critical +#' information about the files and parameters used to prepare the data +#' +#' @param path A single string specifying a filepath to save the JSON file to +#' @param parameters A list containing all parameters used to create the data +#' @param input_files A vector with filepaths of input files used to create the +#' output data. +#' @param output_files A vector with filepaths of output files created. +#' +#' @return Called for the side-effect of writing a JSON file to disk +#' +#' @export + +write_manifest <- function( + path, + parameters, + input_files, + output_files +) { + list( + parameters = parameters, + inputs_manifest = get_info_for_files(input_files), + outputs_manifest = get_info_for_files(output_files), + git = list( + local_git_tag = get_local_git_tag(), + local_git_hash = get_local_git_hash(), + local_git_status = get_local_git_status() + ), + sessionInfo = get_sessionInfo(), + creation_time_date = format(Sys.time(), "%F %R", tz = "UTC", usetz = TRUE) + ) %>% + jsonlite::write_json( + path = path, + pretty = TRUE, + auto_unbox = TRUE + ) + } + + +get_info_for_files <- + function(file_paths) { + get_file_info <- + function(file_path) { + list( + list( + "file name" = basename(file_path), + "file extension" = tools::file_ext(file_path), + "file path" = file_path, + "file size" = format(structure(file.info(file_path)$size, class = "object_size"), units = "auto", standard = "SI"), + "file last modified" = format(as.POSIXlt(file.info(file_path)$mtime, tz = "UTC"), "%Y-%m-%dT%H:%M:%S+00:00"), + "filehash" = cli::hash_file_md5(paths = file_path) + ) + ) + } + + output <- vapply(X = file_paths, FUN = get_file_info, FUN.VALUE = list(1), USE.NAMES = TRUE) + setNames(output, basename(file_paths)) + } + + +get_sessionInfo <- + function() { + session_info <- devtools::session_info(pkgs = "attached", info = "all") + list( + platform = unclass(session_info$platform), + packages = as.data.frame(unclass(session_info$packages)), + external = unclass(session_info$external) + ) + } + + +is_git_repo <- function(dir = ".") { + system2("git", args = c(paste("-C", dir), "status"), stderr = FALSE, stdout = FALSE) == 0 +} + + +get_local_git_hash <- function(repo = ".") { + if (is_git_repo(repo)) { + response <- + suppressWarnings( + system2( + "git", + args = c(paste("-C", repo), "log", "-1", "--pretty=format:'%H'"), + stdout = TRUE, + stderr = FALSE + ) + ) + if (length(response) > 0) { + return(response) + } + return("no git hashes found") + } + "not a git repo" +} + + +get_local_git_tag <- function(repo = ".") { + if (is_git_repo(repo)) { + response <- + suppressWarnings( + system2( + "git", + args = c(paste("-C", repo), "describe", "--tags", "--abbrev=0"), + stdout = TRUE, + stderr = FALSE + ) + ) + if (length(response) > 0) { + return(response) + } + return("no git tags found") + } + "not a git repo" +} + + +get_local_git_status <- function(repo = ".") { + if (is_git_repo(repo)) { + response <- + suppressWarnings( + system2( + "git", + args = c(paste("-C", repo), "status", "--short", "--", "config.yml", "run_pacta_data_preparation.R", "DESCRIPTION"), + stdout = TRUE, + stderr = FALSE + ) + ) + if (length(response) > 0) { + return( + dplyr::tibble(response) %>% + tidyr::separate( + col = 1, + into = c("status", "filename"), + sep = " ", + extra = "merge" + ) + ) + } + return("git status is clean") + } + "not a git repo" +} diff --git a/README.md b/README.md new file mode 100644 index 0000000..8a7d32e --- /dev/null +++ b/README.md @@ -0,0 +1,29 @@ +# pacta.data.preparation + + + +[![Lifecycle: +experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) +[![R-CMD-check](https://github.com/RMI-PACTA/pacta.data.preparation/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/RMI-PACTA/pacta.data.preparation/actions/workflows/R-CMD-check.yaml) +[![CRAN +status](https://www.r-pkg.org/badges/version/pacta.data.preparation)](https://CRAN.R-project.org/package=pacta.data.preparation) +[![pacta.data.preparation status +badge](https://rmi-pacta.r-universe.dev/badges/pacta.data.preparation)](https://rmi-pacta.r-universe.dev/ui#package:pacta.data.preparation) + + +The `pacta.data.preparation` R package provides utilities and functions used to process various data files into data required to run the PACTA for investors tools, such as [RMI-PACTA/workflow.transition.monitor](https://github.com/RMI-PACTA/workflow.transition.monitor) or [RMI-PACTA/workflow.pacta](https://github.com/RMI-PACTA/workflow.pacta). + +## Installation + +You can install the development version of pacta.data.preparation from +[GitHub](https://github.com/) with: + +``` r +# install.packages("pak") +pak::pak("RMI-PACTA/pacta.data.preparation") +``` + +## Running data preparation + +The primary way this package is intended to be used is through the +[workflow.data.preparation](https://github.com/RMI-PACTA/workflow.data.preparation) repo. See there for an example workflow of how to use the functions of this package to complete a data preparation process properly. diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..0619253 --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,3 @@ +url: https://rmi-pacta.github.io/pacta.data.preparation +template: + package: pacta.pkgdown.rmitemplate diff --git a/man/calc_shares_all_classes.Rd b/man/calc_shares_all_classes.Rd new file mode 100644 index 0000000..43b4c13 --- /dev/null +++ b/man/calc_shares_all_classes.Rd @@ -0,0 +1,40 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/calc_shares_all_classes.R +\name{calc_shares_all_classes} +\alias{calc_shares_all_classes} +\title{Calculate the number of shares across all equity asset classes} +\usage{ +calc_shares_all_classes( + shares_outstanding, + asset_type_id, + include_types = c("CP", "EQ", "PF") +) +} +\arguments{ +\item{shares_outstanding}{(numeric vector) contains the number of shares +outstanding} + +\item{asset_type_id}{(character vector) contains an the asset type identifier} + +\item{include_types}{(character vector) defines which of the \code{asset_type_id} +values should be included in the sum} +} +\value{ +a numeric value containing the number of shares across all equity +asset classes +} +\description{ +\code{calc_shares_all_classes()} calculates the number of shares +across all equity asset classes. The asset class is defined by the value in +\code{asset_type_id}, and only asset classes included in \code{include_types} are +summed. + +\code{calc_shares_all_classes()} assumes that all input values are from the same +entity/company, so a single summed value is returned. If it is desired to +calculate the shares in all classes for multiple entities at the same time, +a typical usage would be to apply this function "by" some entity id, e.g. + +\code{mutate(data, shares_all_classes = calc_shares_all_classes(shares_outstanding, asset_type_id), .by = "entity_id")} + +For more details see \code{vignette("share_ownership_methodology")}. +} diff --git a/man/dataprep_abcd_scen_connection.Rd b/man/dataprep_abcd_scen_connection.Rd new file mode 100644 index 0000000..7804eaa --- /dev/null +++ b/man/dataprep_abcd_scen_connection.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/dataprep_abcd_scen_connection.R +\name{dataprep_abcd_scen_connection} +\alias{dataprep_abcd_scen_connection} +\title{Combine ABCD and scenario data into the \verb{[equity/bonds]_abcd_scenario.rds} +format that is used by portfolio.analysis} +\usage{ +dataprep_abcd_scen_connection( + abcd_data, + scenario_data, + reference_year, + relevant_years, + tech_exclude, + scenario_geographies_list, + sector_list, + other_sector_list, + global_aggregate_scenario_sources_list, + global_aggregate_sector_list, + scenario_regions, + index_regions +) +} +\arguments{ +\item{abcd_data}{A tibble containing the ABCD data} + +\item{scenario_data}{A tibble containing the scenario data} + +\item{reference_year}{A single numeric specifying the market share target +reference year} + +\item{relevant_years}{A numeric vector containing all relevant years to be +calculated} + +\item{tech_exclude}{A character vector containing the technologies to be +excluded} + +\item{scenario_geographies_list}{A character vector containing the scenario +geographies to be used} + +\item{sector_list}{A character vector containing the sectors to be included} + +\item{other_sector_list}{A character vector containing the sectors considered +"other"} + +\item{global_aggregate_scenario_sources_list}{A character vector containing +the scenario sources to be included in the global aggreagte} + +\item{global_aggregate_sector_list}{A character vector containing the sectors +to be included in the global aggregate} + +\item{scenario_regions}{A character vector containing the scenario regions} + +\item{index_regions}{A character vector containing the index regions} +} +\value{ +A tibble with the combined ABCD and scenario data +} +\description{ +Combine ABCD and scenario data into the \verb{[equity/bonds]_abcd_scenario.rds} +format that is used by portfolio.analysis +} diff --git a/man/determine_relevant_years.Rd b/man/determine_relevant_years.Rd new file mode 100644 index 0000000..3244b2f --- /dev/null +++ b/man/determine_relevant_years.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/determine_relevant_years.R +\name{determine_relevant_years} +\alias{determine_relevant_years} +\title{Determine relevant years} +\usage{ +determine_relevant_years(market_share_target_reference_year, time_horizon) 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zv1}Rnfv_HT&gpZMjgFzJ6bTZ;9k-$kxSY5i^bThA`>!)UcSVdLrZ89*DSf>|Ji(on z!IUBY3Cd`hyDn(QmSYNa!x+a>Qrt7qe~c`@O}$}ZCLEL?;L=37xVIQ4NUe~JkGoWB z_ss4%k-6V8)7xkf9}%2`eNm#UF8e&j;U;8rvM+@7?&4L%I@CY?5jy1m8p)x$K7XsG zYIv|vlyz$BhyCew)I1;BgN?NfZ6~+z{w`V&{7yX`D??3`wl>{Sc+E_=m7a%g8@!^2 zf0gNZ|Gd6Hf0&M8^XFUO_d4wQ^&0tGoBz;@!5{GM=bwo;osjo*nrb?#x#!LO{tqSY Bi{JnN literal 0 HcmV?d00001 diff --git a/man/import_ar_advanced_company_indicators.Rd b/man/import_ar_advanced_company_indicators.Rd new file mode 100644 index 0000000..574dc02 --- /dev/null +++ b/man/import_ar_advanced_company_indicators.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/import_ar_advanced_company_indicators.R +\name{import_ar_advanced_company_indicators} +\alias{import_ar_advanced_company_indicators} +\title{Import the data from a version of Asset Resolution's proprietary Advanced +Company Indicators XLSX into a tidy data frame.} +\usage{ +import_ar_advanced_company_indicators( + filepath, + drop_nas = TRUE, + fix_names = FALSE, + as_factor = TRUE +) +} +\arguments{ +\item{filepath}{Path to the XLSX file.} + +\item{drop_nas}{A logical indicating whether rows with an \code{NA} value after +pivoting to long-format should be dropped (default is \code{TRUE}).} + +\item{fix_names}{A logical indicating whether the column names should be +fixed to snakecase format. (e.g. \verb{Company Name} becomes \code{company_name}). By +default, column names are not changed (i.e. \code{FALSE}).} + +\item{as_factor}{A logical indicating whether the character columns should be +converted to factors(default is \code{TRUE}).} +} +\value{ +A tibble including all the data from the "Company Information", +"Company ISINs", "Company Emissions", and "Company Activities" tabs +combined into one tidy tibble. +} +\description{ +Import the data from a version of Asset Resolution's proprietary Advanced +Company Indicators XLSX into a tidy data frame. +} diff --git a/man/import_ar_masterdata.Rd b/man/import_ar_masterdata.Rd new file mode 100644 index 0000000..c73c728 --- /dev/null +++ b/man/import_ar_masterdata.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/import_ar_advanced_company_indicators.R +\name{import_ar_masterdata} +\alias{import_ar_masterdata} +\title{Import the data from a version of Asset Resolution's bespoke +\code{masterdata_*.csv} files into a tidy data frame.} +\usage{ +import_ar_masterdata(filepath, drop_nas = TRUE, id_as_string = FALSE) +} +\arguments{ +\item{filepath}{Path to the CSV file.} + +\item{drop_nas}{A logical indicating whether rows with an \code{NA} value after +pivoting to long-format should be dropped (deafult is \code{TRUE}).} + +\item{id_as_string}{A logical indicating whether the \code{company_id} column +should be imported as a character vector. By default, the \code{company_id} +column is imported as a numeric (i.e. \code{FALSE}).} +} +\value{ +A tidy, long-format tibble of all the data in the \code{masterdata_*.csv} +file with an added \code{consolidation_method} column to record which of +"ownership" or "debt" file was imported. +} +\description{ +Import the data from a version of Asset Resolution's bespoke +\code{masterdata_*.csv} files into a tidy data frame. +} diff --git a/man/pacta.data.preparation-package.Rd b/man/pacta.data.preparation-package.Rd new file mode 100644 index 0000000..2233989 --- /dev/null +++ b/man/pacta.data.preparation-package.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/pacta.data.preparation-package.R +\docType{package} +\name{pacta.data.preparation-package} +\alias{pacta.data.preparation} +\alias{pacta.data.preparation-package} +\title{pacta.data.preparation: Prepare Data for PACTA for Investors} +\description{ +\if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}} + +This package provides tools to prepare input datasets to be run in the PACTA_analysis tool. +} +\seealso{ +Useful links: +\itemize{ + \item \url{https://github.com/RMI-PACTA/pacta.data.preparation} + \item Report bugs at \url{https://github.com/RMI-PACTA/pacta.data.preparation/issues} +} + +} +\author{ +\strong{Maintainer}: CJ Yetman \email{cj@cjyetman.com} (\href{https://orcid.org/0000-0001-5099-9500}{ORCID}) [contractor] + +Authors: +\itemize{ + \item Jackson Hoffart \email{jackson.hoffart@gmail.com} (\href{https://orcid.org/0000-0002-8600-5042}{ORCID}) [contractor] + \item Jacob Kastl \email{jacob.kastl@gmail.com} [contractor] + \item Alex Axthelm \email{aaxthelm@rmi.org} (\href{https://orcid.org/0000-0001-8579-8565}{ORCID}) [contractor] +} + +Other contributors: +\itemize{ + \item RMI \email{PACTA4investors@rmi.org} [copyright holder, funder] +} + +} +\keyword{internal} diff --git a/man/prepare_abcd_flags_bonds.Rd b/man/prepare_abcd_flags_bonds.Rd new file mode 100644 index 0000000..670e3ac --- /dev/null +++ b/man/prepare_abcd_flags_bonds.Rd @@ -0,0 +1,35 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_abcd_flags_bonds.R +\name{prepare_abcd_flags_bonds} +\alias{prepare_abcd_flags_bonds} +\title{Title} +\usage{ +prepare_abcd_flags_bonds( + financial_data, + factset_entity_id__ar_company_id, + factset_entity_id__security_mapped_sector, + ar_company_id__sectors_with_assets__debt, + factset_entity_id__credit_parent_id +) +} +\arguments{ +\item{financial_data}{A data frame containing financial data} + +\item{factset_entity_id__ar_company_id}{A data frame containing a +factset_entity_id to ar_company_id look up table} + +\item{factset_entity_id__security_mapped_sector}{A data frame containing a +factset_entity_id to security_mapped_sector look up table} + +\item{ar_company_id__sectors_with_assets__debt}{A data frame containing +a ar_company_id to sectors_with_assets look up table for debt} + +\item{factset_entity_id__credit_parent_id}{A data frame containing a +factset_entity_id to credit_parent_id look up table} +} +\value{ +A data frame +} +\description{ +Title +} diff --git a/man/prepare_abcd_flags_equity.Rd b/man/prepare_abcd_flags_equity.Rd new file mode 100644 index 0000000..ab49686 --- /dev/null +++ b/man/prepare_abcd_flags_equity.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_abcd_flags_equity.R +\name{prepare_abcd_flags_equity} +\alias{prepare_abcd_flags_equity} +\title{Title} +\usage{ +prepare_abcd_flags_equity( + financial_data, + factset_entity_id__ar_company_id, + factset_entity_id__security_mapped_sector, + ar_company_id__sectors_with_assets__ownership +) +} +\arguments{ +\item{financial_data}{A data frame containing financial data} + +\item{factset_entity_id__ar_company_id}{A data frame containing a +factset_entity_id to ar_company_id look up table} + +\item{factset_entity_id__security_mapped_sector}{A data frame containing a +factset_entity_id to security_mapped_sector look up table} + +\item{ar_company_id__sectors_with_assets__ownership}{A data frame containing +a ar_company_id to sectors_with_assets look up table for ownership} +} +\value{ +A data frame +} +\description{ +Title +} diff --git a/man/prepare_ar_company_id__country_of_domicile.Rd b/man/prepare_ar_company_id__country_of_domicile.Rd new file mode 100644 index 0000000..e0dee5b --- /dev/null +++ b/man/prepare_ar_company_id__country_of_domicile.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_ar_company_id__country_of_domicile.R +\name{prepare_ar_company_id__country_of_domicile} +\alias{prepare_ar_company_id__country_of_domicile} +\title{Prepare an \code{ar_company_id__country_of_domicile} lookup table from the +\code{entity_info} data} +\usage{ +prepare_ar_company_id__country_of_domicile(entity_info) +} +\arguments{ +\item{entity_info}{A data frame containing the entity info} +} +\value{ +A tibble +} +\description{ +Prepare an \code{ar_company_id__country_of_domicile} lookup table from the +\code{entity_info} data +} diff --git a/man/prepare_ar_company_id__credit_parent_ar_company_id.Rd b/man/prepare_ar_company_id__credit_parent_ar_company_id.Rd new file mode 100644 index 0000000..b2c6620 --- /dev/null +++ b/man/prepare_ar_company_id__credit_parent_ar_company_id.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in +% R/prepare_ar_company_id__credit_parent_ar_company_id.R +\name{prepare_ar_company_id__credit_parent_ar_company_id} +\alias{prepare_ar_company_id__credit_parent_ar_company_id} +\title{Prepare an \code{ar_company_id__credit_parent_ar_company_id} lookup table from the +\code{entity_info} data} +\usage{ +prepare_ar_company_id__credit_parent_ar_company_id(entity_info) +} +\arguments{ +\item{entity_info}{A data frame containing the entity info} +} +\value{ +A tibble +} +\description{ +Prepare an \code{ar_company_id__credit_parent_ar_company_id} lookup table from the +\code{entity_info} data +} diff --git a/man/prepare_ar_company_id__sectors_with_assets__debt.Rd b/man/prepare_ar_company_id__sectors_with_assets__debt.Rd new file mode 100644 index 0000000..3aafded --- /dev/null +++ b/man/prepare_ar_company_id__sectors_with_assets__debt.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in +% R/prepare_ar_company_id__sectors_with_assets__debt.R +\name{prepare_ar_company_id__sectors_with_assets__debt} +\alias{prepare_ar_company_id__sectors_with_assets__debt} +\title{Prepare an \code{ar_company_id__sectors_with_assets__debt} lookup table from the +\code{masterdata_debt_datastore} data} +\usage{ +prepare_ar_company_id__sectors_with_assets__debt( + masterdata_debt_datastore, + relevant_years +) +} +\arguments{ +\item{masterdata_debt_datastore}{A data frame containing processed +production data from Asset Impact's masterdata_ownership CSV} + +\item{relevant_years}{A numeric vector containing the relevant years of data +to include} +} +\value{ +A tibble +} +\description{ +Prepare an \code{ar_company_id__sectors_with_assets__debt} lookup table from the +\code{masterdata_debt_datastore} data +} diff --git a/man/prepare_ar_company_id__sectors_with_assets__ownership.Rd b/man/prepare_ar_company_id__sectors_with_assets__ownership.Rd new file mode 100644 index 0000000..bc23160 --- /dev/null +++ b/man/prepare_ar_company_id__sectors_with_assets__ownership.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in +% R/prepare_ar_company_id__sectors_with_assets__ownership.R +\name{prepare_ar_company_id__sectors_with_assets__ownership} +\alias{prepare_ar_company_id__sectors_with_assets__ownership} +\title{Prepare an \code{ar_company_id__sectors_with_assets__ownership} lookup table from +the \code{masterdata_ownership_datastore} data} +\usage{ +prepare_ar_company_id__sectors_with_assets__ownership( + masterdata_ownership_datastore, + relevant_years +) +} +\arguments{ +\item{masterdata_ownership_datastore}{A data frame containing processed +production data from Asset Impact's masterdata_ownership CSV} + +\item{relevant_years}{A numeric vector containing the relevant years of data +to include} +} +\value{ +A tibble +} +\description{ +Prepare an \code{ar_company_id__sectors_with_assets__ownership} lookup table from +the \code{masterdata_ownership_datastore} data +} diff --git a/man/prepare_company_id__creditor_company_id.Rd b/man/prepare_company_id__creditor_company_id.Rd new file mode 100644 index 0000000..feb167d --- /dev/null +++ b/man/prepare_company_id__creditor_company_id.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_company_id__creditor_company_id.R +\name{prepare_company_id__creditor_company_id} +\alias{prepare_company_id__creditor_company_id} +\title{Prepare a \code{company_id__creditor_company_id} lookup table from Asset Impact's +\code{masterdata_debt} data} +\usage{ +prepare_company_id__creditor_company_id(masterdata_debt) +} +\arguments{ +\item{masterdata_debt}{A data frame containing raw production data from Asset +Impact's masterdata_debt CSV} +} +\value{ +A tibble +} +\description{ +Prepare a \code{company_id__creditor_company_id} lookup table from Asset Impact's +\code{masterdata_debt} data +} diff --git a/man/prepare_entity_info.Rd b/man/prepare_entity_info.Rd new file mode 100644 index 0000000..1f8fc4f --- /dev/null +++ b/man/prepare_entity_info.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_entity_info.R +\name{prepare_entity_info} +\alias{prepare_entity_info} +\title{Prepare the \code{entity_info} output data frame from data frames imported from +the \code{factset_entity_info.rds} and \code{ar_company_id__factset_entity_id.rds} +files} +\usage{ +prepare_entity_info( + data, + factset_entity_id__ar_company_id, + factset_industry_map_bridge, + factset_manual_pacta_sector_override +) +} +\arguments{ +\item{data}{A data frame containing the imported \code{factset_entity_info.rds} +file} + +\item{factset_entity_id__ar_company_id}{A data frame containing the imported +\code{ar_company_id__factset_entity_id.rds} file with columns +\code{factset_entity_id} and \code{ar_company_id}} + +\item{factset_industry_map_bridge}{A data frame containing the imported +\code{factset_industry_map_bridge.rds}} + +\item{factset_manual_pacta_sector_override}{A data frame containing the imported +\code{factset_manual_pacta_sector_override}} +} +\value{ +A tibble properly prepared to be saved as the \code{entity_info.rds} +output file +} +\description{ +Prepare the \code{entity_info} output data frame from data frames imported from +the \code{factset_entity_info.rds} and \code{ar_company_id__factset_entity_id.rds} +files +} diff --git a/man/prepare_factset_entity_id__ar_company_id.Rd b/man/prepare_factset_entity_id__ar_company_id.Rd new file mode 100644 index 0000000..8ce161f --- /dev/null +++ b/man/prepare_factset_entity_id__ar_company_id.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_factset_entity_id__ar_company_id.R +\name{prepare_factset_entity_id__ar_company_id} +\alias{prepare_factset_entity_id__ar_company_id} +\title{Prepare a \code{factset_entity_id__ar_company_id} lookup table from Asset Impact's +\code{ar_company_id__factset_entity_id} crosswalk} +\usage{ +prepare_factset_entity_id__ar_company_id(ar_company_id__factset_entity_id) +} +\arguments{ +\item{ar_company_id__factset_entity_id}{A data frame containing a production +data company ID to financial data entity lookup table} +} +\value{ +A tibble +} +\description{ +Prepare a \code{factset_entity_id__ar_company_id} lookup table from Asset Impact's +\code{ar_company_id__factset_entity_id} crosswalk +} diff --git a/man/prepare_factset_entity_id__credit_parent_id.Rd b/man/prepare_factset_entity_id__credit_parent_id.Rd new file mode 100644 index 0000000..0deab20 --- /dev/null +++ b/man/prepare_factset_entity_id__credit_parent_id.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_factset_entity_id__credit_parent_id.R +\name{prepare_factset_entity_id__credit_parent_id} +\alias{prepare_factset_entity_id__credit_parent_id} +\title{Prepare a \code{factset_entity_id__credit_parent_id} lookup table from +\code{entity_info}} +\usage{ +prepare_factset_entity_id__credit_parent_id(entity_info) +} +\arguments{ +\item{entity_info}{A data frame containing the entity info} +} +\value{ +A tibble +} +\description{ +Prepare a \code{factset_entity_id__credit_parent_id} lookup table from +\code{entity_info} +} diff --git a/man/prepare_factset_entity_id__security_mapped_sector.Rd b/man/prepare_factset_entity_id__security_mapped_sector.Rd new file mode 100644 index 0000000..49e9386 --- /dev/null +++ b/man/prepare_factset_entity_id__security_mapped_sector.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in +% R/prepare_factset_entity_id__security_mapped_sector.R +\name{prepare_factset_entity_id__security_mapped_sector} +\alias{prepare_factset_entity_id__security_mapped_sector} +\title{Prepare a \code{factset_entity_id__security_mapped_sector} lookup table from +\code{entity_info}} +\usage{ +prepare_factset_entity_id__security_mapped_sector(entity_info) +} +\arguments{ +\item{entity_info}{A data frame containing the entity info} +} +\value{ +A tibble +} +\description{ +Prepare a \code{factset_entity_id__security_mapped_sector} lookup table from +\code{entity_info} +} diff --git a/man/prepare_financial_data.Rd b/man/prepare_financial_data.Rd new file mode 100644 index 0000000..83242fc --- /dev/null +++ b/man/prepare_financial_data.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_financial_data.R +\name{prepare_financial_data} +\alias{prepare_financial_data} +\title{Prepare the \code{financial_data} output data frame from the imported +\code{factset_financial_data.rds} file} +\usage{ +prepare_financial_data(data, issue_code_bridge) +} +\arguments{ +\item{data}{A data frame containing the imported \code{factset_financial_data.rds} +file} + +\item{issue_code_bridge}{A data frame containing data that bridges from +factset issue codes to one of \code{c("Listed Equity", "Corporate Bond", "Fund", "Other")}} +} +\value{ +A tibble properly prepared to be saved as the \code{financial_data.rds} +output file +} +\description{ +Prepare the \code{financial_data} output data frame from the imported +\code{factset_financial_data.rds} file +} diff --git a/man/prepare_fund_data.Rd b/man/prepare_fund_data.Rd new file mode 100644 index 0000000..4e737b6 --- /dev/null +++ b/man/prepare_fund_data.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_fund_data.R +\name{prepare_fund_data} +\alias{prepare_fund_data} +\title{Prepare fund data, filtering to funds with data according to a given +threshold and adding a \code{MISSINGWEIGHT} holding for the difference} +\usage{ +prepare_fund_data(fund_data, threshold = 0) +} +\arguments{ +\item{fund_data}{A data frame containing fund data} + +\item{threshold}{A numeric value between 0 and 1 (inclusive) indicating the +allowable percentage of the total fund value that the summed values of its +component holdings should be equal to or greater than} +} +\value{ +A tibble +} +\description{ +Prepare fund data, filtering to funds with data according to a given +threshold and adding a \code{MISSINGWEIGHT} holding for the difference +} diff --git a/man/prepare_isin_to_fund_table.Rd b/man/prepare_isin_to_fund_table.Rd new file mode 100644 index 0000000..0fb5d9f --- /dev/null +++ b/man/prepare_isin_to_fund_table.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_isin_to_fund_table.R +\name{prepare_isin_to_fund_table} +\alias{prepare_isin_to_fund_table} +\title{Prepare \code{isin_to_fund_table}, filtering out fsyms that have more than 1 row +and either no fund data or fund data for both rows} +\usage{ +prepare_isin_to_fund_table(isin_to_fund_table, fund_data) +} +\arguments{ +\item{isin_to_fund_table}{A data frame containing isin_to_fund_table data} + +\item{fund_data}{A data frame containing fund data} +} +\value{ +A tibble +} +\description{ +Prepare \code{isin_to_fund_table}, filtering out fsyms that have more than 1 row +and either no fund data or fund data for both rows +} diff --git a/man/prepare_iss_average_sector_emission_intensities.Rd b/man/prepare_iss_average_sector_emission_intensities.Rd new file mode 100644 index 0000000..149ba36 --- /dev/null +++ b/man/prepare_iss_average_sector_emission_intensities.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in +% R/prepare_iss_average_sector_emission_intensities.R +\name{prepare_iss_average_sector_emission_intensities} +\alias{prepare_iss_average_sector_emission_intensities} +\title{Prepare a \code{iss_average_sector_emission_intensities} object} +\usage{ +prepare_iss_average_sector_emission_intensities( + iss_company_emissions, + factset_financial_data, + factset_entity_info, + factset_entity_financing_data, + currencies +) +} +\arguments{ +\item{iss_company_emissions}{A data frame containing \code{iss_company_emissions} +data} + +\item{factset_financial_data}{A data frame containing +\code{factset_financial_data} data} + +\item{factset_entity_info}{A data frame containing \code{factset_entity_info} data} + +\item{factset_entity_financing_data}{A data frame containing +\code{factset_entity_financing_data} data} + +\item{currencies}{A data frame containing currency exchange rate data} +} +\value{ +A data frame containing the prepared +\code{iss_average_sector_emission_intensities} object +} +\description{ +Prepare a \code{iss_average_sector_emission_intensities} object +} diff --git a/man/prepare_iss_company_emissions.Rd b/man/prepare_iss_company_emissions.Rd new file mode 100644 index 0000000..cad8ce1 --- /dev/null +++ b/man/prepare_iss_company_emissions.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_iss_company_emissions.R +\name{prepare_iss_company_emissions} +\alias{prepare_iss_company_emissions} +\title{Prepare an \code{iss_company_emissions} object from \code{factset_iss_emissions_data}} +\usage{ +prepare_iss_company_emissions(factset_iss_emissions_data) +} +\arguments{ +\item{factset_iss_emissions_data}{A data frame containing ISS emissions data} +} +\value{ +A tibble +} +\description{ +Prepare an \code{iss_company_emissions} object from \code{factset_iss_emissions_data} +} diff --git a/man/prepare_iss_entity_emission_intensities.Rd b/man/prepare_iss_entity_emission_intensities.Rd new file mode 100644 index 0000000..6585236 --- /dev/null +++ b/man/prepare_iss_entity_emission_intensities.Rd @@ -0,0 +1,35 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_iss_entity_emission_intensities.R +\name{prepare_iss_entity_emission_intensities} +\alias{prepare_iss_entity_emission_intensities} +\title{Prepare a \code{iss_entity_emission_intensities} object} +\usage{ +prepare_iss_entity_emission_intensities( + iss_company_emissions, + factset_financial_data, + factset_entity_info, + factset_entity_financing_data, + currencies +) +} +\arguments{ +\item{iss_company_emissions}{A data frame containing \code{iss_company_emissions} +data} + +\item{factset_financial_data}{A data frame containing +\code{factset_financial_data} data} + +\item{factset_entity_info}{A data frame containing \code{factset_entity_info} data} + +\item{factset_entity_financing_data}{A data frame containing +\code{factset_entity_financing_data} data} + +\item{currencies}{A data frame containing currency exchange rate data} +} +\value{ +A data frame containing the prepared +\code{iss_entity_emission_intensities} object +} +\description{ +Prepare a \code{iss_entity_emission_intensities} object +} diff --git a/man/prepare_masterdata.Rd b/man/prepare_masterdata.Rd new file mode 100644 index 0000000..e70a458 --- /dev/null +++ b/man/prepare_masterdata.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_masterdata.R +\name{prepare_masterdata} +\alias{prepare_masterdata} +\title{Prepare the \code{masterdata_ownership_datastore} or \code{masterdata_debt_datastore} +output data frame from an import of a raw AR masterdata_* CSV} +\usage{ +prepare_masterdata( + data, + ar_company_id__country_of_domicile, + pacta_financial_timestamp, + zero_emission_factor_techs +) +} +\arguments{ +\item{data}{A dataframe containing the raw input of an AR masterdata_* CSV +files} + +\item{ar_company_id__country_of_domicile}{A data frame with two columns +mapping \code{ar_company_id} to \code{country_of_domicile}} + +\item{pacta_financial_timestamp}{A single element character vector specifying +the timestamp in the PACTA format, e.g. "2021Q4"} + +\item{zero_emission_factor_techs}{A character vector listing technologies +that will have emission factors manually forced to 0} +} +\value{ +A tibble properly prepared to be saved as the +\code{masterdata_ownership_datastore.rds} or \code{masterdata_debt_datastore.rds} +output file +} +\description{ +Prepare the \code{masterdata_ownership_datastore} or \code{masterdata_debt_datastore} +output data frame from an import of a raw AR masterdata_* CSV +} diff --git a/man/prepare_masterdata_debt.Rd b/man/prepare_masterdata_debt.Rd new file mode 100644 index 0000000..7b93e40 --- /dev/null +++ b/man/prepare_masterdata_debt.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_masterdata_debt.R +\name{prepare_masterdata_debt} +\alias{prepare_masterdata_debt} +\title{Prepare the \code{masterdata_debt_datastore} object from a raw masterdata_debt CSV} +\usage{ +prepare_masterdata_debt( + masterdata_debt_raw, + ar_company_id__country_of_domicile, + ar_company_id__credit_parent_ar_company_id, + pacta_financial_timestamp, + zero_emission_factor_techs +) +} +\arguments{ +\item{masterdata_debt_raw}{A data frame containing the raw data from a +masterdata_debt CSV} + +\item{ar_company_id__country_of_domicile}{A data frame containing an +\code{ar_company_id} to \code{country_of_domicile} lookup} + +\item{ar_company_id__credit_parent_ar_company_id}{A data frame containing an +\code{ar_company_id} to \code{credit_parent_ar_company_id} lookup} + +\item{pacta_financial_timestamp}{A single character vector containing the +PACTA financial timestamp, e.g. \verb{2023Q4}} + +\item{zero_emission_factor_techs}{A character vector containing the zero +emission factor technologies} +} +\value{ +A data frame containing the prepared masterdata_debt_datastore +} +\description{ +Prepare the \code{masterdata_debt_datastore} object from a raw masterdata_debt CSV +} diff --git a/man/prepare_total_fund_list.Rd b/man/prepare_total_fund_list.Rd new file mode 100644 index 0000000..8621185 --- /dev/null +++ b/man/prepare_total_fund_list.Rd @@ -0,0 +1,17 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prepare_total_fund_list.R +\name{prepare_total_fund_list} +\alias{prepare_total_fund_list} +\title{Prepare a \code{total_fund_list} object from \code{fund_data}} +\usage{ +prepare_total_fund_list(fund_data) +} +\arguments{ +\item{fund_data}{A data frame containing fund data} +} +\value{ +A tibble +} +\description{ +Prepare a \code{total_fund_list} object from \code{fund_data} +} diff --git a/man/quarter_to_factset_timestamp.Rd b/man/quarter_to_factset_timestamp.Rd new file mode 100644 index 0000000..30cb10d --- /dev/null +++ b/man/quarter_to_factset_timestamp.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/quarter_to_factset_timestamp.R +\name{quarter_to_factset_timestamp} +\alias{quarter_to_factset_timestamp} +\title{Convert a PACTA style quarter string to a FactSet style date string for the +last day of that quarter} +\usage{ +quarter_to_factset_timestamp(quarter) +} +\arguments{ +\item{quarter}{A character vector containing PACTA style quarter strings in +the form e.g. "2021Q4"} +} +\value{ +A character vector containing the equivalent FactSet style date +strings for the last day of the quarter e.g. "2021-12-31" +} +\description{ +Convert a PACTA style quarter string to a FactSet style date string for the +last day of that quarter +} diff --git a/man/quarter_to_imf_timestamp.Rd b/man/quarter_to_imf_timestamp.Rd new file mode 100644 index 0000000..dbe830a --- /dev/null +++ b/man/quarter_to_imf_timestamp.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/quarter_to_imf_timestamp.R +\name{quarter_to_imf_timestamp} +\alias{quarter_to_imf_timestamp} +\title{Convert a PACTA style quarter string to an IMF style quarter string} +\usage{ +quarter_to_imf_timestamp(quarter) +} +\arguments{ +\item{quarter}{A character vector containing PACTA style quarter strings in +the form e.g. "2021Q4"X} +} +\value{ +A character vector containing the equivalent IMF style quarter +strings e.g. "2021-Q4" +} +\description{ +Convert a PACTA style quarter string to an IMF style quarter string +} diff --git a/man/standardize_asset_type_names.Rd b/man/standardize_asset_type_names.Rd new file mode 100644 index 0000000..fc4e294 --- /dev/null +++ b/man/standardize_asset_type_names.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/standardize_asset_type_names.R +\name{standardize_asset_type_names} +\alias{standardize_asset_type_names} +\title{Standardize asset type names} +\usage{ +standardize_asset_type_names(factset_issue_code_bridge) +} +\arguments{ +\item{factset_issue_code_bridge}{A data frame containing the FactSet issue +code bridge} +} +\value{ +A data frame containing a FactSet \code{issue_type_code} to \code{asset_type} +lookup +} +\description{ +Standardize asset type names +} diff --git a/man/write_manifest.Rd b/man/write_manifest.Rd new file mode 100644 index 0000000..ccfd0fd --- /dev/null +++ b/man/write_manifest.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/write_manifest.R +\name{write_manifest} +\alias{write_manifest} +\title{Write a manifest.json file to the specified path including critical +information about the files and parameters used to prepare the data} +\usage{ +write_manifest(path, parameters, input_files, output_files) +} +\arguments{ +\item{path}{A single string specifying a filepath to save the JSON file to} + +\item{parameters}{A list containing all parameters used to create the data} + +\item{input_files}{A vector with filepaths of input files used to create the +output data.} + +\item{output_files}{A vector with filepaths of output files created.} +} +\value{ +Called for the side-effect of writing a JSON file to disk +} +\description{ +Write a manifest.json file to the specified path including critical +information about the files and parameters used to prepare the data +} diff --git a/pacta.data.preparation.Rproj b/pacta.data.preparation.Rproj new file mode 100644 index 0000000..28ead6c --- /dev/null +++ b/pacta.data.preparation.Rproj @@ -0,0 +1,22 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX + +AutoAppendNewline: Yes +StripTrailingWhitespace: Yes +LineEndingConversion: Posix + +BuildType: Package +PackageUseDevtools: Yes +PackageInstallArgs: --no-multiarch --with-keep.source +PackageRoxygenize: rd,collate,namespace,vignette diff --git a/tests/testthat.R b/tests/testthat.R new file mode 100644 index 0000000..8ec9dc1 --- /dev/null +++ b/tests/testthat.R @@ -0,0 +1,12 @@ +# This file is part of the standard setup for testthat. +# It is recommended that you do not modify it. +# +# Where should you do additional test configuration? +# Learn more about the roles of various files in: +# * https://r-pkgs.org/tests.html +# * https://testthat.r-lib.org/reference/test_package.html#special-files + +library(testthat) +library(pacta.data.preparation) + +test_check("pacta.data.preparation") diff --git a/tests/testthat/helper-fake_data.R b/tests/testthat/helper-fake_data.R new file mode 100644 index 0000000..245aca3 --- /dev/null +++ b/tests/testthat/helper-fake_data.R @@ -0,0 +1,104 @@ +#' Minimal datasets that allow over-writing +#' +#' These functions are developer-oriented. They all call [tibble::tibble()] so +#' you can expect all the goodies that come with that. +#' +#' @section Params +#' The arguments are the column names of the datasets being faked. They all have +#' a default and it can be overwritten. +#' +#' @section Pros and cons +#' These functions help you to avoid duplicating test code, and help +#' the reader of your code to focus on the one thing you want to test, instead +#' of burying that thing in the much longer code you need to create a fake +#' object from scratch. +#' +#' But `fake_*()` functions hide the explicit content. If the reader of your +#' code wants to inspect the data being tested, they need to jump to the +#' function definition or call them interactively. +#' +#' @return A data frame +#' +#' @examples +#' fake_entity_info() +#' +#' # Helps invalidate values for tests +#' fake_entity_info(factset_entity_id = "bad") +#' +#' # tibble() goodies: +#' +#' # Create new columns on the fly +#' fake_entity_info(a = "new") +#' +#' # Support for trailing commas +#' fake_entity_info(id = 1, ) +#' @noRd +fake_entity_info <- function(factset_entity_id = NULL, + company_name = NULL, + country_of_domicile = NULL, + bics_sector = NULL, + security_bics_subgroup = NULL, + security_mapped_sector = NULL, + ar_company_id = NULL, + ...) { + dplyr::tibble( + factset_entity_id = factset_entity_id %||% "002KY7-E", + company_name = company_name %||% "WW Energy, Inc.", + country_of_domicile = country_of_domicile %||% "US", + bics_sector = bics_sector %||% "Industrial Services", + security_bics_subgroup = security_bics_subgroup %||% "Oil & Gas Power Plant", + security_mapped_sector = security_mapped_sector %||% "Oil&Gas", + ... + ) +} + +fake_masterdata <- function(creditor_company_id = NULL, + company_id = NULL, + company_name = NULL, + is_ultimate_parent = NULL, + is_ultimate_listed_parent = NULL, + has_financial_data = NULL, + sector = NULL, + technology = NULL, + technology_type = NULL, + asset_type = NULL, + asset_country = NULL, + emissions_factor = NULL, + emissions_factor_unit = NULL, + metric = NULL, + unit = NULL, + `_2021` = NULL, + asset_level_timestamp = NULL, + ...) { + dplyr::tibble( + company_id = company_id %||% "10001", + sector = sector %||% "Power", + technology = technology %||% "CoalCap", + asset_country = asset_country %||% "US", + emissions_factor = emissions_factor %||% 0.123, + emissions_factor_unit = emissions_factor_unit %||% "tCO2e/MWh", + metric = metric %||% "direct production", + unit = unit %||% "MW", + `_2021` = `_2021` %||% 123.45, + asset_level_timestamp = asset_level_timestamp %||% "2021Q4", + ... + ) +} + +fake_ar_company_id__country_of_domicile <- function(ar_company_id = NULL, + country_of_domicile = NULL, + ...) { + dplyr::tibble( + ar_company_id = ar_company_id %||% "10001", + country_of_domicile = country_of_domicile %||% "US", + ... + ) +} + +`%||%` <- function(x, y) { + if (is.null(x)) { + y + } else { + x + } +} diff --git a/tests/testthat/test-calc_shares_all_classes.R b/tests/testthat/test-calc_shares_all_classes.R new file mode 100644 index 0000000..2eaf882 --- /dev/null +++ b/tests/testthat/test-calc_shares_all_classes.R @@ -0,0 +1,94 @@ +test_that("returns a numeric value", { + expect_type(calc_shares_all_classes(1, "EQ"), "double") +}) + + +test_that("returns a single value", { + shares_outstanding <- c(NA, 1:5, NA) + asset_type_id <- "EQ" + result <- calc_shares_all_classes(shares_outstanding, asset_type_id) + expect_length(result, 1L) +}) + + +test_that("returns expected value", { + shares_outstanding <- c(1, 1, 1, 1) + asset_type_id <- c("CP", "EQ", "PF", "XXX") + expected_result <- 3 + + result <- calc_shares_all_classes(shares_outstanding, asset_type_id) + expect_equal(result, expected_result) + + + shares_outstanding <- c(1, 1, 1, 1) + asset_type_id <- c("CP", "EQ", "PF", NA) + expected_result <- 3 + + result <- calc_shares_all_classes(shares_outstanding, asset_type_id) + expect_equal(result, expected_result) + + + shares_outstanding <- c(1, 1, NA, 1) + asset_type_id <- c("CP", "EQ", "PF", "XXX") + expected_result <- 2 + + result <- calc_shares_all_classes(shares_outstanding, asset_type_id) + expect_equal(result, expected_result) + + + shares_outstanding <- c(1, 1, 1, 1) + asset_type_id <- c("CP", "EQ", "PF", "XXX") + include_types <- c("CP", "EQ", "PF", "XXX") + expected_result <- 4 + + result <- calc_shares_all_classes(shares_outstanding, asset_type_id, include_types) + expect_equal(result, expected_result) + + + shares_outstanding <- c(1, 1, 1, 1) + asset_type_id <- c("CP", "EQ", "PF", "XXX") + include_types <- c("CP") + expected_result <- 1 + + result <- calc_shares_all_classes(shares_outstanding, asset_type_id, include_types) + expect_equal(result, expected_result) +}) + + +test_that("returns expected values when used in dplyr chain", { + shares_outstanding <- c(1, 1, 1, 1) + asset_type_id <- c("CP", "EQ", "PF", "XXX") + expected_result <- c(3, 3, 3, 3) + + result <- + data.frame( + shares_outstanding = shares_outstanding, + asset_type_id = asset_type_id + ) %>% + mutate(shares_all_classes = calc_shares_all_classes(shares_outstanding, asset_type_id)) %>% + dplyr::pull(shares_all_classes) + + expect_equal(result, expected_result) +}) + + +test_that("returns expected values when used in dplyr chain by an entity ID", { + shares_outstanding <- c(1, 1, 1, 1, 2, 2, 2, 2) + asset_type_id <- c("CP", "EQ", "PF", "XXX", "CP", "EQ", "PF", "XXX") + entity_id <- c("A", "A", "A", "A", "B", "B", "B", "B") + expected_result <- c(3, 3, 3, 3, 6, 6, 6, 6) + + result <- + data.frame( + shares_outstanding = shares_outstanding, + asset_type_id = asset_type_id, + entity_id = entity_id + ) %>% + mutate( + shares_all_classes = calc_shares_all_classes(shares_outstanding, asset_type_id), + .by = "entity_id" + ) %>% + dplyr::pull(shares_all_classes) + + expect_equal(result, expected_result) +}) diff --git a/tests/testthat/test-check_crucial_names.R b/tests/testthat/test-check_crucial_names.R new file mode 100644 index 0000000..3fda5b9 --- /dev/null +++ b/tests/testthat/test-check_crucial_names.R @@ -0,0 +1,24 @@ +test_that("check_crucial_names with expected names returns `x` invisibly", { + x <- c(a = 1) + expect_silent(out <- check_crucial_names(x, "a")) + expect_identical(x, out) + + x <- data.frame(a = 1) + expect_silent(out <- check_crucial_names(x, "a")) + expect_identical(x, out) +}) + +test_that("check_crucial_names with expected names returns `x` invisibly", { + x <- c(a = 1) + expect_error(check_crucial_names(x, "b"), class = "missing_names") + + x <- data.frame(a = 1) + expect_error(check_crucial_names(x, "b"), class = "missing_names") +}) + +test_that("check_crucial_names w/ duplicated expected informs unique", { + expect_error( + check_crucial_names(c(x = 1), expected_names = c("x", "z", "a")), + class = "missing_names" + ) +}) diff --git a/tests/testthat/test-expand_tech_rows.R b/tests/testthat/test-expand_tech_rows.R new file mode 100644 index 0000000..f90767f --- /dev/null +++ b/tests/testthat/test-expand_tech_rows.R @@ -0,0 +1,35 @@ +data <- dplyr::tribble( + ~id_name, ~id, ~equity_market, ~scenario_geography, ~ald_sector, ~technology, ~year, ~plan_emission_factor, ~plan_tech_prod, ~current_plan_row, + "ar_company_id", "100067", "DevelopedMarket", "Global", "Power", "HydroCap", 2022, 0, 41.9770564, 1, + "ar_company_id", "100067", "DevelopedMarket", "Global", "Power", "HydroCap", 2023, 0, 41.9770564, 1, + "ar_company_id", "100067", "DevelopedMarket", "Global", "Power", "HydroCap", 2024, 0, 41.9770564, 1, + "ar_company_id", "100067", "DevelopedMarket", "Global", "Power", "RenewablesCap", 2022, 0, 1.06002, 1 +) + +output <- expand_tech_rows(data) + + +test_that("for given input, returns a tibble", { + expect_s3_class(output, "tbl_df") +}) + +test_that("for given input, expands the tibble to the proper number of rows", { + expect_equal(nrow(output), 6L) +}) + +test_that("for given input, adds the expected years to the technology that was missing them", { + renew_years <- output$year[output$technology == "RenewablesCap"] + expect_equal(renew_years, c(2022, 2023, 2024)) +}) + +test_that("for given input, adds `NA` to `plan_emission_factor` for the added years", { + expect_equal(rev(output$plan_emission_factor)[1:2], c(NA_real_, NA_real_)) +}) + +test_that("for given input, adds `0` to `plan_tech_prod` for the added years", { + expect_equal(rev(output$plan_tech_prod)[1:2], c(0, 0)) +}) + +test_that("for given input, adds `0` to `current_plan_row` for the added years", { + expect_equal(rev(output$current_plan_row)[1:2], c(0, 0)) +}) diff --git a/tests/testthat/test-filter_most_recent_timestamp.R b/tests/testthat/test-filter_most_recent_timestamp.R new file mode 100644 index 0000000..af467ce --- /dev/null +++ b/tests/testthat/test-filter_most_recent_timestamp.R @@ -0,0 +1,48 @@ +test_that("with bad `data` errors with informative message", { + data <- dplyr::tibble( + sector = "aviation", + bad = "2021Q4" + ) + + expect_error( + filter_most_recent_timestamp("bad", "2020Q4"), + "data.frame.*not.*TRUE", + ) + + expect_error( + filter_most_recent_timestamp(data, FALSE), + "character.*not.*TRUE", + ) +}) + +test_that("with missing crucial columns errors with informative message", { + bad_data <- dplyr::tibble( + sector = "aviation", + bad = "2021Q4" + ) + + expect_error( + filter_most_recent_timestamp(bad_data, "2020Q4"), + "names.*data.*not.*TRUE", + ) +}) + +test_that("for given input, outputs expected values", { + pacta_financial_timestamp <- "2021Q4" + + data <- dplyr::tribble( + ~sector, ~asset_level_timestamp, + "aviation", "2021Q4", + "power", "2021Q1", + "power", "2021Q2", + "automotive", "2020Q4", + "automotive", "2022Q1" + ) + + out <- filter_most_recent_timestamp(data, pacta_financial_timestamp, sector) %>% + split(.$sector) + + expect_equal(out$automotive$asset_level_timestamp, "2020Q4") + expect_equal(out$aviation$asset_level_timestamp, "2021Q4") + expect_equal(out$power$asset_level_timestamp, "2021Q2") +}) diff --git a/tests/testthat/test-override_sector_by_factset_entity_id.R b/tests/testthat/test-override_sector_by_factset_entity_id.R new file mode 100644 index 0000000..d649971 --- /dev/null +++ b/tests/testthat/test-override_sector_by_factset_entity_id.R @@ -0,0 +1,47 @@ +test_that("with known input, outputs as expected", { + entity_info <- fake_entity_info( + factset_entity_id = "002KY7-E", + security_mapped_sector = "Other" + ) + + sector_override <- dplyr::tibble( + factset_entity_id = "002KY7-E", + pacta_sector_override = "Power" + ) + + out <- override_sector_by_factset_entity_id(entity_info, sector_override) + + expect_equal(out$security_mapped_sector, "Power") +}) + +test_that("with no matching factset_entity_id, outputs original sector", { + entity_info <- fake_entity_info( + factset_entity_id = "002KY7-E", + security_mapped_sector = "Other" + ) + + sector_override <- dplyr::tibble( + factset_entity_id = "Bad", + pacta_sector_override = "Power" + ) + + out <- override_sector_by_factset_entity_id(entity_info, sector_override) + + expect_equal(out$security_mapped_sector, "Other") +}) + +test_that("only modifies sector of matched entity", { + entity_info <- fake_entity_info( + factset_entity_id = c("002KY7-E", "Other"), + security_mapped_sector = "Other" + ) + + sector_override <- dplyr::tibble( + factset_entity_id = "002KY7-E", + pacta_sector_override = "Power" + ) + + out <- override_sector_by_factset_entity_id(entity_info, sector_override) + + expect_equal(out$security_mapped_sector, c("Power", "Other")) +}) diff --git a/tests/testthat/test-prepare_masterdata.R b/tests/testthat/test-prepare_masterdata.R new file mode 100644 index 0000000..1dcfc0d --- /dev/null +++ b/tests/testthat/test-prepare_masterdata.R @@ -0,0 +1,15 @@ +test_that('masterdata with Power technologies both w/ and wo/ "Cap" are formatted as expected (#285)', { + + out <- fake_masterdata( + company_id = c(1, 2, 3), + sector = c("Power", "Power", "Coal"), # test several sectors + technology = c("Coal", "CoalCap", "Coal") # test input both w/ and wo/ "Cap" + ) %>% + prepare_masterdata( + ar_company_id__country_of_domicile = fake_ar_company_id__country_of_domicile(), + pacta_financial_timestamp = "2021Q4", + zero_emission_factor_techs = "RenewablesCap" + ) + + expect_equal(out$technology, c("CoalCap", "CoalCap", "Coal")) +}) diff --git a/tests/testthat/test-quarter_to_factset_timestamp.R b/tests/testthat/test-quarter_to_factset_timestamp.R new file mode 100644 index 0000000..c347bda --- /dev/null +++ b/tests/testthat/test-quarter_to_factset_timestamp.R @@ -0,0 +1,21 @@ +test_that("for valid input, outputs as expected", { + expect_equal(quarter_to_factset_timestamp(quarter = "2021Q1"), "2021-03-31") + expect_equal(quarter_to_factset_timestamp(quarter = "2021Q2"), "2021-06-30") + expect_equal(quarter_to_factset_timestamp(quarter = "2021Q3"), "2021-09-30") + expect_equal(quarter_to_factset_timestamp(quarter = "2021Q4"), "2021-12-31") + expect_equal(quarter_to_factset_timestamp(quarter = "2022Q4"), "2022-12-31") +}) + +test_that("for invalid input, errors as expected", { + expect_error(quarter_to_factset_timestamp(quarter = "2021Q5")) + expect_error(quarter_to_factset_timestamp(quarter = "2021-Q4")) + expect_error(quarter_to_factset_timestamp(quarter = "2022-Q4")) + expect_error(quarter_to_factset_timestamp(quarter = "XXX")) +}) + +test_that("for valid vector of inpouts, outputs as expected", { + expect_equal( + quarter_to_factset_timestamp(quarter = c("2021Q4", "2022Q4")), + c("2021-12-31", "2022-12-31") + ) +}) diff --git a/tests/testthat/test-quarter_to_imf_timestamp.R b/tests/testthat/test-quarter_to_imf_timestamp.R new file mode 100644 index 0000000..2f9c9f2 --- /dev/null +++ b/tests/testthat/test-quarter_to_imf_timestamp.R @@ -0,0 +1,21 @@ +test_that("for valid input, outputs as expected", { + expect_equal(quarter_to_imf_timestamp(quarter = "2021Q1"), "2021-Q1") + expect_equal(quarter_to_imf_timestamp(quarter = "2021Q2"), "2021-Q2") + expect_equal(quarter_to_imf_timestamp(quarter = "2021Q3"), "2021-Q3") + expect_equal(quarter_to_imf_timestamp(quarter = "2021Q4"), "2021-Q4") + expect_equal(quarter_to_imf_timestamp(quarter = "2022Q4"), "2022-Q4") +}) + +test_that("for invalid input, errors as expected", { + expect_error(quarter_to_imf_timestamp(quarter = "2021Q5")) + expect_error(quarter_to_imf_timestamp(quarter = "2021-Q4")) + expect_error(quarter_to_imf_timestamp(quarter = "2022-Q4")) + expect_error(quarter_to_imf_timestamp(quarter = "XXX")) +}) + +test_that("for valid vector of inpouts, outputs as expected", { + expect_equal( + quarter_to_imf_timestamp(quarter = c("2021Q4", "2022Q4")), + c("2021-Q4", "2022-Q4") + ) +}) diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 0000000..097b241 --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R diff --git a/vignettes/Airbus_1.png b/vignettes/Airbus_1.png new file mode 100644 index 0000000000000000000000000000000000000000..8e29fbee285a7619a76c41c26fb65132f40a17db GIT binary patch literal 126458 zcmd?RbySq!`!=eA0@B?eoq`C`FoL8=C@s<;DBV3nccUQEpn^)*AT@MIDiTry%mC6b z5&uT_t;Z2=F2PAa-8*RIv2kz#Cb0?#C#TIN32 zt}*ca{kg9FjQ8-`HT*|yH5H>kt6j^R;q24X${vV)m9<+WBph7bx9J2YIY}v0bn3Wo zJ{Eeu`0+lKeYA;|CFB#)(8b2_Z=pL5_h#`D5F z_VZ*UV}AZrC%J}`5u~ftzHJaAO>_4)(=+2|5A&i0n0o6f3>>ttOHK|Hdoz(53)*m_ z{qEM&-^=bX=vL=dMGo?#G{NJjcuWQerHLC#yMax^WE2r_L)=!x>r{;Nf)mSB`3+`b 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+title: "Share Ownership Weighting and Share Class Inclusion in PACTA" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Share Ownership Weighting and Share Class Inclusion in PACTA} + %\VignetteEncoding{UTF-8} + %\VignetteEngine{knitr::rmarkdown} +editor_options: + markdown: + wrap: 72 +--- + +PACTA methodology note\ +**Share Ownership Weighting and Share Class Inclusion in PACTA** + +Prepared by Nicholas Dodd + +## 1. Background + +### 1.1 Ownership-based attribution methods + +In order to attribute an investee companies production or climate alignment results to a financial portfolio a weighting method is required. This methodology note describes the share ownership attribution method for listed equity holdings. This weighting method is based on the individual shares owned by an investor. + +Other possible weighting methods for listed equity include equity ownership, which weights based on the market value of the shares held, and voting influence, which weights based on the number of AGM votes associated with the shares held. + +### 1.2 Defining share ownership + +Share ownership in the context of PACTA refers to the act of holding a proportion of the publicly listed shares of a given company. Each share, also sometimes referred to as stock, represents an ownership stake in the company that confers the holder with rights to a share of the dividends and/or influence on company decision making. + +## 2. The share ownership weight attribution method + +The share ownership weight method is one of several methods that can be used to attribute a companies production or climate performance to a financial portfolio based on the percentage of a companies outstanding public shares that are held. + +For holdings in a given entity, share ownership weight is defined as the total number of outstanding shares in all classes of shares within scope held by the investor (the numerator) divided by the total number of outstanding shares for the entity across all listed security issuance for the entity (the denominator). Each listed security issuance is denoted by an ISIN in the dataset. + +$$Share \: ownership \: weight = \dfrac{\sum outstanding \: shares \: held \: by \: the \: investor \: (SC_{eq} + SC_{pf} + SC_{dl} + SC_{cp} + (SC_{ad} \times AD_{cf}))}{\sum outstanding \: shares \: of \: the \: entity \: (SC_{eq} + SC_{pf} + SC_{cp})}$$ +Where:\ +$SC_{ST}$ are the Share Class and Share Type of each security held by an investor (see table 2.1) $AD_{cf}$ is the conversion factor required to derive the number of ordinary shares from the number of depositary note shares + +As the number of outstanding shares held by an investor is not provided by users, rather the financial value of the shares held, the number of shares held per security (as denoted by an individual ISIN) must first be obtained by dividing the value of the shares held by the market value of each share. + +## 3. The scope of share classes outstanding + +The scope of share classes to be included within the calculation of both the total number of shares outstanding held by an investor (the numerator) and the total number of shares outstanding for the entity as a whole is summarized in table 1.1. The share type bridge and codes identified are proprietary to the use of financial data supplied by FactSet. + +Table 1.1 Scope of share classes to be included within the share ownership weight calculation + +| Share type (from FactSet bridge) | Included within the numerator for investor holdings? | Included within the denominator for entity shares outstanding? | +|:----------------------------------|:------------------|:------------------| +| Equity (EQ) | Yes | Yes | +| Equity (Pre-IPO) | No | No | +| Preferred (PF) | Yes | Yes | +| Treasury (no bridge code) | No | No | +| Dual listing (DL) | Yes, if identified | No | +| Convertible Preferred (CP) | Yes | Yes | +| ADR/GDR (AD) | Yes, with conversion factor applied | No | +| Convertible bond (CB) | No | No | + +## 4. How share classes should be calculated + +### 4.1 Common and preferred share classes + +A number of different classes of shares may be issued as part of a public listing. These include + +- Common share capital (EQ) that confers the holder with voting rights at the Annual General Meeting. +- Preferred share capital (PF) that confers the holder with specific rights to dividend payments but are not accompanied by voting rights. +- Convertible share capital (CP) confers a right to convert the preferred shares with a fixed income stream to common shares at a future date. + +Common and preferred shares will have different prices and may be also be issued in distinct share classes, such as A, B, and R. These shared classes are targeted at specific types of investors and may have different share prices, administrative costs and associated voting rights. + +> **Case study: Duke Energy -- preferred share class** +> +> The screenshot below presents the common and preferred shares that Duke Energy has issued on listed stock exchanges. The first two ISINs in the list are preferred shares, and the third ISIN represents common shares. +> +> The market capitalization of the company is the the sum of the market value (price multiplied by outstanding shares) of all the company's ISINs, and the number of shares issued by the company is the sum of the number of shares outstanding at security level. In this case 770.08+40.00+10.00=820.08 million outstanding shares. The column "Shares Out" reflects the total common outstanding shares. +> +> The column "Shares Out" reflects the total common outstanding shares. +> +> ![](Duke_Energy.png){#id .class width=100%} + +*How should these types of shares be treated?* + +All common equity shares (EQ), preferred shares (PF) and Convertible preferred shares (CP) are to be included in the total of the numerator and denominator. + +*How should the number of shares be back calculated from the market value?* + +In order to derive the number of shares the market value of outstanding shares held by an investor should be divided by the market price. + +### 4.2 Treasury shares + +Treasury shares are common or preferred shares that have been bought back by an issuer or were never publicly issued and are instead held by the issuer on their balance sheet. These shares are not strictly a class of shares and are not included within outstanding share values provided by FactSet. For the purposes of calculating the share ownership weight treasury shares are therefore excluded. + +> **Case study: Airbus – treasury shares** +> +> Airbus issued shares on the Amsterdam stock exchange. Around 25% of the shares are held as treasury by the company and are not available in the market, as can be seen in the chart below. Those shares are excluded from calculations of the denominator as institutions cannot buy those assets on the market. +> +> ![](Airbus_1.png){#id .class width=45%} ![](Airbus_2.png){#id .class width=45%} + +*How should these types of shares be treated?* + +Treasury shares should be excluded if they can be identified. + +### 4.3 Depository note (ADR/GDR) ISINs + +Depositary notes are used in the US and other jurisdictions globally to enable investors to purchase non-US shares issued on exchanges in other jurisdictions. The shares are purchased by a bank or other intermediary who issue depositary notes for the value of the shares, applying currency conversion factors and a corresponding adjustment of the number of shares. + +American Depositary Receipts (ADR) and Global Depositary Receipts (GDR) are common examples. The holder of ADR or GDR do not directly hold shares in listed securities issuers, instead they correspond to listed shares purchased by depositary banks that issue the receipts. + +> **Case study: BHP Billingdon – US and Argentinian depositary notes** +> +> BHP Billingdon has made on equity share issue against a specific ISIN on the Australian Stock Exchange market. This is their primary issuance of listed equity and so the number of shares outstanding of Billingdon is the one for of the Australian ISIN. +> +> Two issuances of depositary notes have been made against ISINs in the USA and Argentina, allowing investors there to purchase notes equivalent to shares bought on the Australian market. Those depositary note shares therefore represent a proportion of the total outstanding shares of Billingdon on the Australian market, but to calculate how many the conversion factor based on the currency exchange rate needs be applied. +> +> ![](BHP.png){#id .class width=100%} + +*How should these types of shares be treated?* + +ADR and GDR do in practice represent and are equivalent to indirectly held listed shares. This means that they should be included in investors holdings in the total of the numerator. However, a conversion factor must be applied in order to obtain the correct equivalent number of shares. + +*How should the number of shares be back calculated from the market value?* + +In order to derive the number of shares the market value of outstanding shares held by an investor should be divided by the market price. A conversion factor must then be applied in order to obtain the correct equivalent number of equity shares. + +### 4.4 Dual listed share class ISINs + +In some cases, companies make the same common and preferred share issuance available on more than one listed exchange, generally referred to as dual listing. Whilst each issuance on an exchange has a distinct ISIN, in practice dual listing gives investors in more than one jurisdiction access to the same pool of equity (the primary listing). + +> **Case study: Stora Enso – dual listing and multiple share classes in Finland and Sweden** +> +> Stora Enso has made two primary issuances in Finland which have then been made available on another national listed exchange in Sweden. The primary issuance is traded on the Helsinski Stock Exchange, these are then dual listed on the Stockholm stock exchange. A depositary note issuance has also been made in the USA. +> +> The two Dual Listed ISIN can be seen in the data to mirror the two Finnish ISINs. The data from FactSet shows that the number of shares outstanding is 788 million, which is the sum of the two Finnish traded ISIN. The data shows that neither the shares associated with the Dual Listings nor the ADR listing should be included in the denominator calculation. +> +> ![](Stora_Enso.png){#id .class width=100%} +> +> However, from an investor's point of view the three last ISIN are mirrors of the first two ISIN. This means that in practice if you own those shares, that still means that the investor either owns, or an entity has bought on the investors behalf, the same shares in the first two ISIN, and by transposition you still own those shares, and by consequence the underlying assets. +> +> Both issuances against the Finnish ISINs are common stocks. They do, however, differ based on the shares class. The first ISIN is a class A common stocks and the second ISIN is a class R common stock which results in different voting rights. The first ISIN will have 10 votes for a share, while the second one will have 1 vote per share. + +*How should these types of shares be treated?* + +Whilst the shares held by an investor are directly held securities and should be included in the total of the numerator, the total shares outstanding issued across several exchanges cannot be totalled as there would be a duplication of the shares caused by secondary listings. Only the shares associated with the primary listing should be totaled in the denominator. + +*How should the number of shares be back calculated from the market value?* + +In order to derive the number of shares the market value of outstanding shares held by an investor should be divided by the market price. + +## 5. Corporate actions that may result in changes to ISINs + +According to the ISIN organization so called 'corporate actions' may result in changes in the status of a company's securities and their identifiers^1^. The financial data provider is likely to take into account these changes and reflect them in updates to the financial dataset. However, these actions may still need to be taken into account in the process of calculating the outstanding shares. Corporate actions of relevance identified by ISIN include: + +- Company changes its name: The replacement shares receive a new ISIN. +- A company changes the country in which it is headquartered: A new ISIN is required only if the company replaces an old security with a new one. +- Merger and acquisition: Old ISINs for stock become inactive and are replaced by securities with a new ISIN. +- New stock issued or changed to stock par value: A new ISIN is only needed if new stock has different rights or is meant to replace old stock.