Package 'pacta.data.preparation'

Title: Prepare Data for PACTA for Investors
Description: This package provides tools to prepare input datasets to be run in the PACTA_analysis tool.
Authors: CJ Yetman [aut, cre, ctr] , Jackson Hoffart [aut, ctr] , Jacob Kastl [aut, ctr], Alex Axthelm [aut, ctr] , RMI [cph, fnd]
Maintainer: CJ Yetman <[email protected]>
License: MIT + file LICENSE
Version: 0.1.0.9003
Built: 2024-10-30 12:23:50 UTC
Source: https://github.com/rmi-pacta/pacta.data.preparation

Help Index

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").

Usage

calc_shares_all_classes(
  shares_outstanding,
  asset_type_id,
  include_types = c("CP", "EQ", "PF")
)

Arguments

shares_outstanding

(numeric vector) contains the number of shares outstanding
asset_type_id

(character vector) contains an the asset type identifier
include_types

(character vector) defines which of the asset_type_id values should be included in the sum

Value

a numeric value containing the number of shares across all equity asset classes

Combine ABCD and scenario data into the ⁠[equity/bonds]_abcd_scenario.rds⁠ format that is used by portfolio.analysis

Description

Combine ABCD and scenario data into the ⁠[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

abcd_data

A tibble containing the ABCD data
scenario_data

A tibble containing the scenario data
reference_year

A single numeric specifying the market share target reference year
relevant_years

A numeric vector containing all relevant years to be calculated
tech_exclude

A character vector containing the technologies to be excluded
scenario_geographies_list

A character vector containing the scenario geographies to be used
sector_list

A character vector containing the sectors to be included
other_sector_list

A character vector containing the sectors considered "other"
global_aggregate_scenario_sources_list

A character vector containing the scenario sources to be included in the global aggreagte
global_aggregate_sector_list

A character vector containing the sectors to be included in the global aggregate
scenario_regions

A character vector containing the scenario regions
index_regions

A character vector containing the index regions

Value

A tibble with the combined ABCD and scenario data

Determine relevant years

Description

Determine relevant years

Usage

determine_relevant_years(market_share_target_reference_year, time_horizon)

Arguments

market_share_target_reference_year

A single numeric value determining the Market Share target reference year
time_horizon

A single numeric value determining the number of forward looking years

Value

A numeric vector containg all of the relevant years

Import the data from a version of Asset Resolution's proprietary Advanced Company Indicators XLSX into a tidy data frame.

Description

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

filepath

Path to the XLSX file.
drop_nas

A logical indicating whether rows with an NA value after pivoting to long-format should be dropped (default is TRUE).
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).
as_factor

A logical indicating whether the character columns should be converted to factors(default is 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.

Import the data from a version of Asset Resolution's bespoke masterdata_*.csv files into a tidy data frame.

Description

Import the data from a version of Asset Resolution's bespoke masterdata_*.csv files into a tidy data frame.

Usage

import_ar_masterdata(filepath, drop_nas = TRUE, id_as_string = FALSE)

Arguments

filepath

Path to the CSV file.
drop_nas

A logical indicating whether rows with an NA value after pivoting to long-format should be dropped (deafult is TRUE).
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).

Value

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.

Title

Description

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

financial_data

A data frame containing financial data
factset_entity_id__ar_company_id

A data frame containing a factset_entity_id to ar_company_id look up table
factset_entity_id__security_mapped_sector

A data frame containing a factset_entity_id to security_mapped_sector look up table
ar_company_id__sectors_with_assets__debt

A data frame containing a ar_company_id to sectors_with_assets look up table for debt
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

Title

Description

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

financial_data

A data frame containing financial data
factset_entity_id__ar_company_id

A data frame containing a factset_entity_id to ar_company_id look up table
factset_entity_id__security_mapped_sector

A data frame containing a factset_entity_id to security_mapped_sector look up table
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

Prepare an ar_company_id__country_of_domicile lookup table from the entity_info data

Description

Prepare an ar_company_id__country_of_domicile lookup table from the entity_info data

Usage

prepare_ar_company_id__country_of_domicile(entity_info)

Arguments

entity_info

A data frame containing the entity info

Value

A tibble

Prepare an ar_company_id__credit_parent_ar_company_id lookup table from the entity_info data

Description

Prepare an ar_company_id__credit_parent_ar_company_id lookup table from the entity_info data

Usage

prepare_ar_company_id__credit_parent_ar_company_id(entity_info)

Arguments

entity_info

A data frame containing the entity info

Value

A tibble

Prepare an ar_company_id__sectors_with_assets__debt lookup table from the masterdata_debt_datastore data

Description

Prepare an ar_company_id__sectors_with_assets__debt lookup table from the masterdata_debt_datastore data

Usage

prepare_ar_company_id__sectors_with_assets__debt(
  masterdata_debt_datastore,
  relevant_years
)

Arguments

masterdata_debt_datastore

A data frame containing processed production data from Asset Impact's masterdata_ownership CSV
relevant_years

A numeric vector containing the relevant years of data to include

Value

A tibble

Prepare an ar_company_id__sectors_with_assets__ownership lookup table from the masterdata_ownership_datastore data

Description

Prepare an ar_company_id__sectors_with_assets__ownership lookup table from the masterdata_ownership_datastore data

Usage

prepare_ar_company_id__sectors_with_assets__ownership(
  masterdata_ownership_datastore,
  relevant_years
)

Arguments

masterdata_ownership_datastore

A data frame containing processed production data from Asset Impact's masterdata_ownership CSV
relevant_years

A numeric vector containing the relevant years of data to include

Value

A tibble

Prepare a company_id__creditor_company_id lookup table from Asset Impact's masterdata_debt data

Description

Prepare a company_id__creditor_company_id lookup table from Asset Impact's masterdata_debt data

Usage

prepare_company_id__creditor_company_id(masterdata_debt)

Arguments

masterdata_debt

A data frame containing raw production data from Asset Impact's masterdata_debt CSV

Value

A tibble

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

Description

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

Usage

prepare_entity_info(
  data,
  factset_entity_id__ar_company_id,
  factset_industry_map_bridge,
  factset_manual_pacta_sector_override
)

Arguments

data

A data frame containing the imported factset_entity_info.rds file
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
factset_industry_map_bridge

A data frame containing the imported factset_industry_map_bridge.rds
factset_manual_pacta_sector_override

A data frame containing the imported factset_manual_pacta_sector_override

Value

A tibble properly prepared to be saved as the entity_info.rds output file

Prepare a factset_entity_id__ar_company_id lookup table from Asset Impact's ar_company_id__factset_entity_id crosswalk

Description

Prepare a factset_entity_id__ar_company_id lookup table from Asset Impact's ar_company_id__factset_entity_id crosswalk

Usage

prepare_factset_entity_id__ar_company_id(ar_company_id__factset_entity_id)

Arguments

ar_company_id__factset_entity_id

A data frame containing a production data company ID to financial data entity lookup table

Value

A tibble

Prepare a factset_entity_id__credit_parent_id lookup table from entity_info

Description

Prepare a factset_entity_id__credit_parent_id lookup table from entity_info

Usage

prepare_factset_entity_id__credit_parent_id(entity_info)

Arguments

entity_info

A data frame containing the entity info

Value

A tibble

Prepare a factset_entity_id__security_mapped_sector lookup table from entity_info

Description

Prepare a factset_entity_id__security_mapped_sector lookup table from entity_info

Usage

prepare_factset_entity_id__security_mapped_sector(entity_info)

Arguments

entity_info

A data frame containing the entity info

Value

A tibble

Prepare the financial_data output data frame from the imported factset_financial_data.rds file

Description

Prepare the financial_data output data frame from the imported factset_financial_data.rds file

Usage

prepare_financial_data(data, issue_code_bridge)

Arguments

data

A data frame containing the imported factset_financial_data.rds file
issue_code_bridge

A data frame containing data that bridges from factset issue codes to one of c("Listed Equity", "Corporate Bond", "Fund", "Other")

Value

A tibble properly prepared to be saved as the financial_data.rds output file

Prepare fund data, filtering to funds with data according to a given threshold and adding a MISSINGWEIGHT holding for the difference

Description

Prepare fund data, filtering to funds with data according to a given threshold and adding a MISSINGWEIGHT holding for the difference

Usage

prepare_fund_data(fund_data, threshold = 0)

Arguments

fund_data

A data frame containing fund data
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

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

Description

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

Usage

prepare_isin_to_fund_table(isin_to_fund_table, fund_data)

Arguments

isin_to_fund_table

A data frame containing isin_to_fund_table data
fund_data

A data frame containing fund data

Value

A tibble

Prepare a iss_average_sector_emission_intensities object

Description

Prepare a 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

iss_company_emissions

A data frame containing iss_company_emissions data
factset_financial_data

A data frame containing factset_financial_data data
factset_entity_info

A data frame containing factset_entity_info data
factset_entity_financing_data

A data frame containing factset_entity_financing_data data
currencies

A data frame containing currency exchange rate data

Value

A data frame containing the prepared iss_average_sector_emission_intensities object

Prepare an iss_company_emissions object from factset_iss_emissions_data

Description

Prepare an iss_company_emissions object from factset_iss_emissions_data

Usage

prepare_iss_company_emissions(factset_iss_emissions_data)

Arguments

factset_iss_emissions_data

A data frame containing ISS emissions data

Value

A tibble

Prepare a iss_entity_emission_intensities object

Description

Prepare a 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

iss_company_emissions

A data frame containing iss_company_emissions data
factset_financial_data

A data frame containing factset_financial_data data
factset_entity_info

A data frame containing factset_entity_info data
factset_entity_financing_data

A data frame containing factset_entity_financing_data data
currencies

A data frame containing currency exchange rate data

Value

A data frame containing the prepared iss_entity_emission_intensities object

Prepare the masterdata_ownership_datastore or masterdata_debt_datastore output data frame from an import of a raw AR masterdata_* CSV

Description

Prepare the masterdata_ownership_datastore or 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

data

A dataframe containing the raw input of an AR masterdata_* CSV files
ar_company_id__country_of_domicile

A data frame with two columns mapping ar_company_id to country_of_domicile
pacta_financial_timestamp

A single element character vector specifying the timestamp in the PACTA format, e.g. "2021Q4"
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 masterdata_ownership_datastore.rds or masterdata_debt_datastore.rds output file

Prepare the masterdata_debt_datastore object from a raw masterdata_debt CSV

Description

Prepare the 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

masterdata_debt_raw

A data frame containing the raw data from a masterdata_debt CSV
ar_company_id__country_of_domicile

A data frame containing an ar_company_id to country_of_domicile lookup
ar_company_id__credit_parent_ar_company_id

A data frame containing an ar_company_id to credit_parent_ar_company_id lookup
pacta_financial_timestamp

A single character vector containing the PACTA financial timestamp, e.g. ⁠2023Q4⁠
zero_emission_factor_techs

A character vector containing the zero emission factor technologies

Value

A data frame containing the prepared masterdata_debt_datastore

Prepare a total_fund_list object from fund_data

Description

Prepare a total_fund_list object from fund_data

Usage

prepare_total_fund_list(fund_data)

Arguments

fund_data

A data frame containing fund data

Value

A tibble

Convert a PACTA style quarter string to a FactSet style date string for the last day of that quarter

Description

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

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"

Convert a PACTA style quarter string to an IMF style quarter string

Description

Convert a PACTA style quarter string to an IMF style quarter string

Usage

quarter_to_imf_timestamp(quarter)

Arguments

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"

Standardize asset type names

Description

Standardize asset type names

Usage

standardize_asset_type_names(factset_issue_code_bridge)

Arguments

factset_issue_code_bridge

A data frame containing the FactSet issue code bridge

Value

A data frame containing a FactSet issue_type_code to asset_type lookup

Write a manifest.json file to the specified path including critical information about the files and parameters used to prepare the data

Description

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

path

A single string specifying a filepath to save the JSON file to
parameters

A list containing all parameters used to create the data
input_files

A vector with filepaths of input files used to create the output data.
output_files

A vector with filepaths of output files created.

Value

Called for the side-effect of writing a JSON file to disk