Help for package pacta.loanbook

Title:

Easily Install and Load PACTA for Banks Packages

Version:

0.1.1

Description:

PACTA (Paris Agreement Capital Transition Assessment) for Banks is a tool that allows banks to calculate the climate alignment of their corporate lending portfolios. This package is designed to make it easy to install and load multiple PACTA for Banks packages in a single step. It also provides thorough documentation - the PACTA for Banks cookbook at https://rmi-pacta.github.io/pacta.loanbook/articles/cookbook_overview.html - on how to run a PACTA for Banks analysis. This covers prerequisites for the analysis, the separate steps of running the analysis, the interpretation of PACTA for Banks results, and advanced use cases.

License:

MIT + file LICENSE

URL:

https://rmi-pacta.github.io/pacta.loanbook/, https://github.com/RMI-PACTA/pacta.loanbook

BugReports:

https://github.com/RMI-PACTA/pacta.loanbook/issues

Depends:

R (≥ 4.1)

Imports:

cli, dplyr, ggrepel, magrittr, purrr, r2dii.analysis (≥ 0.5.1), r2dii.data (≥ 0.6.0), r2dii.match (≥ 0.4.1), r2dii.plot (≥ 0.5.2), rlang, rstudioapi, scales, tibble, tidyselect

Suggests:

covr, DiagrammeR, ggplot2, gt (≥ 0.11.0), knitr (≥ 1.42), readr, readxl, rmarkdown (≥ 2.19), roxygen2 (≥ 4.1.0), spelling, testthat (≥ 3.2.2), tidyr, writexl

Encoding:

UTF-8

Language:

en-US

RoxygenNote:

7.3.2

Config/Needs/website:

rmi-pacta/pacta.pkgdown.rmitemplate, rmarkdown

Config/testthat/edition:

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2025-06-23 12:01:00 UTC; cjrmi

Author:

Jacob Kastl

[aut, cre, ctr], Jackson Hoffart

[aut, ctr], CJ Yetman

[aut, ctr], RMI

[cph, fnd]

Maintainer:

Jacob Kastl <jacob.kastl@gmail.com>

Repository:

CRAN

Date/Publication:

2025-06-24 09:50:02 UTC

pacta.loanbook: Easily Install and Load PACTA for Banks Packages

Description

Author(s)

Maintainer: Jacob Kastl jacob.kastl@gmail.com (ORCID) [contractor]

Authors:

Jackson Hoffart jackson.hoffart@gmail.com (ORCID) [contractor]
CJ Yetman cj@cjyetman.com (ORCID) [contractor]

Other contributors:

RMI PACTA4banks@rmi.org (03anfar33) [copyright holder, funder]

An asset-based company dataset for demonstration

Description

Fake data about physical assets (e.g. wind turbine power plant capacities), aggregated to company-level. These data are used to assess the climate alignment of financial portfolios. It imitates data from market-intelligence databases.

Demo datasets are synthetic because most financial data is strictly private; they help to demonstrate and test the implementation in R of 'PACTA' (https://www.transitionmonitor.com/).

Usage

abcd_demo

Format

An object of class tbl_df (inherits from tbl, data.frame) with 4972 rows and 12 columns.

Definitions

company_id (character): The id of the company owning the asset created by the data provider., * emission_factor (double): Company level emission factor of the technology., * emission_factor_unit (character): The units that the emission factor is measured in., * is_ultimate_owner (logical): Flag if company is the ultimate parent in our database., * lei (character): The legal entity identifier of the company owning the asset., * name_company (character): The name of the company owning the asset., * plant_location (character): Country where asset is located., * production (double): Company level production of the technology., * production_unit (character): The units that production is measured in., * sector (character): Sector to which the asset belongs., * technology (character): Technology implemented by the asset., * year (integer): Year at which the production value is predicted.

Examples

head(abcd_demo)

A prepared CO₂ intensity climate scenario dataset for demonstration

Description

Fake CO₂ intensity climate scenario dataset, prepared for the software PACTA (Paris Agreement Capital Transition Assessment). It imitates climate scenario data (e.g. from the International Energy Agency (IEA)) including the change through time in production across industrial sectors.

Demo datasets are synthetic because most financial data is strictly private; they help to demonstrate and test the implementation in R of 'PACTA' (https://www.transitionmonitor.com/).

Usage

co2_intensity_scenario_demo

Format

An object of class tbl_df (inherits from tbl, data.frame) with 22 rows and 7 columns.

Definitions

emission_factor (double): The target sector level emissions factor that the scenario prescribes., * emission_factor_unit (character): The units that the emissions factor is measured in., * region (character): The region to which the pathway is relevant., * scenario (character): The name of the scenario., * scenario_source (character): The source publication from which the scenario was taken., * sector (character): The sector to which the scenario prescribes a pathway., * year (integer): The year at which the pathway value is prescribed.

Examples

head(co2_intensity_scenario_demo)

Crucial `loanbook` columns for `match_name()`

Description

This is a helper to select the minimum loanbook columns you need to run match_name(). Using more columns may use too much time and memory.

Usage

crucial_lbk()

Value

A character vector.

Examples

crucial_lbk()

Data Dictionary

Description

A table of column names and descriptions of data frames used or exported by the functions in this package.

Usage

data_dictionary

Format

`data_dictionary`

dataset: Name of the dataset
column: Name of the column
typeof: Type of the column
definition: Definition of the column

Examples

data_dictionary

Dataset to bridge (translate) common sector-classification codes

Description

This dataset serves as a translation key between common sector-classification systems and sectors relevant to the 'PACTA' tool (https://www.transitionmonitor.com/).

Usage

gics_classification

Format

An object of class tbl_df (inherits from tbl, data.frame) with 282 rows and 5 columns.

Definitions

borderline (logical): Flag indicating if PACTA sector and classification code are a borderline match. The value TRUE indicates that the match is uncertain between the PACTA sector and the classification. The value FALSE indicates that the match is certainly perfect or the classification is certainly out of PACTA's scope.., * code (character): Original GICS code., * description (character): Original GICS description., * sector (character): Associated PACTA sector., * version (character): Column identifying to which GICS version the code belongs.

Details

Classification datasets help to standardize sector classification codes from the wild to a relevant subset including 'power', 'oil and gas', 'coal', 'automotive', 'aviation', 'concrete', 'steel', and 'shipping'.

Examples

head(gics_classification)

Determine if a technology is increasing or decreasing

Description

This dataset provides a simple lookup table to determine if a technology is meant to increase or decrease to align with a scenario that predicts a less than 2 degree temperature rise.

Usage

increasing_or_decreasing

Format

An object of class tbl_df (inherits from tbl, data.frame) with 20 rows and 3 columns.

Definitions

increasing_or_decreasing (character): If the technology is increasing or decreasing, as defined by the Paris-aligned IEA scenarios., * sector (character): The sector to which the technology belongs., * technology (character): The technology sub-category within the sector.

Examples

head(increasing_or_decreasing)

Dataset to bridge (translate) common sector-classification codes

Description

This dataset serves as a translation key between common sector-classification systems and sectors relevant to the 'PACTA' tool (https://www.transitionmonitor.com/).

Usage

isic_classification

Format

An object of class tbl_df (inherits from tbl, data.frame) with 830 rows and 6 columns.

Definitions

borderline (logical): Flag indicating if PACTA sector and classification code are a borderline match. The value TRUE indicates that the match is uncertain between the PACTA sector and the classification. The value FALSE indicates that the match is certainly perfect or the classification is certainly out of PACTA's scope.., * code (character): ISIC Rev 5 code with top-level letter prepended., * description (character): Original ISIC Rev 5 title., * original_code (character): Original ISIC Rev 5 code., * revision (character): Column identifying to which ISIC revision the code belongs.., * sector (character): Associated PACTA sector.

Details

Examples

head(isic_classification)

Countries and codes

Description

This dataset maps countries to codes.

For information about the ISO standard for country codes see https://www.iso.org/iso-3166-country-codes.html.

Usage

iso_codes

Format

An object of class tbl_df (inherits from tbl, data.frame) with 286 rows and 2 columns.

Definitions

country (character): Country name., * country_iso (character): Corresponding ISO code.

Examples

head(iso_codes)

A loanbook dataset for demonstration

Description

Fake financial portfolio.

Demo datasets are synthetic because most financial data is strictly private; they help to demonstrate and test the implementation in R of 'PACTA' (https://www.transitionmonitor.com/).

Usage

loanbook_demo

Format

An object of class tbl_df (inherits from tbl, data.frame) with 283 rows and 13 columns.

Definitions

id_direct_loantaker (character): Borrower identifier unique to each borrower/sector combination in loanbook., * id_loan (character): Unique loan identifier., * id_ultimate_parent (character): Ultimate parent identifier unique to each ultimate parent/sector combination., * isin_direct_loantaker (logical): Optional input: providing the isin identifier of the direct loan taker to improve the matching coverage., * lei_direct_loantaker (logical): Optional input: providing the lei (legal entity identifier) of the direct loan taker to improve the matching coverage., * loan_size_credit_limit (double): Total credit limit or exposure at default., * loan_size_credit_limit_currency (character): Currency corresponding to credit limit., * loan_size_outstanding (double): Amount drawn by borrower from total credit limit., * loan_size_outstanding_currency (character): Currency corresponding to outstandings., * name_direct_loantaker (character): Name of the company directly taking the loan., * name_ultimate_parent (character): Name of the ultimate parent company to which the borrower belongs. Can be the same as borrower., * sector_classification_direct_loantaker (double): Sector classification code of the direct loantaker., * sector_classification_system (character): Name of the sector classification standard being used.

Examples

head(loanbook_demo)

An example of a `market_share_demo`-like dataset

Description

Dataset imitating the output of r2dii.analysis::target_market_share().

Usage

market_share_demo

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 802 rows and 10 columns.

Examples

market_share_demo

Match a loanbook to asset-based company data (abcd) by the `⁠name_*⁠` columns

Description

match_name() scores the match between names in a loanbook dataset (columns can be name_direct_loantaker, ⁠name_intermediate_parent*⁠ and name_ultimate_parent) with names in an asset-based company data (column name_company). The raw names are first internally transformed, and aliases are assigned. The similarity between aliases in each of the loanbook and abcd is scored using stringdist::stringsim().

Usage

match_name(
  loanbook,
  abcd,
  by_sector = TRUE,
  min_score = 0.8,
  method = "jw",
  p = 0.1,
  overwrite = NULL,
  join_id = NULL,
  sector_classification = default_sector_classification(),
  ...
)

Arguments

loanbook, abcd

data frames structured like r2dii.data::loanbook_demo and r2dii.data::abcd_demo.

by_sector

Should names only be compared if companies belong to the same sector?

min_score

A number between 0-1, to set the minimum score threshold. A score of 1 is a perfect match.

method

Method for distance calculation. One of c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex"). See stringdist::stringdist-metrics.

p

Prefix factor for Jaro-Winkler distance. The valid range for p is 0 <= p <= 0.25. If p=0 (default), the Jaro-distance is returned. Applies only to method='jw'.

overwrite

A data frame used to overwrite the sector and/or name columns of a particular direct loantaker or ultimate parent. To overwrite only sector, the value in the name column should be NA and vice-versa. This file can be used to manually match loanbook companies to abcd.

join_id

A join specification passed to dplyr::inner_join(). If a character string, it assumes identical join columns between loanbook and abcd. If a named character vector, it uses the name as the join column of loanbook and the value as the join column of abcd.

sector_classification

A data frame containing sector classifications in the same format as r2dii.data::sector_classifications. The default value is r2dii.data::sector_classifications.

...

Arguments passed on to stringdist::stringsim().

Value

A data frame with the same groups (if any) and columns as loanbook, and the additional columns:

id_2dii - an id used internally by match_name() to distinguish companies
level - the level of granularity that the loan was matched at (e.g direct_loantaker or ultimate_parent)
sector - the sector of the loanbook company
sector_abcd - the sector of the abcd company
name - the name of the loanbook company
name_abcd - the name of the abcd company
score - the score of the match (manually set this to 1 prior to calling prioritize() to validate the match)
source - determines the source of the match. (equal to loanbook unless the match is from overwrite

The returned rows depend on the argument min_value and the result of the column score for each loan: * If any row has score equal to 1, match_name() returns all rows where score equals 1, dropping all other rows. * If no row has score equal to 1,match_name() returns all rows where score is equal to or greater than min_score. * If there is no match the output is a 0-row tibble with the expected column names – for type stability.

Assigning aliases

The transformation process used to compare names between loanbook and abcd datasets applies best practices commonly used in name matching algorithms:

Remove special characters.
Replace language specific characters.
Abbreviate certain names to reduce their importance in the matching.
Spell out numbers to increase their importance.

Handling grouped data

This function ignores but preserves existing groups.

Examples

library(r2dii.data)
library(tibble)

# Small data for examples
loanbook <- head(loanbook_demo, 50)
abcd <- head(abcd_demo, 50)

match_name(loanbook, abcd)

match_name(loanbook, abcd, min_score = 0.9)

# match on LEI
loanbook <- tibble(
  sector_classification_system = "NACE",
  sector_classification_direct_loantaker = "D35.11",
  id_ultimate_parent = "UP15",
  name_ultimate_parent = "Won't fuzzy match",
  id_direct_loantaker = "C294",
  name_direct_loantaker = "Won't fuzzy match",
  lei_direct_loantaker = "LEI123"
)

abcd <- tibble(
  name_company = "alpine knits india pvt. limited",
  sector = "power",
  lei = "LEI123"
)

match_name(loanbook, abcd, join_id = c(lei_direct_loantaker = "lei"))

# Use your own `sector_classifications`
your_classifications <- tibble(
  sector = "power",
  borderline = FALSE,
  code = "D35.11",
  code_system = "XYZ"
)

loanbook <- tibble(
  sector_classification_system = "XYZ",
  sector_classification_direct_loantaker = "D35.11",
  id_ultimate_parent = "UP15",
  name_ultimate_parent = "Alpine Knits India Pvt. Limited",
  id_direct_loantaker = "C294",
  name_direct_loantaker = "Yuamen Xinneng Thermal Power Co Ltd"
)

abcd <- tibble(
  name_company = "alpine knits india pvt. limited",
  sector = "power"
)

match_name(loanbook, abcd, sector_classification = your_classifications)

Dataset to bridge (translate) common sector-classification codes

Description

This dataset serves as a translation key between common sector-classification systems and sectors relevant to the 'PACTA' tool (https://www.transitionmonitor.com/).

Usage

nace_classification

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1047 rows and 6 columns.

Definitions

borderline (logical): Flag indicating if PACTA sector and classification code are a borderline match. The value TRUE indicates that the match is uncertain between the PACTA sector and the classification. The value FALSE indicates that the match is certainly perfect or the classification is certainly out of PACTA's scope., * code (character): NACE version 2.1 code with top-level letter prepended., * description (character): Original NACE version 2.1 description., * original_code (character): Original NACE version 2.1 code., * sector (character): Associated PACTA sector., * version (character): Column identifying to which NACE version the code belongs.

Details

Examples

head(nace_classification)

Dataset to bridge (translate) common sector-classification codes

Description

This dataset serves as a translation key between common sector-classification systems and sectors relevant to the 'PACTA' tool (https://www.transitionmonitor.com/).

Usage

naics_classification

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2125 rows and 5 columns.

Definitions

borderline (logical): Flag indicating if PACTA sector and classification code are a borderline match. The value TRUE indicates that the match is uncertain between the PACTA sector and the classification. The value FALSE indicates that the match is certainly perfect or the classification is certainly out of PACTA's scope.., * code (character): Six-digit NAICS code., * description (character): Original NAICS sector title., * sector (character): Associated PACTA sector., * version (character): Column identifying which year the classification was published in..

Details

Examples

head(naics_classification)

A demonstration dataset used to overwrite specific entity names or sectors

Description

Fake dataset used to manually link loanbook entities to mismatched asset level entities.

Demo datasets are synthetic because most financial data is strictly private; they help to demonstrate and test the implementation in R of 'PACTA' (https://www.transitionmonitor.com/).

Usage

overwrite_demo

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2 rows and 5 columns.

Definitions

id_2dii (character): IDs of the entities to overwrite., * level (character): Which level should be overwritten (e.g. direct_loantaker or ultimate_parent)., * name (character): Overwrite name (if only overwriting sector, type NA)., * sector (character): Overwrite sector (if only overwriting name, type NA)., * source (character): What is the source of this information (leave as "manual" for now, may remove this flag later).

Examples

head(overwrite_demo)

Conflicts between the `{pacta.loanbook}` and other packages

Description

This function lists all the conflicts between packages in the {pacta.loanbook} and other packages that you have loaded.

Usage

pacta_loanbook_conflicts(only = NULL)

Arguments

only

Set this to a character vector to restrict to conflicts only with these packages.

Details

There are four conflicts that are deliberately ignored: intersect, union, setequal, and setdiff from dplyr. These functions make the base equivalents generic, so shouldn't negatively affect any existing code.

Value

a pacta_loanbook_conflicts classed list which will print a list of conflicts to the console in interactive sessions, or NULL if no conflicts are found.

Examples

pacta_loanbook_conflicts()

List all `{pacta.loanbook}` dependencies

Description

List all {pacta.loanbook} dependencies

Usage

pacta_loanbook_deps(recursive = FALSE, repos = getOption("repos"))

Arguments

recursive

If TRUE, will also list all dependencies of {pacta.loanbook} packages.

repos

The repositories to use to check for updates. Defaults to getOption("repos").

Value

a tibble containing the local and CRAN versions of dependent packages.

Examples

pacta_loanbook_deps(repos = "https://cran.r-project.org")

The `{pacta.loanbook}` logo, using ASCII or Unicode characters

Description

Use cli::ansi_strip() to get rid of the colors.

Usage

pacta_loanbook_logo(unicode = cli::is_utf8_output())

Arguments

unicode

Whether to use Unicode symbols. Default is TRUE on UTF-8 platforms.

Value

a pacta_loanbook_logo classed cli_ansi_string which will print the PACTA logo in the console in interactive sessions.

Examples

pacta_loanbook_logo()

List all packages in `{pacta.loanbook}`

Description

List all packages in {pacta.loanbook}

Usage

pacta_loanbook_packages(include_self = TRUE)

Arguments

include_self

Include {pacta.loanbook} in the list?

Value

a character vector containing the names of packages imported by {pacta.loanbook}.

Examples

pacta_loanbook_packages()

Get a situation report on `{pacta.loanbook}`

Description

This function gives a quick overview of the versions of R and RStudio as well as the {pacta.loanbook} package. It's primarily designed to help you get a quick idea of what's going on when you're helping someone else debug a problem.

Usage

pacta_loanbook_sitrep(repos = getOption("repos"))

Arguments

repos

The repositories to use to check for updates. Defaults to getOption("repos").

Value

returns NULL invisibly. The function is called for its side effect of printing a situation report of {pacta.loanbook} and its core packages.

Examples

pacta_loanbook_sitrep(repos = "https://cran.r-project.org")

Update `{pacta.loanbook}` packages

Description

This will check to see if all {pacta.loanbook} packages (and optionally, their dependencies) are up-to-date, and will install after an interactive confirmation.

Usage

pacta_loanbook_update(recursive = FALSE, repos = getOption("repos"))

Arguments

recursive

If TRUE, will also list all dependencies of {pacta.loanbook} packages.

repos

The repositories to use to check for updates. Defaults to getOption("repos").

Value

returns NULL invisibly. The function is called for its side effect of printing the status of locally installed, relevant packages.

Examples

pacta_loanbook_update(repos = "https://cran.r-project.org")

Colour datasets

Description

All datasets have at least two columns:

label: Text label of the colour.
hex: Hex code of the colour.

Usage

palette_colours

Format

An object of class tbl_df (inherits from tbl, data.frame) with 9 rows and 2 columns.

Details

In scenario_colours, colours are ordered from red to green to be used in trajectory charts.

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

Create an emission intensity plot

Description

Create an emission intensity plot

Usage

plot_emission_intensity(data)

Arguments

data

A data frame like the output of prep_emission_intensity().

Value

An object of class "ggplot".

Examples

# plot with `qplot_emission_intensity()` parameters
data <- subset(sda_demo, sector == "cement" & region == "global")
data <- prep_emission_intensity(data, span_5yr = TRUE, convert_label = to_title)

plot_emission_intensity(data)

Create a techmix plot

Description

Create a techmix plot

Usage

plot_techmix(data)

Arguments

data

A data frame like the output of prep_techmix().

Value

An object of class "ggplot".

Examples

# plot with `qplot_techmix()` parameters
data <- subset(
  market_share_demo,
  scenario_source == "demo_2020" &
    sector == "power" &
    region == "global" &
    metric %in% c("projected", "corporate_economy", "target_sds")
)
data <- prep_techmix(
  data,
  span_5yr = TRUE,
  convert_label = recode_metric_techmix,
  convert_tech_label = spell_out_technology
)

plot_techmix(data)

Create a trajectory plot

Description

Create a trajectory plot

Usage

plot_trajectory(data, center_y = FALSE, perc_y_scale = FALSE)

Arguments

data

A data frame like the outputs of prep_trajectory().

(Optional) If present, the column label is used for data labels.

center_y

Logical. Use TRUE to center the y-axis around start value (the default behavior of qplot_trajectory()), or use FALSE to not center.

perc_y_scale

Logical. FALSE defaults to using no label conversion. Use TRUE to convert labels on y-axis to percentage using scales::percent (the default behavior of qplot_trajectory()).

Value

An object of class "ggplot".

Examples

# plot with `qplot_trajectory()` parameters
data <- subset(
  market_share_demo,
  sector == "power" &
    technology == "renewablescap" &
    region == "global" &
    scenario_source == "demo_2020"
)
data <- prep_trajectory(data)

plot_trajectory(
  data,
  center_y = TRUE,
  perc_y_scale = TRUE
)

Prepare data for a emission intensity plot

Description

Prepare data for a emission intensity plot

Usage

prep_emission_intensity(data, convert_label = identity, span_5yr = FALSE)

Arguments

data

A data frame. Requirements:

The structure must be like sda_demo.
The column sector must have a single value (e.g. "cement").
(Optional) If present, the column label is used for data labels.

convert_label

A symbol. The unquoted name of a function to apply to y-axis labels. For example:

To convert labels to uppercase use convert_label = toupper.
To get the default behavior of qplot_emission_intensity use convert_label = to_title.

span_5yr

Logical. Use TRUE to restrict the time span to 5 years from the start year (the default behavior of qplot_emission_intensity()), or use FALSE to impose no restriction.

Value

A data-frame ready to be plotted using plot_emission_intensity().

Examples

# `data` must meet documented "Requirements"
data <- subset(sda_demo, sector == "cement" & region == "global")
prep_emission_intensity(data)

Prepare data for plotting technology mix

Description

Prepare data for plotting technology mix

Usage

prep_techmix(
  data,
  convert_label = identity,
  span_5yr = FALSE,
  convert_tech_label = identity
)

Arguments

data

A data frame. Requirements:

The structure must be like market_share_demo.
The following columns must have a single value: sector, region, scenario_source.
The column metric must have a portfolio (e.g. "projected"), a benchmark (e.g. "corporate_economy"), and a single scenario (e.g. "target_sds").
(Optional) If present, the column label is used for data labels.
(Optional) If present, the column label_tech is used for technology labels.

convert_label

A symbol. The unquoted name of a function to apply to y-axis labels. For example:

To convert labels to uppercase use convert_label = toupper.
To get the default behavior of qplot_techmix use convert_label = recode_metric_techmix.

span_5yr

Logical. Use TRUE to restrict the time span to 5 years from the start year (the default behavior of qplot_techmix()), or use FALSE to impose no restriction.

convert_tech_label

A symbol. The unquoted name of a function to apply to technology legend labels. For example, to convert labels to uppercase use convert_tech_label = toupper. To get the default behavior of qplot_techmix() use convert_tech_label = spell_out_technology.

Value

A data-frame ready to be plotted using plot_techmix().

Examples

# `data` must meet documented "Requirements"
data <- subset(
  market_share_demo,
  scenario_source == "demo_2020" &
    sector == "power" &
    region == "global" &
    metric %in% c("projected", "corporate_economy", "target_sds")
)

prep_techmix(data)

Prepare data for a trajectory plot

Description

Prepare data for a trajectory plot

Usage

prep_trajectory(
  data,
  convert_label = identity,
  span_5yr = FALSE,
  value_col = "percentage_of_initial_production_by_scope"
)

Arguments

data

A data frame. Requirements:

The structure must be like market_share_demo.
The following columns must have a single value: sector, technology, region, scenario_source.
(Optional) If present, the column label is used for data labels.

convert_label

A symbol. The unquoted name of a function to apply to y-axis labels. For example:

To convert labels to uppercase use convert_label = toupper.
To get the default behavior of qplot_trajectory use convert_label = recode_metric_trajectory.

span_5yr

Logical. Use TRUE to restrict the time span to 5 years from the start year (the default behavior of qplot_trajectory()), or use FALSE to impose no restriction.

value_col

Character. Name of the column to be used as a value to be plotted.

Value

A data-frame ready to be plotted using plot_trajectory().

Examples

# `data` must meet documented "Requirements"
data <- subset(
  market_share_demo,
  sector == "power" &
    technology == "renewablescap" &
    region == "global" &
    scenario_source == "demo_2020"
)

prep_trajectory(data)

Pick rows where `score` is 1 and `level` per loan is of highest `priority`

Description

When multiple perfect matches are found per loan (e.g. a match at direct_loantaker level and ultimate_parent level), we must prioritize the desired match. By default, the highest priority is the most granular match (i.e. direct_loantaker).

Usage

prioritize(data, priority = NULL)

Arguments

data

A data frame like the validated output of match_name(). See Details on how to validate data.

priority

One of:

NULL: defaults to the default level priority as returned by prioritize_level().
A character vector giving a custom priority.
A function to apply to the output of prioritize_level(), e.g. rev.
A quosure-style lambda function, e.g. ~ rev(.x).

Details

How to validate data Write the output of match_name() into a .csv file with:

# Writting to current working directory
matched %>%
  readr::write_csv("matched.csv")

Compare, edit, and save the data manually:

Open matched.csv with any spreadsheet editor (Excel, Google Sheets, etc.).
Compare the columns name and name_abcd manually to determine if the match is valid. Other information can be used in conjunction with just the names to ensure the two entities match (sector, internal information on the company structure, etc.)
Edit the data:
- If you are happy with the match, set the score value to 1.
- Otherwise set or leave the score value to anything other than 1.
Save the edited file as, say, valid_matches.csv.

Re-read the edited file (validated) with:

# Reading from current working directory
valid_matches <- readr::read_csv("valid_matches.csv")

Value

A data frame with a single row per loan, where score is 1 and priority level is highest.

Handling grouped data

This function ignores but preserves existing groups.

Examples

library(dplyr)

# styler: off
matched <- tribble(
  ~sector, ~sector_abcd,  ~score, ~id_loan,                ~level,
   "coal",      "coal",       1,     "aa",     "ultimate_parent",
   "coal",      "coal",       1,     "aa",    "direct_loantaker",
   "coal",      "coal",       1,     "bb", "intermediate_parent",
   "coal",      "coal",       1,     "bb",     "ultimate_parent",
)
# styler: on

prioritize_level(matched)

# Using default priority
prioritize(matched)

# Using the reverse of the default priority
prioritize(matched, priority = rev)

# Same
prioritize(matched, priority = ~ rev(.x))

# Using a custom priority
bad_idea <- c("intermediate_parent", "ultimate_parent", "direct_loantaker")

prioritize(matched, priority = bad_idea)

Arrange unique `level` values in default order of `priority`

Description

Arrange unique level values in default order of priority

Usage

prioritize_level(data)

Arguments

data

A data frame, commonly the output of match_name().

Value

A character vector of the default level priority per loan.

Examples

matched <- tibble::tibble(
  level = c(
    "intermediate_parent_1",
    "direct_loantaker",
    "direct_loantaker",
    "direct_loantaker",
    "ultimate_parent",
    "intermediate_parent_2"
  )
)
prioritize_level(matched)

Dataset to bridge (translate) common sector-classification codes

Description

This dataset serves as a translation key between common sector-classification systems and sectors relevant to the 'PACTA' tool (https://www.transitionmonitor.com/).

Usage

psic_classification

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1271 rows and 5 columns.

Definitions

borderline (logical): Flag indicating if PACTA sector and classification code are a borderline match. The value TRUE indicates that the match is uncertain between the PACTA sector and the classification. The value FALSE indicates that the match is certainly perfect or the classification is certainly out of PACTA's scope.., * code (character): Formatted PSIC classification code., * description (character): Original PSIC classification sector name., * sector (character): Associated PACTA sector., * version (character): Column identifying which year the classification was published in..

Details

Examples

head(psic_classification)

Create a quick emission intensity plot

Description

Compared to plot_emission_intensity() this function:

is restricted to plotting future as 5 years from the start year,
outputs formatted labels, based on emission metric column,
outputs a title,
outputs formatted axis labels.

Usage

qplot_emission_intensity(data)

Arguments

data

A data frame like the output of prep_emission_intensity().

Value

An object of class "ggplot".

Examples

# `data` must meet documented "Requirements"
data <- subset(sda_demo, sector == "cement" & region == "global")

qplot_emission_intensity(data)

Create a quick techmix plot

Description

Compared to plot_techmix() this function:

is restricted to plotting future as 5 years from the start year,
outputs pretty bar labels, based on metric column,
outputs pretty legend labels, based on technology column,
outputs a title.

Usage

qplot_techmix(data)

Arguments

data

A data frame like the output of prep_techmix().

Value

An object of class "ggplot".

Examples

# `data` must meet documented "Requirements"
data <- subset(
  market_share_demo,
  sector == "power" &
    region == "global" &
    scenario_source == "demo_2020" &
    metric %in% c("projected", "corporate_economy", "target_sds")
)

qplot_techmix(data)

Create a quick trajectory plot

Description

Compared to plot_trajectory() this function:

is restricted to plotting only 5 years from the start year,
outputs pretty legend labels, based on the column holding metrics,
outputs a title,
outputs a subtitle,
outputs informative axis labels in sentence case.

Usage

qplot_trajectory(data)

Arguments

data

A data frame like the outputs of prep_trajectory().

(Optional) If present, the column label is used for data labels.

Value

An object of class "ggplot".

Examples

# `data` must meet documented "Requirements"
data <- subset(
  market_share_demo,
  sector == "power" &
    technology == "renewablescap" &
    region == "global" &
    scenario_source == "demo_2020"
)

qplot_trajectory(data)

Replicate labels produced with `⁠qplot_*()⁠` functions

Description

to_title() converts labels like qplot_emission_intensity().
recode_metric_trajectory() converts labels like qplot_trajectory().
recode_metric_techmix() converts labels like qplot_techmix().
spell_out_technology() converts technology labels like qplot_techmix().

Usage

recode_metric_techmix(x)

Arguments

x

A character vector.

Value

A character vector.

Examples

to_title(c("a.string", "another_STRING"))

metric <- c("projected", "corporate_economy", "target_xyz", "else")
recode_metric_trajectory(metric)

recode_metric_techmix(metric)

spell_out_technology(c("gas", "ice", "coalcap", "hdv"))

Replicate labels produced with `⁠qplot_*()⁠` functions

Description

to_title() converts labels like qplot_emission_intensity().
recode_metric_trajectory() converts labels like qplot_trajectory().
recode_metric_techmix() converts labels like qplot_techmix().
spell_out_technology() converts technology labels like qplot_techmix().

Usage

recode_metric_trajectory(x)

Arguments

x

A character vector.

Value

A character vector.

Examples

to_title(c("a.string", "another_STRING"))

metric <- c("projected", "corporate_economy", "target_xyz", "else")
recode_metric_trajectory(metric)

recode_metric_techmix(metric)

spell_out_technology(c("gas", "ice", "coalcap", "hdv"))

A dataset outlining various region definitions

Description

This dataset maps codes representing countries to regions.

For information about the ISO standard for country codes see https://www.iso.org/iso-3166-country-codes.html.

Usage

region_isos

Format

An object of class tbl_df (inherits from tbl, data.frame) with 9262 rows and 3 columns.

Definitions

isos (character): Countries in region, defined by iso code., * region (character): Benchmark region name., * source (character): Source publication from which the regions are defined.

Examples

head(region_isos)

A dataset outlining various region definitions

Description

This dataset maps codes representing countries to regions. It is similar to but smaller than region_isos.

Demo datasets are synthetic because most financial data is strictly private; they help to demonstrate and test the implementation in R of 'PACTA' (https://www.transitionmonitor.com/).

For information about the ISO standard for country codes see https://www.iso.org/iso-3166-country-codes.html.

Usage

region_isos_demo

Format

An object of class tbl_df (inherits from tbl, data.frame) with 358 rows and 3 columns.

Definitions

isos (character): Countries in region, defined by iso code., * region (character): Benchmark region name., * source (character): Source publication from which the regions are defined.

Examples

region_isos_demo

Custom PACTA colour and fill scales

Description

A custom discrete colour and fill scales with colours from the PACTA palette.

Usage

scale_colour_r2dii(colour_labels = NULL, ...)

Arguments

colour_labels

A character vector. Specifies colour labels to use and their order. Run unique(r2dii.plot:::palette_colours$label) to see available colours. Similar to value parameter in ggplot2::scale_colour_manual().

...

Other parameters passed on to ggplot2::discrete_scale().

Value

An object of class "ScaleDiscrete".

Examples

library(ggplot2, warn.conflicts = FALSE)

ggplot(mpg) +
  geom_point(aes(displ, hwy, color = class)) +
  scale_colour_r2dii()

ggplot(mpg) +
  geom_histogram(aes(cyl, fill = class), position = "dodge", bins = 5) +
  scale_fill_r2dii()

Custom PACTA sector colour and fill scales

Description

A custom discrete colour and fill scales with colours from the PACTA sector palette.

Usage

scale_colour_r2dii_sector(sectors = NULL, ...)

Arguments

sectors

A character vector. Specifies sector colours to use and their order. Run unique(r2dii.plot:::sector_colours$label) to see available labels. Similar to value parameter in ggplot2::scale_colour_manual().

...

Other parameters passed on to ggplot2::discrete_scale().

Value

An object of class "ScaleDiscrete".

Examples

library(ggplot2, warn.conflicts = FALSE)

ggplot(mpg) +
  geom_point(aes(displ, hwy, color = class)) +
  scale_colour_r2dii_sector()

ggplot(mpg) +
  geom_histogram(aes(cyl, fill = class), position = "dodge", bins = 5) +
  scale_fill_r2dii_sector()

Custom PACTA technology colour and fill scales

Description

A custom discrete colour and fill scales with colours from the PACTA technology palette.

Usage

scale_colour_r2dii_tech(sector, technologies = NULL, ...)

Arguments

sector

A string. Sector name specifying a colour palette. Run unique(r2dii.plot:::technology_colours$sector) to see available sectors.

technologies

A character vector. Specifies technologies to use as colours and their order. Run unique(r2dii.plot:::technology_colours$technology) to see available technologies (pay attention if they match the sector). Similar to value parameter in ggplot2::scale_colour_manual().

...

Other parameters passed on to ggplot2::discrete_scale().

Value

An object of class "ScaleDiscrete".

Examples

library(ggplot2, warn.conflicts = FALSE)

ggplot(mpg) +
  geom_point(aes(displ, hwy, color = class)) +
  scale_colour_r2dii_tech("automotive")

ggplot(mpg) +
  geom_histogram(aes(cyl, fill = class), position = "dodge", bins = 5) +
  scale_fill_r2dii_tech("automotive")

Custom PACTA colour and fill scales

Description

A custom discrete colour and fill scales with colours from the PACTA palette.

Usage

scale_fill_r2dii(colour_labels = NULL, ...)

Arguments

colour_labels

...

Other parameters passed on to ggplot2::discrete_scale().

Value

An object of class "ScaleDiscrete".

Examples

library(ggplot2, warn.conflicts = FALSE)

ggplot(mpg) +
  geom_point(aes(displ, hwy, color = class)) +
  scale_colour_r2dii()

ggplot(mpg) +
  geom_histogram(aes(cyl, fill = class), position = "dodge", bins = 5) +
  scale_fill_r2dii()

Custom PACTA sector colour and fill scales

Description

A custom discrete colour and fill scales with colours from the PACTA sector palette.

Usage

scale_fill_r2dii_sector(sectors = NULL, ...)

Arguments

sectors

...

Other parameters passed on to ggplot2::discrete_scale().

Value

An object of class "ScaleDiscrete".

Examples

library(ggplot2, warn.conflicts = FALSE)

ggplot(mpg) +
  geom_point(aes(displ, hwy, color = class)) +
  scale_colour_r2dii_sector()

ggplot(mpg) +
  geom_histogram(aes(cyl, fill = class), position = "dodge", bins = 5) +
  scale_fill_r2dii_sector()

Custom PACTA technology colour and fill scales

Description

A custom discrete colour and fill scales with colours from the PACTA technology palette.

Usage

scale_fill_r2dii_tech(sector, technologies = NULL, ...)

Arguments

sector

A string. Sector name specifying a colour palette. Run unique(r2dii.plot:::technology_colours$sector) to see available sectors.

technologies

...

Other parameters passed on to ggplot2::discrete_scale().

Value

An object of class "ScaleDiscrete".

Examples

library(ggplot2, warn.conflicts = FALSE)

ggplot(mpg) +
  geom_point(aes(displ, hwy, color = class)) +
  scale_colour_r2dii_tech("automotive")

ggplot(mpg) +
  geom_histogram(aes(cyl, fill = class), position = "dodge", bins = 5) +
  scale_fill_r2dii_tech("automotive")

Colour datasets

Description

All datasets have at least two columns:

label: Text label of the colour.
hex: Hex code of the colour.

Usage

scenario_colours

Format

An object of class tbl_df (inherits from tbl, data.frame) with 5 rows and 2 columns.

Details

In scenario_colours, colours are ordered from red to green to be used in trajectory charts.

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

A prepared climate scenario dataset for demonstration

Description

Fake climate scenario dataset, prepared for the software PACTA (Paris Agreement Capital Transition Assessment). It imitates climate scenario data (e.g. from the International Energy Agency (IEA)) including the change through time in production across industrial sectors.

Demo datasets are synthetic because most financial data is strictly private; they help to demonstrate and test the implementation in R of 'PACTA' (https://www.transitionmonitor.com/).

Usage

scenario_demo_2020

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1512 rows and 8 columns.

Definitions

region (character): The region to which the pathway is relevant., * scenario (character): The name of the scenario., * scenario_source (character): The source publication from which the scenario was taken., * sector (character): The sector to which the scenario prescribes a pathway., * smsp (double): Sector market share percentage of the pathway calculated in 2020., * technology (character): The technology within the sector to which the scenario prescribes a pathway., * tmsr (double): Technology market share ratio of the pathway calculated in 2020., * year (integer): The year at which the pathway value is prescribed.

Examples

head(scenario_demo_2020)

An example of an `sda_demo`-like dataset

Description

Dataset imitating the output of r2dii.analysis::target_sda().

Usage

sda_demo

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 110 rows and 6 columns.

Source

https://github.com/RMI-PACTA/r2dii.plot/issues/55.

Examples

sda_demo

A view of available sector classification datasets

Description

This dataset lists all sector classification code standards used by 'PACTA' (https://www.transitionmonitor.com/).

Usage

sector_classifications

Format

An object of class tbl_df (inherits from tbl, data.frame) with 6559 rows and 4 columns.

Definitions

borderline (character): Flag indicating if 2dii sector and classification code are a borderline match. The value TRUE indicates that the match is uncertain between the 2dii sector and the classification. The value FALSE indicates that the match is certainly perfect or the classification is certainly out of 2dii's scope.., * code (character): Formatted code., * code_system (character): Code system., * sector (character): Associated 2dii sector.

Details

Examples

head(sector_classifications)

Colour datasets

Description

All datasets have at least two columns:

label: Text label of the colour.
hex: Hex code of the colour.

Usage

sector_colours

Format

An object of class tbl_df (inherits from tbl, data.frame) with 8 rows and 2 columns.

Details

In scenario_colours, colours are ordered from red to green to be used in trajectory charts.

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

Dataset to bridge (translate) common sector-classification codes

Description

This dataset serves as a translation key between common sector-classification systems and sectors relevant to the 'PACTA' tool (https://www.transitionmonitor.com/).

Usage

sic_classification

Format

An object of class tbl_df (inherits from tbl, data.frame) with 1005 rows and 5 columns.

Definitions

borderline (character): Flag indicating if PACTA sector and classification code are a borderline match. The value TRUE indicates that the match is uncertain between the PACTA sector and the classification. The value FALSE indicates that the match is certainly perfect or the classification is certainly out of PACTA's scope.., * code (character): Original SIC code., * description (character): Original SIC description., * sector (character): Associated PACTA sector., * version (character): Column identifying to which SIC version the code belongs.

Details

Examples

head(sic_classification)

Replicate labels produced with `⁠qplot_*()⁠` functions

Description

to_title() converts labels like qplot_emission_intensity().
recode_metric_trajectory() converts labels like qplot_trajectory().
recode_metric_techmix() converts labels like qplot_techmix().
spell_out_technology() converts technology labels like qplot_techmix().

Usage

spell_out_technology(x)

Arguments

x

A character vector.

Value

A character vector.

Examples

to_title(c("a.string", "another_STRING"))

metric <- c("projected", "corporate_economy", "target_xyz", "else")
recode_metric_trajectory(metric)

recode_metric_techmix(metric)

spell_out_technology(c("gas", "ice", "coalcap", "hdv"))

Add targets for production, using the market share approach

Description

This function calculates the portfolio-level production targets, as calculated using the market share approach applied to each relevant climate production forecast.

Usage

target_market_share(
  data,
  abcd,
  scenario,
  region_isos = r2dii.data::region_isos,
  use_credit_limit = FALSE,
  by_company = FALSE,
  weight_production = TRUE,
  increasing_or_decreasing = r2dii.data::increasing_or_decreasing
)

Arguments

data

A "data.frame" like the output of r2dii.match::prioritize.

abcd

An asset level data frame like r2dii.data::abcd_demo.

scenario

A scenario data frame like r2dii.data::scenario_demo_2020.

region_isos

A data frame like r2dii.data::region_isos (default).

use_credit_limit

Logical vector of length 1. FALSE defaults to using the column loan_size_outstanding. Set to TRUE to use the column loan_size_credit_limit instead.

by_company

Logical vector of length 1. FALSE defaults to outputting production_value at the portfolio-level. Set to TRUE to output production_value at the company-level.

weight_production

Logical vector of length 1. TRUE defaults to outputting production, weighted by relative loan-size. Set to FALSE to output the unweighted production values.

increasing_or_decreasing

A data frame like r2dii.data::increasing_or_decreasing.

Value

A tibble including the summarized columns metric, production, technology_share, percentage_of_initial_production_by_scope and scope. If by_company = TRUE, the output will also have the column name_abcd.

Handling grouped data

This function ignores existing groups and outputs ungrouped data.

Examples



library(r2dii.data)
library(r2dii.match)

loanbook <- head(loanbook_demo, 100)
abcd <- head(abcd_demo, 100)

matched <- loanbook %>%
  match_name(abcd) %>%
  prioritize()

# Calculate targets at portfolio level
matched %>%
  target_market_share(
    abcd = abcd,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo
    )

# Calculate targets at company level
matched %>%
  target_market_share(
  abcd = abcd,
  scenario = scenario_demo_2020,
  region_isos = region_isos_demo,
  by_company = TRUE
  )

matched %>%
  target_market_share(
    abcd = abcd,
    scenario = scenario_demo_2020,
    region_isos = region_isos_demo,
    # Calculate unweighted targets
    weight_production = FALSE
    )

Add targets for CO₂ emissions per unit production at the portfolio level, using the SDA approach

Description

This function calculates targets of CO₂ emissions per unit production at the portfolio-level, otherwise referred to as "emissions factors". It uses the sectoral-decarbonization approach (SDA) to calculate these targets.

Usage

target_sda(
  data,
  abcd,
  co2_intensity_scenario,
  use_credit_limit = FALSE,
  by_company = FALSE,
  region_isos = r2dii.data::region_isos
)

Arguments

data

A dataframe like the output of r2dii.match::prioritize().

abcd

An asset-level data frame like r2dii.data::abcd_demo.

co2_intensity_scenario

A scenario data frame like r2dii.data::co2_intensity_scenario_demo.

use_credit_limit

Logical vector of length 1. FALSE defaults to using the column loan_size_outstanding. Set to TRUE to instead use the column loan_size_credit_limit.

by_company

Logical vector of length 1. FALSE defaults to outputting weighted_production_value at the portfolio-level. Set to TRUE to output weighted_production_value at the company-level.

region_isos

A data frame like r2dii.data::region_isos (default).

Value

A tibble including the summarized columns emission_factor_metric and emission_factor_value. If by_company = TRUE, the output will also have the column name_abcd.

Handling grouped data

This function ignores existing groups and outputs ungrouped data.

Examples



library(r2dii.match)
library(r2dii.data)

loanbook <- head(loanbook_demo, 150)
abcd <- head(abcd_demo, 100)

matched <- loanbook %>%
  match_name(abcd) %>%
  prioritize()

# Calculate targets at portfolio level
matched %>%
  target_sda(
   abcd = abcd,
   co2_intensity_scenario = co2_intensity_scenario_demo,
   region_isos = region_isos_demo
   )

# Calculate targets at company level
matched %>%
  target_sda(
   abcd = abcd,
   co2_intensity_scenario = co2_intensity_scenario_demo,
   region_isos = region_isos_demo,
   by_company = TRUE
   )

Colour datasets

Description

All datasets have at least two columns:

label: Text label of the colour.
hex: Hex code of the colour.

Usage

technology_colours

Format

An object of class tbl_df (inherits from tbl, data.frame) with 18 rows and 3 columns.

Details

In scenario_colours, colours are ordered from red to green to be used in trajectory charts.

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

Complete theme

Description

A ggplot theme which can be applied to all graphs to appear according to the PACTA plotting aesthetics.

Usage

theme_2dii(
  base_size = 12,
  base_family = "Helvetica",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

Arguments

base_size

base font size, given in pts.

base_family

base font family

base_line_size

base size for line elements

base_rect_size

base size for rect elements

Value

An object of class "theme", "gg".

Examples

library(ggplot2, warn.conflicts = FALSE)

ggplot(mtcars) +
  geom_histogram(aes(mpg), bins = 10) +
  theme_2dii()

Replicate labels produced with `⁠qplot_*()⁠` functions

Description

to_title() converts labels like qplot_emission_intensity().
recode_metric_trajectory() converts labels like qplot_trajectory().
recode_metric_techmix() converts labels like qplot_techmix().
spell_out_technology() converts technology labels like qplot_techmix().

Usage

to_title(x)

Arguments

x

A character vector.

Value

A character vector.

Examples

to_title(c("a.string", "another_STRING"))

metric <- c("projected", "corporate_economy", "target_xyz", "else")
recode_metric_trajectory(metric)

recode_metric_techmix(metric)

spell_out_technology(c("gas", "ice", "coalcap", "hdv"))

pacta.loanbook: Easily Install and Load PACTA for Banks Packages

Description

Author(s)

See Also

An asset-based company dataset for demonstration

Description

Usage

Format

Definitions

See Also

Examples

A prepared CO2 intensity climate scenario dataset for demonstration

Description

Usage

Format

Definitions

See Also

Examples

Crucial loanbook columns for match_name()

Description

Usage

Value

See Also

Examples

Data Dictionary

Description

Usage

Format

data_dictionary

Examples

Dataset to bridge (translate) common sector-classification codes

Description

Usage

Format

Definitions

Details

See Also

Examples

Determine if a technology is increasing or decreasing

Description

Usage

Format

Definitions

See Also

Examples

Dataset to bridge (translate) common sector-classification codes

Description

Usage

Format

Definitions

Details

See Also

Examples

Countries and codes

Description

Usage

Format

Definitions

See Also

Examples

A loanbook dataset for demonstration

Description

Usage

Format

Definitions

See Also

Examples

An example of a market_share_demo-like dataset

Description

Usage

Format

See Also

Examples

Match a loanbook to asset-based company data (abcd) by the ⁠name_*⁠ columns

Description

Usage

Arguments

Value

Assigning aliases

Handling grouped data

A prepared CO₂ intensity climate scenario dataset for demonstration

Crucial `loanbook` columns for `match_name()`

`data_dictionary`

An example of a `market_share_demo`-like dataset

Match a loanbook to asset-based company data (abcd) by the `⁠name_*⁠` columns

Conflicts between the `{pacta.loanbook}` and other packages

List all `{pacta.loanbook}` dependencies

The `{pacta.loanbook}` logo, using ASCII or Unicode characters

List all packages in `{pacta.loanbook}`

Get a situation report on `{pacta.loanbook}`

Update `{pacta.loanbook}` packages