Package 'pacta.loanbook'

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.

See Also

Other demo data: co2_intensity_scenario_demo, loanbook_demo, market_share, overwrite_demo, region_isos_demo, scenario_demo_2020, sda

Examples

head(abcd_demo)

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.

See Also

Other demo data: abcd_demo, loanbook_demo, market_share, overwrite_demo, region_isos_demo, scenario_demo_2020, sda

Examples

head(co2_intensity_scenario_demo)

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.

See Also

Other matching functions: match_name(), prioritize(), prioritize_level()

Examples

crucial_lbk()

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

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'.

See Also

Other datasets: increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(gics_classification)

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.

See Also

Other datasets: gics_classification, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(increasing_or_decreasing)

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

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'.

See Also

Other datasets: gics_classification, increasing_or_decreasing, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(isic_classification)

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.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(iso_codes)

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.

See Also

Other demo data: abcd_demo, co2_intensity_scenario_demo, market_share, overwrite_demo, region_isos_demo, scenario_demo_2020, sda

Examples

head(loanbook_demo)

Description

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

Usage

market_share

Format

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

See Also

Other demo data: abcd_demo, co2_intensity_scenario_demo, loanbook_demo, overwrite_demo, region_isos_demo, scenario_demo_2020, sda

Examples

market_share

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

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.

See Also

Other matching functions: crucial_lbk(), prioritize(), prioritize_level()

Examples

## Not run: 
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)

# Cleanup
options(restore)

## End(Not run)

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

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'.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(nace_classification)

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

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'.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(naics_classification)

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).

See Also

Other demo data: abcd_demo, co2_intensity_scenario_demo, loanbook_demo, market_share, region_isos_demo, scenario_demo_2020, sda

Examples

head(overwrite_demo)

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

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.

See Also

Other utility functions: pacta_loanbook_deps(), pacta_loanbook_logo(), pacta_loanbook_packages(), pacta_loanbook_sitrep(), pacta_loanbook_update()

Examples

pacta_loanbook_conflicts()

Description

List all {pacta.loanbook} dependencies

Usage

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

Arguments

Value

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

See Also

Other utility functions: pacta_loanbook_conflicts(), pacta_loanbook_logo(), pacta_loanbook_packages(), pacta_loanbook_sitrep(), pacta_loanbook_update()

Examples

## Not run: 
pacta_loanbook_deps()

## End(Not run)

Description

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

Usage

pacta_loanbook_logo(unicode = cli::is_utf8_output())

Arguments

Value

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

See Also

Other utility functions: pacta_loanbook_conflicts(), pacta_loanbook_deps(), pacta_loanbook_packages(), pacta_loanbook_sitrep(), pacta_loanbook_update()

Examples

pacta_loanbook_logo()

Description

List all packages in {pacta.loanbook}

Usage

pacta_loanbook_packages(include_self = TRUE)

Arguments

Value

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

See Also

Other utility functions: pacta_loanbook_conflicts(), pacta_loanbook_deps(), pacta_loanbook_logo(), pacta_loanbook_sitrep(), pacta_loanbook_update()

Examples

pacta_loanbook_packages()

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()

Value

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

See Also

Other utility functions: pacta_loanbook_conflicts(), pacta_loanbook_deps(), pacta_loanbook_logo(), pacta_loanbook_packages(), pacta_loanbook_update()

Examples

## Not run: 
pacta_loanbook_sitrep()

## End(Not run)

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

Value

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

See Also

Other utility functions: pacta_loanbook_conflicts(), pacta_loanbook_deps(), pacta_loanbook_logo(), pacta_loanbook_packages(), pacta_loanbook_sitrep()

Examples

## Not run: 
pacta_loanbook_update()

## End(Not run)

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.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

Description

Create an emission intensity plot

Usage

plot_emission_intensity(data)

Arguments

Value

An object of class "ggplot".

See Also

Other plotting functions: plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

plot_emission_intensity(data)

Description

Create a techmix plot

Usage

plot_techmix(data)

Arguments

Value

An object of class "ggplot".

See Also

Other plotting functions: plot_emission_intensity(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

plot_techmix(data)

Description

Create a trajectory plot

Usage

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

Arguments

Value

An object of class "ggplot".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

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

Description

Prepare data for a emission intensity plot

Usage

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

Arguments

Value

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

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

Description

Prepare data for plotting technology mix

Usage

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

Arguments

Value

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

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

prep_techmix(data)

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

Value

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

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

prep_trajectory(data)

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

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.

See Also

Other matching functions: crucial_lbk(), match_name(), prioritize_level()

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)

Description

Arrange unique level values in default order of priority

Usage

prioritize_level(data)

Arguments

Value

A character vector of the default level priority per loan.

See Also

Other matching functions: crucial_lbk(), match_name(), prioritize()

Examples

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

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

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'.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(psic_classification)

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

Value

An object of class "ggplot".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

qplot_emission_intensity(data)

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

Value

An object of class "ggplot".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

qplot_techmix(data)

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

Value

An object of class "ggplot".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

Examples

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

qplot_trajectory(data)

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

Value

A character vector.

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

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"))

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

Value

A character vector.

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

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"))

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.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, scenario_colours, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

head(region_isos)

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.

See Also

Other demo data: abcd_demo, co2_intensity_scenario_demo, loanbook_demo, market_share, overwrite_demo, scenario_demo_2020, sda

Examples

region_isos_demo

Description

A custom discrete colour and fill scales with colours from 2DII palette.

Usage

scale_colour_r2dii(colour_labels = NULL, ...)

Arguments

Value

An object of class "ScaleDiscrete".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

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()

Description

A custom discrete colour and fill scales with colours from 2DII sector palette.

Usage

scale_colour_r2dii_sector(sectors = NULL, ...)

Arguments

Value

An object of class "ScaleDiscrete".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

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()

Description

A custom discrete colour and fill scales with colours from 2DII technology palette.

Usage

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

Arguments

Value

An object of class "ScaleDiscrete".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

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")

Description

A custom discrete colour and fill scales with colours from 2DII palette.

Usage

scale_fill_r2dii(colour_labels = NULL, ...)

Arguments

Value

An object of class "ScaleDiscrete".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

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()

Description

A custom discrete colour and fill scales with colours from 2DII sector palette.

Usage

scale_fill_r2dii_sector(sectors = NULL, ...)

Arguments

Value

An object of class "ScaleDiscrete".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii(), to_title()

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()

Description

A custom discrete colour and fill scales with colours from 2DII technology palette.

Usage

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

Arguments

Value

An object of class "ScaleDiscrete".

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), spell_out_technology(), theme_2dii(), to_title()

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")

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.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, sector_classifications, sector_colours, sic_classification, technology_colours

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

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.

See Also

Other demo data: abcd_demo, co2_intensity_scenario_demo, loanbook_demo, market_share, overwrite_demo, region_isos_demo, sda

Examples

head(scenario_demo_2020)

Description

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

Usage

sda

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.

See Also

Other demo data: abcd_demo, co2_intensity_scenario_demo, loanbook_demo, market_share, overwrite_demo, region_isos_demo, scenario_demo_2020

Examples

sda

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

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'.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_colours, sic_classification, technology_colours

Examples

head(sector_classifications)

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.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sic_classification, technology_colours

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

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

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'.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, technology_colours

Examples

head(sic_classification)

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

Value

A character vector.

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), theme_2dii(), to_title()

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"))

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

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.

See Also

Other analysis functions: target_sda()

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
    )

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

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.

See Also

Other analysis functions: target_market_share()

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
   )

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.

See Also

Other datasets: gics_classification, increasing_or_decreasing, isic_classification, iso_codes, nace_classification, naics_classification, palette_colours, psic_classification, region_isos, scenario_colours, sector_classifications, sector_colours, sic_classification

Examples

palette_colours

scenario_colours

sector_colours

technology_colours

Description

A ggplot theme which can be applied to all graphs to appear according to 2DII plotting aesthetics.

Usage

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

Arguments

Value

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

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), to_title()

Examples

library(ggplot2, warn.conflicts = FALSE)

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

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

Value

A character vector.

See Also

Other plotting functions: plot_emission_intensity(), plot_techmix(), plot_trajectory(), prep_emission_intensity(), prep_techmix(), prep_trajectory(), qplot_emission_intensity(), qplot_techmix(), qplot_trajectory(), recode_metric_techmix(), recode_metric_trajectory(), scale_colour_r2dii(), scale_colour_r2dii_sector(), scale_colour_r2dii_tech(), scale_fill_r2dii(), scale_fill_r2dii_sector(), scale_fill_r2dii_tech(), spell_out_technology(), theme_2dii()

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"))