Package 'pacta.multi.loanbook'

Title: Run PACTA on multiple loan books easily
Description: This repo allows running PACTA analyses on multiple loan books in a structured way and provides access to additional PACTA-related metrics for multiple loan books. Results take the form of csv files and plots and are exported to specified project paths.
Authors: Jacob Kastl [aut, cre, ctr] , CJ Yetman [aut, ctr] , Monika Furdyna [aut, ctr] , RMI [cph, fnd]
Maintainer: Jacob Kastl <[email protected]>
License: MIT + file LICENSE
Version: 0.0.0.9000
Built: 2024-09-19 15:17:46 UTC
Source: https://github.com/rmi-pacta/pacta.multi.loanbook

Help Index

ABCD test data

Description

ABCD test data

Usage

abcd_test_data

Format

An object of class tbl_df (inherits from tbl, data.frame) with 582 rows and 13 columns.

Data dictionary

Description

An overview of the output data sets generated by the package, their data types, and the definitions of the variables.

Usage

data_dictionary

Format

data_dictionary

dataset

Name of the dataset

column

Name of the column

typeof

Data type of the column

definition

Description of what the column stands for

value

Which values are allowed for the column

...

Details

For more details see the help vignette: vignette("data_dictionary", package = "pacta.multi.loanbook")

Source

internal

Loan book test data

Description

Loan book test data

Usage

loanbook_test_data

Format

An object of class tbl_df (inherits from tbl, data.frame) with 20 rows and 19 columns.

plot_aggregate_loanbooks

Description

plot_aggregate_loanbooks

Usage

plot_aggregate_loanbooks(config)

Arguments

config

either a single string defining the path to a config YML file or a list object that contains the appropriate config params

Value

NULL (called for side effects)

Make a sankey plot

Description

Make a sankey plot

Usage

plot_sankey(
  data,
  group_var,
  capitalise_node_labels = TRUE,
  save_png_to = NULL,
  png_name = "sankey.png",
  nodes_order_from_data = FALSE
)

prep_sankey(
  data_alignment,
  region,
  year,
  group_var,
  middle_node,
  middle_node2 = NULL
)

Arguments

data

data.frame. Should have the same format as output of prep_sankey() and contain columns: "middle_node", optionally "middle_node2", "is_aligned", "loan_size_outstanding", and any column implied by group_var.
group_var

Character. Vector of length 1. Variable to group by.
capitalise_node_labels

Logical. Flag indicating if node labels should be converted into better looking capitalised form.
save_png_to

Character. Path where the output in png format should be saved
png_name

Character. File name of the output.
nodes_order_from_data

Logical. Flag indicating if nodes order should be determined by an algorithm (in case of big datasets often results in a better looking plot) or should they be ordered based on data.
data_alignment

data.frame. Holds aggregated alignment metrics per company for tms sectors. Must contain columns: "name_abcd", "sector" and any column implied by group_var.
region

Character. Region to filter data_alignment data frame on.
year

Integer. Year on which data_alignment should be filtered.
middle_node

Character. Column specifying the middle nodes to be plotted in sankey plot. Must be present in data_alignment.
middle_node2

Character. Column specifying the middle nodes to be plotted in sankey plot. Must be present in data_alignment.

Value

data.frame

Examples

# TODO

Plot alignment scatterplot

Description

Plot alignment scatterplot

Usage

plot_scatter(
  data,
  sector = NULL,
  scenario_source = NULL,
  scenario = NULL,
  year = NULL,
  region = NULL,
  title = NULL,
  subtitle = NULL,
  alignment_limit = NULL,
  data_level = c("company", "group_var"),
  cap_outliers = NULL,
  floor_outliers = NULL
)

prep_scatter(
  data_bopo,
  data_net,
  data_level = c("group_var", "company"),
  year,
  sector,
  region,
  group_var,
  groups_to_plot = NULL
)

Arguments

data

data.frame. Should have the same format as output of prep_scatter() and contain columns: 'name', 'buildout', phaseout', 'net'.
sector

Character. Sector to filter data on.
scenario_source

Character. Scenario source to be used in the plot caption.
scenario

Character. Scenario name to be used in the plot caption.
year

Integer. Year on which the data should be filtered.
region

Character. Region to filter data on.
title

Character. Custom title if different than default.
subtitle

Character. Custom subtitle if different than default.
alignment_limit

Numeric. Limit to be applied to the x- and y-axis scales and to alignment values for colouring. By default the maximum absolute alignment value of is used.
data_level

Character. Level of the plotted data. Can be 'group_var' or 'company'.
cap_outliers

Numeric. Cap which should be applied to the alignment values in the data. Values bigger than cap are plotted on the border of the plot.
floor_outliers

Numeric. Floor which should be applied to the alignment values in the data. Values smaller than floor are plotted on the border of the plot.
data_bopo

data.frame. Data containing buildout and phaseout alignment values. Must contain columns: 'year', 'sector', 'region', 'direction' and either 'name_abcd' and 'alignment_metric' or 'exposure_weighted_net_alignment' plus any column implied by group_var.
data_net

data.frame. Data containing net alignment values. Must contain columns: group_var, 'year', 'sector', 'region', 'direction' and either 'name_abcd' and 'alignment_metric' or 'exposure_weighted_net_alignment'.
group_var

Character. Vector of length 1. Variable to group by.
groups_to_plot

Character vector. Groups to filter on.

Value

object of type "ggplot"

data.frame

Examples

# TODO

Plot alignment scatterplot

Description

Plot alignment scatterplot

Usage

plot_scatter_alignment_exposure(
  data,
  floor_outliers,
  cap_outliers,
  group_var,
  currency
)

prep_scatter_alignment_exposure(
  data,
  year,
  region,
  scenario,
  group_var,
  exclude_groups = "benchmark"
)

Arguments

data

data.frame. Holds net aggregated alignment metrics on the loan book level. Must contain columns: "scenario", "region", "sector", "year", "exposure_weighted_net_alignment", "sum_loan_size_outstanding" and any column implied by group_var.
floor_outliers

Numeric. Floor which should be applied to the alignment values in the data. Values smaller than floor are plotted on the border of the plot.
cap_outliers

Numeric. Cap which should be applied to the alignment values in the data. Values bigger than cap are plotted on the border of the plot.
group_var

Character. Vector of length 1. A column to group by. Must be available variables in data.
currency

Character. Currency to display in the plot labels.
year

Integer. Year on which data should be filtered.
region

Character. Region to filter data data frame on.
scenario

Character. Scenario to filter data data frame on.
exclude_groups

Character. Character specifying any values from group_var that should not be included in the analysis. This is useful to remove benchmarks that are not meant to be compared at the same level. Defaults to "benchmark".

Value

object of type "ggplot"

data.frame

Examples

# TODO

Plot alignment scatterplot

Description

Plot alignment scatterplot

Usage

plot_scatter_animated(
  data,
  data_level = c("company", "group_var"),
  sector = NULL,
  scenario_source = NULL,
  scenario = NULL,
  region = NULL,
  title = NULL,
  subtitle = NULL,
  alignment_limit = NULL,
  cap_outliers = NULL,
  floor_outliers = NULL
)

prep_scatter_animated(
  data_bopo,
  data_net,
  data_level = c("group_var", "company"),
  sector,
  region,
  group_var,
  groups_to_plot = NULL
)

Arguments

data

data.frame. Should have the same format as output of prep_scatter_animated() and contain columns: 'name', 'buildout', 'phaseout', 'net' and 'year'.
data_level

Character. Level of the plotted data. Can be 'group_var' or 'company'.
sector

Character. Sector to filter data on.
scenario_source

Character. Scenario source to be used in the plot caption.
scenario

Character. Scenario name to be used in the plot caption.
region

Character. Region to filter data on.
title

Character. Custom title if different than default.
subtitle

Character. Custom subtitle if different than default.
alignment_limit

Numeric. Limit to be applied to the x- and y-axis scales and to alignment values for colouring. By default the maximum absolute alignment value from data is used.
cap_outliers

Numeric. Cap which should be applied to the alignment values in the data. Values bigger than cap are plotted on the border of the plot.
floor_outliers

Numeric. Floor which should be applied to the alignment values in the data. Values smaller than floor are plotted on the border of the plot.
data_bopo

data.frame. Data containing buildout and phaseout alignment values. Must contain columns: 'year', 'sector', 'region', 'direction' and either 'name_abcd' and 'alignment_metric' or 'exposure_weighted_net_alignment' plus any column implied by group_var.
data_net

data.frame. Data containing net alignment values. Must contain columns: group_var, 'year', 'sector', 'region', 'direction' and either 'name_abcd' and 'alignment_metric' or 'exposure_weighted_net_alignment'.
group_var

Character. Vector of length 1. Variable to group by.
groups_to_plot

Character vector. Groups to filter on.

Value

object of type "plotly"

data.frame

Examples

# TODO

run_pacta

Description

run_pacta

Usage

run_pacta(config)

Arguments

config

either a single string defining the path to a config YML file or a list object that contains the appropriate config params

Value

NULL (called for side effects)