In 2DegreesInvesting/r2dii.ggplot: Create Standard Static '2dii' Plots

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  fig.align = "left",
  out.width = "100%"
)

#' Pull the title of a documented object
#' This function helps keep README and help files in sync.
#' @param name String. The name of a documented object.
#' @return String.
#' @examples 
#' pull_title("plot_trajectory")
pull_title <- function(name, db = enframe_documentation("r2dii.ggplot")) {
  is_name <- db$name == name
  out <- db$title[is_name]
  tolower(out)
}

enframe_documentation <- function(package) {
  db <- utils::hsearch_db(package, lib.loc = locate_package(package))[["Base"]]
  names(db) <- tolower(names(db))
  db
}

locate_package <- function(pkg) {
  locate <- function(path) any(grepl(pkg, list.files(path)))
  has_pkg <- unlist(lapply(.libPaths(), locate))
  .libPaths()[has_pkg][[1]]
}

r2dii.ggplot DEPRECATED (see r2dii.plot.static)

The goal of r2dii.ggplot is to provide users with plotting and data processing functions that will allow the users to create standard 2DII plots using PACTA_analysis or banks' output data as input. The plots are in the form of ggplot objects.

Installation

You can install the development version of r2dii.ggplot from GitHub with:

# install.packages("devtools")
devtools::install_github("2DegreesInvesting/r2dii.ggplot")

How to minimize installation errors?

Example

library(r2dii.ggplot)

get_example_data() r pull_title("get_example_data").
process_input_data() r pull_title("process_input_data").

example_data <- get_example_data()
example_data <- process_input_data(example_data)

plot_trajectory() r pull_title("plot_trajectory").

data_trajectory <- prepare_for_trajectory_chart(
  example_data, 
  sector_filter = "power", 
  technology_filter = "oilcap",
  region_filter = "global", 
  scenario_source_filter = "demo_2020",
  value_name = "production", 
  end_year_filter = 2025,
  normalize_to_start_year = TRUE
)

scenario_specs <- data.frame(
  scenario = c("sds", "sps", "cps", "worse"),
  color = c("#9CAB7C", "#FFFFCC", "#FDE291", "#E07B73"),
  label = c("SDS", "STEPS", "CPS", "worse")
)

main_line_metric <- data.frame(metric = "projected", label = "Portfolio")
additional_line_metrics <- data.frame(
  metric = "corporate_economy",
  label = "Corporate Economy"
)

plot <- plot_trajectory(data_trajectory,
  plot_title = "Production trajectory of Oil Capacity technology \n in the Power sector",
  x_title = "Year", y_title = "Production rate (normalized to 2020)",
  annotate_data = FALSE,
  scenario_specs_good_to_bad = scenario_specs,
  main_line_metric, additional_line_metrics
)

plot

prepare_for_techmix_chart() r pull_title("prepare_for_techmix_chart").
get_r2dii_technology_colours() r pull_title("get_r2dii_technology_colours").
plot_techmix() r pull_title("plot_techmix").

data_techmix_power <- prepare_for_techmix_chart(example_data,
  sector_filter = "power",
  years_filter = c(2020, 2025), region_filter = "global",
  scenario_source_filter = "demo_2020",
  scenario_filter = "sds", value_name = "technology_share"
)

tech_colors_power <- get_r2dii_technology_colours("power")
bars_labels_specs <- data.frame(
  "metric_type" = c(
    "portfolio_2020", "benchmark_2020", "portfolio_2025",
    "benchmark_2025", "scenario_2025"
  ),
  "label" = c(
    "Portfolio 2020", "Benchmark 2020", "Portfolio 2025",
    "Benchmark 2025", "Target SDS 2025"
  )
)

plot_techmix_power <- plot_techmix(data_techmix_power,
  plot_title = "Technology mix for the Power sector",
  show_legend = TRUE,
  tech_colors_power,
  bars_labels_specs
)
plot_techmix_power

power_colors_custom <- data.frame(
  "technology" = c("coalcap", "oilcap", "gascap", "nuclearcap", "hydrocap", "renewablescap"),
  "label" = c("Coal Capacity", "Oil Capacity", "Gas Capacity", "Nuclear Capacity", "Hydro Capacity", "Renewables Capacity"),
  "colour" = c("black", "brown", "grey", "red", "blue", "green4")
)

plot_techmix_custom_col <- plot_techmix(data_techmix_power, "Technology mix for the Power sector",
  show_legend = TRUE, power_colors_custom, bars_labels_specs
)
plot_techmix_custom_col

Metareport code examples

This is a basic example usage of plot_metareport_security_types() given that you have the total_portfolio.rda data set from PACTA analysis results loaded in your environment:

data_security_type <- prepare_for_metareport_security_type_chart(data_total_portfolio)

bars_labels_specs <- data.frame(
  "investor_name" = c("pensionfund", "Meta Investor", "insurance", 
  "bank", "assetmanager"),
  "label" = c("Pension Funds", "Meta Investor", "Insurance", 
  "Banks", "Asset Managers")
)

bars_asset_type_specs <- data.frame(
  "asset_type" = c("Equity", "Bonds", "Others"),
  "label" = c("Equity", "Bonds", "Others"),
  "r2dii_colour_name" = c("dark_blue", "moss_green", "grey")
)

p <- plot_metareport_security_types(data_security_type, 
                                    bars_asset_type_specs, 
                                    bars_labels_specs)
p

This is a basic example usage of plot_metareport_pacta_sectors() given that you have the overview_portfolio.rda data set from PACTA analysis results loaded in your environment:

data_climate_relevant <- prepare_for_pacta_sectors_chart(data_overview)

bars_labels_climate_rel <- data.frame(
  "investor_name" = c("pensionfund","insurance", "bank", "assetmanager"),
  "label" = c("Pension Funds", "Insurance", "Banks", "Asset Managers")
)

plot <- plot_metareport_pacta_sectors(data = data_climate_relevant, 
                                      bars_labels_specs = bars_labels_climate_rel,
                                      plot_title = "Percentage of Asset type Portfolios invested in PACTA sectors")
plot

These are basic examples of using plot_metareport_distribution() given that you have the Equity_results_portfolio.rda or Bonds_results_portfolio.rda data set from PACTA analysis results loaded in your environment:

investor_labels <- data.frame(
  "investor_name" = c("assetmanager","bank","insurance","pensionfund"),
  "label" = c("Asset Managers","Banks","Insurance","Pension funds")
)

data_distr_br_port <- prepare_for_metareport_distribution_chart(data_equity,
                                                      sectors_filter = "Power",
                                                      technologies_filter = c("CoalCap","OilCap","GasCap"),
                                                      year_filter = 2020,
                                                      value_to_plot = "plan_carsten")

p_br <- plot_metareport_distribution(data_distr_br_port, 
                                  plot_title = "Percentage of a portfolio invested in brown technologies in the Power sector - Listed Equity",
                                  x_title = "Participants",
                                  y_title = "",
                                  investor_labels = investor_labels)
p_br

data_distr_gr_prod <- prepare_for_metareport_distribution_chart(data_equity,
                                                      sectors_filter = "Power",
                                                      technologies_filter = c("RenewablesCap", "HydroCap"),
                                                      year_filter = 2020,
                                                      value_to_plot = "plan_tech_share")

p_gr <- plot_metareport_distribution(data_distr_gr_prod, 
                                  plot_title = "Percentage share of a low carbon power production - Listed Equity",
                                  x_title = "Participants",
                                  y_title = "",
                                  investor_labels = investor_labels)
p_gr

These are basic examples of using plot_metareport_bubble() given that you have the Equity_results_portfolio.rda or Bonds_results_portfolio.rda data set from PACTA analysis results loaded in your environment:

data_bubble <- prepare_for_metareport_bubble_chart(data_equity,
                                                asset_type = "Equity",
                                                start_year = 2020,
                                                technologies_filter = c("RenewablesCap"),
                                                scenario_filter = "WEO2019_SDS",
                                                scenario_geography_filter = "GlobalAggregate")

investor_labels <- data.frame(
  "investor_name" = c("assetmanager","bank","insurance","pensionfund"),
  "label" = c("Asset Managers","Banks","Insurance","Pension funds")
)

p <- plot_metareport_bubble(data_bubble,
                            plot_title = "Listed Equity",
                            x_title = "Current share of renewable energy capacity",
                            y_title = "Planned build-out as % of build-out required by SDS",
                            investor_labels = investor_labels,
                            colour_investors = TRUE
                            )
p

These is a basic example of using plot_metareport_map() given that you have the Equity_results_map.rda or Bonds_results_map.rda data set from PACTA analysis results loaded in your environment:

data_map_eq_chart <- prepare_for_map_chart(data_map_eq,
                                  asset_type = "Equity",
                                  technology_filter = "Oil",
                                  year_filter = 2020,
                                  value_divisor = 10^6)

legend_unit <- data_map_eq_chart %>%
  filter(!is.na(unit)) %>%
  pull(unit) %>%
  unique()

legend_divisor <- data_map_eq_chart %>%
  filter(!is.na(abbreviation_divisor)) %>%
  pull(abbreviation_divisor) %>%
  unique()

legend_title <- paste(legend_divisor, legend_unit, sep = " ")

p <- plot_metareport_map(data,
                         plot_title = "Geographical distribution of physical assets - Oil Production, Equity",
                         legend_title = legend_title,
                         sector = "Oil&Gas")
p