README.md

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

r2dii.plot.static

The goal of r2dii.plot.static 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.plot.static from GitHub with:

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

How to minimize installation errors?

Example

library(dplyr, warn.conflicts = FALSE)
library(ggplot2, warn.conflicts = FALSE)
library(r2dii.plot.static)

• example_data imports example data set for plotting.
• process_input_data() performs the initial processing on raw input data in banks’ format.

example_data <- process_input_data(example_data)

• plot_trajectory() create a trajectory alignment chart in a ggplot object.

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

scenario_specs <- tibble(
  scenario = c("sds", "sps", "cps"),
  label = c("SDS", "STEPS", "CPS")
)

main_line_metric <- tibble(
  metric = "projected",
  label = "Portfolio"
)

additional_line_metrics <- tibble(
  metric = "corporate_economy",
  label = "Corporate Economy"
)

plot_trajectory(
  data_trajectory,
  scenario_specs_good_to_bad = scenario_specs,
  main_line_metric = main_line_metric,
  additional_line_metrics = additional_line_metrics
)

• prepare_for_techmix_chart() prepares pre-processed data for plotting a tech-mix chart.
• get_r2dii_technology_colours() get the predefined technology colors for a sector.
• plot_techmix() create a techmix chart in a ggplot object.

# Default colours, all data, added title

sector <- "power"

data <- prepare_for_techmix_chart(example_data,
  sector_filter = sector,
  years_filter = c(2020, 2025), region_filter = "global",
  scenario_source_filter = "demo_2020",
  scenario_filter = "sds", value_to_plot = "technology_share"
)

plot <- plot_techmix(data)
plot +
  ggplot2::labs(title = "Technology mix for the Power sector")

# Custom colours, all data, no title
power_colors_custom <- tibble(
  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 <- plot_techmix(data,
  tech_colours = power_colors_custom
)
plot

# Default colours, selected data and labels (metric_type parameters), added title

sector <- "automotive"

data <- prepare_for_techmix_chart(example_data,
  sector_filter = sector,
  years_filter = c(2020, 2025), region_filter = "global",
  scenario_source_filter = "demo_2020",
  scenario_filter = "sds", value_to_plot = "technology_share"
)

metric_type_order = c(
    "portfolio_2020", "benchmark_2020", "portfolio_2025",
    "benchmark_2025", "scenario_2025"
)
metric_type_labels = c(
    "Portfolio 2020", "Benchmark 2020", "Portfolio 2025",
    "Benchmark 2025", "Target SDS 2025"
  )

plot <- plot_techmix(data,
  metric_type_order = metric_type_order,
  metric_type_labels = metric_type_labels
)
plot +
  ggplot2::labs(title = "Technology mix for the Automotive sector")

• prepare_for_timelineA() .
• plot_timelineA() creates a time line plot.

# Using default preparation and specs
data <- prepare_for_timelineA(sda_target, sector_filter = "cement")
plot_timelineA(data)

# Using custom preparation
data <- prepare_for_timelineA(
  sda_target,
  sector_filter = "cement",
  year_start = 2020,
  year_end = 2050,
  column_line_names = "emission_factor_metric",
  value_to_plot = "emission_factor_value",
  extrapolate_missing_values = TRUE
)

# Using custom specs and extending the plot with ggplot2
plot_timelineA(data) +
  labs(
    title = "Emission intensity trend for Cement.",
    x = "Year",
    y = "Tons of CO2 per ton",
    caption = "Dashed line is an extrapolation of the last value in the dataset."
  )

• timeline_specs() creates the default specs data frame for ‘plot_timelinea()’.
• r2dii_palette_colours() outputs a data frame giving the 2dii colour palette.

# You may use it as a template to create your custom specs
timeline_specs(data)
#> # A tibble: 4 x 3
#>   line_name              label                  colour_hex
#>   <chr>                  <chr>                  <chr>     
#> 1 projected              Projected              #1b324f   
#> 2 corporate_economy      Corporate Economy      #00c082   
#> 3 target_demo            Target Demo            #ff9623   
#> 4 adjusted_scenario_demo Adjusted Scenario Demo #d0d7e1

# You may use it as a reference for 2DII's colour palette
r2dii_palette_colours()
#> # A tibble: 9 x 2
#>   label       colour_hex
#>   <chr>       <chr>     
#> 1 dark_blue   #1b324f   
#> 2 green       #00c082   
#> 3 orange      #ff9623   
#> 4 grey        #d0d7e1   
#> 5 dark_purple #574099   
#> 6 yellow      #f2e06e   
#> 7 soft_blue   #78c4d6   
#> 8 ruby_red    #a63d57   
#> 9 moss_green  #4a5e54

2DegreesInvesting/r2dii.plot.static documentation built on Dec. 17, 2021, 6:37 a.m.