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dry high level docs #246

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Jun 9, 2021
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dry high level docs #246

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225cadd
DRY intro to r2dii.plot X and Y
maurolepore Jun 9, 2021
fc00396
Re-build README.Rmd
actions-user Jun 9, 2021
7f91ea2
Make README useful again
maurolepore Jun 9, 2021
f764450
Merged origin/master into dry-high-level-docs
maurolepore Jun 9, 2021
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218 changes: 3 additions & 215 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -12,30 +12,6 @@ knitr::opts_chunk$set(
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.plot")) {
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.plot <a href='https://github.com/2DegreesInvesting/r2dii.plot'><img src='https://imgur.com/A5ASZPE.png' align='right' height='43' /></a>
Expand All @@ -48,197 +24,9 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
[![CRAN status](https://www.r-pkg.org/badges/version/r2dii.plot)](https://CRAN.R-project.org/package=r2dii.plot)
<!-- badges: end -->

The goal of r2dii.plot 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 from
[GitHub](https://github.com/2DegreesInvesting/r2dii.plot) with:

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

[How to minimize installation errors?](https://gist.github.com/maurolepore/a0187be9d40aee95a43f20a85f4caed6#installation)

## Example

```{r}
library(dplyr, warn.conflicts = FALSE)
library(ggplot2, warn.conflicts = FALSE)
library(r2dii.plot)
```

* `market_share` `r pull_title("market_share")`.

```{r}
market_share
```

* `prep_trajectoryY()`: `r pull_title("prep_trajectory")`.

```{r}
data_trajectory <- prep_trajectoryY(
market_share,
sector_filter = "power",
technology_filter = "oilcap",
region_filter = "global",
scenario_source_filter = "demo_2020",
end_year_filter = 2025,
normalize = TRUE
)
```{r echo=FALSE}
intro <- system.file("intro.Rmd", package = "r2dii.plot")
```

* `plot_trajectoryY()` is an alternative to `plot_trajectoryB()`.

```{r}
# `plot_trajectoryY()` takes more arguments

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

# more elaborate annotations, title and labels

data_trajectory <- prep_trajectoryY(
market_share,
sector_filter = "power",
technology_filter = "renewablescap",
region_filter = "global",
scenario_source_filter = "demo_2020",
value = "production",
end_year_filter = 2025,
normalize = TRUE
)

scenario_specs <- tibble(
scenario = c("sds", "sps", "cps"),
label = c(
"Sustainable Development Scenario",
"Stated Policies Scenario",
"Current Policies Scenario")
)

plot <- plot_trajectoryY(data_trajectory,
scenario_specs_good_to_bad = scenario_specs,
main_line_metric = main_line_metric,
additional_line_metrics = additional_line_metrics
)

plot +
ggplot2::theme(
plot.margin = ggplot2::unit(c(0.5, 7, 0.5, 0.5), "cm")
) +
ggplot2::labs(
title = "Production trajectory of Renewables Capacity technology\n in the Power sector",
subtitle = "The coloured areas indicate trajectories in reference to a scenario.\n The red area indicates trajectories below any sustainble scenario.",
x = "Year",
y = "Production rate (normalized to 2020)"
)
```

* `prep_techmixY()` `r pull_title("prep_techmix")`.
* `plot_techmixY()` `r pull_title("plot_techmix")`.

```{r}
# Default colours, all data, added title
sector <- "power"

data <- prep_techmixY(
market_share,
sector_filter = sector,
years_filter = c(2020, 2025),
region_filter = "global",
scenario_source_filter = "demo_2020",
scenario_filter = "sds",
value = "technology_share"
)

plot <- plot_techmixY(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"),
hex = palette.colors(n = length(technology), palette = "ggplot2")
)

plot <- plot_techmixY(data,
tech_colours = power_colors_custom
)
plot

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

sector <- "automotive"

data <- prep_techmixY(market_share,
sector_filter = sector,
years_filter = c(2020, 2025), region_filter = "global",
scenario_source_filter = "demo_2020",
scenario_filter = "sds", value = "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_techmixY(data,
metric_type_order = metric_type_order,
metric_type_labels = metric_type_labels
)
plot +
ggplot2::labs(title = "Technology mix for the Automotive sector")
```

* `prep_timelineY()` `r pull_title("prep_timeline")`.
* `plot_timelineY()` `r pull_title("plot_timelineA")`.

```{r}
data <- prep_timelineY(sda, sector_filter = "cement", extrapolate = TRUE)

# Plot and customize with ggplot2
plot_timelineY(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()` `r pull_title("timeline_specs")`.

```{r}
# You may use it as a template to create your custom specs
timeline_specs(data)
```{r child=intro}
```
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