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Workshop_prioriactions_IEB_2022.Rmd
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---
title: "Prioriactions in Action"
author: "**José Salgado-Rojas / Matías Moreno-Faguett**"
date: "December 16, 2022"
output:
ioslides_presentation:
incremental: false
ccs: styles.css
keep_md: yes
logo: Figures/Logo.png
widescreen: yes
df_print: paged
smaller: yes
beamer_presentation: default
subtitle: Tutorial for ``R`` Users <br>
bibliography: bibtex.bib
editor_options:
markdown:
wrap: 72
---
```{=html}
<!---
- ioslides manual:
https://bookdown.org/yihui/rmarkdown/ioslides-presentation.html
- Compile from command-line
Rscript -e "rmarkdown::render('R_for_HPC.Rmd'); knitr::knit('R_for_HPC.Rmd', tangle=TRUE)"
-->
```
```{=html}
<!---
Note: following css chunks are required for scrolling support beyond slide boundaries
-->
```
```{=html}
<style>
slides > slide {
overflow-x: auto !important;
overflow-y: auto !important;
}
slides > slide.title-slide p {
color: black;
}
slides > slide.title-slide hgroup h1{
color: #4C7843;
}
</style>
```
```{css, echo=FALSE}
pre {
max-height: 300px;
overflow-y: auto;
}
pre[class] {
max-height: 300px;
}
```
```{css, echo=FALSE}
.scroll-300 {
max-height: 300px;
overflow-y: auto;
background-color: inherit;
}
```
##
### <https://github.com/prioriactions/teaching>
# Outline
- ::: white
**Features**
:::
- Overview
- Toy example
- Actual case study: The Mitchell River, Australia
- Shiny Application
## Why {#id .class width="8%" height="8%"}?
Reproducibility, Academic use, ...
<center>{.class
width="60%" height="60%"}</center>
## Features
::: {style="float: left; width: 50%;"}
<br>
- [Powered by **Rcpp and RcppArmadillo**]{style=""}
<img src="Figures/rcpp.png" style="vertical-align:middle" width="10%" height="10%"/>
- Find **optimal** solutions
- Solve a specific new problem using mathematical programming
- Ideally, fast 🙂 <br> <br>
:::
::: {style="float: left; width: 50%;"}
<center>{.class width="80%" height="80%"}</center>
:::
## Overview
[**Three**]{style="color: #B93600;"} questions: **Planning objective**,
**Optimization objective**, and **Intensity of threats**.
<center>{.class width="70%"
height="70%"}</center>
## Workflow
<center>{.class width="80%"
height="80%"}</center>
## [**Q1.**]{style="color: #B93600;"} Planning objective?
#### Recovery vs Conservation
<center>{.class width="60%"
height="60%"}</center>
## Workflow
<center>{.class width="80%"
height="80%"}</center>
## [**Q2.**]{style="color: #B93600;"} Threat intensities?
#### Binary vs Continuous
<br>
<center>{.class width="80%"
height="80%"}</center>
## [**Q2.**]{style="color: #B93600;"} Threat intensities?
#### Binary vs Continuous
<br>
<center>{.class width="70%"
height="70%"}</center>
## Workflow
<center>{.class width="80%"
height="80%"}</center>
# I) Input data validation
## Inputs (`inputData()`)
Data that defines the spatial prioritization problem (**planning units
data**, **feature data**, **threats data**, and their **spatial
distributions**).
<div>
<center>[**5**]{style=""}
<img src="Figures/table.png" style="vertical-align:middle" width="10%" height="10%"/>
[**+**]{style=""} [**2**]{style=""}
<img src="Figures/table_optional.png" style="vertical-align:middle" width="10%" height="10%"/>
[(optional)]{style=""}</center>
</div>
```{r input0_0, eval=FALSE, include=TRUE}
inputData(
pu,
features,
dist_features,
threats,
dist_threats,
sensitivity = NULL,
boundary = NULL
)
```
## Inputs
**What if I have my data in another format?**<br>
[**A:**]{style="color: red;"} There are functions that allow you to
import and convert data to `data.frame` format
Examples:
```{r inputs0_1, eval=FALSE, include=TRUE}
##from .txt to data.frame
df <- read.table('myfile.txt',sep='\t')
```
```{r inputs0_2, eval=FALSE, include=TRUE}
##from .csv to data.frame
df <- read.csv("myfile.csv")
```
<center>{.class width="80%"
height="80%"}</center>
<br>
## #1 Planning units data (`pu`)
#### `data.frame`
<br> Specifies the **planning units (PU)** of the corresponding instance
and their corresponding **monitoring cost** and **status**. Each row
corresponds to a different planning unit. This file is inherited from
the *pu.dat* in Marxan. <br> <br>
::: {style="float: left; width: 50%;"}
- [**id**]{style="color: #4385CD;"}<br> `integer` unique identifier
for each planning unit.
- [**monitoring_cost**]{style="color: #4385CD;"}<br> `numeric` cost of
including each planning unit in the reserve system.
- [**status** (optional)]{style="color: #4385CD;"}<br> `integer` value
that indicate if each planning unit should be available to be
selected (`0`), locked-in (`2`) as part of the solution, or
locked-out (`3`) and excluded from the solution.
:::
::: {style="float: right; width: 40%;"}
```{r inputs1, eval=TRUE, include=TRUE}
## id monitoring_cost status
## 1 2 0
## 2 2 0
## 3 2 0
## 4 2 0
## 5 2 0
## 6 2 0
```
:::
## #1 Planning units data (`pu`)
### [**id**]{style="color: #4385CD;"}<br>
<br>
<center>{.class
width="70%" height="70%"}</center>
## #1 Planning units data (`pu`)
### [**monitoring_cost**]{style="color: #4385CD;"}<br>
<center>{.class
width="50%" height="50%"}</center>
## #1 Planning units data (`pu`)
### [**status** (optional)]{style="color: #4385CD;"}<br>
- free (`0`)
- locked-in (`2`)
- locked-out (`3`)
<center>{.class width="40%"
height="40%"}</center>
## #2 Features data (`features`)
#### `data.frame`
<br> Specifies the **conservation features** to consider in the
optimization problem. Each row corresponds to a different feature. This
file is inherited from the *spec.dat* in Marxan. <br> <br>
::: {style="float: left; width: 50%;"}
- [**id**]{style="color: #4385CD;"}<br> `integer` unique identifier
for each conservation feature.
- [**target_recovery**]{style="color: #4385CD;"}<br> `numeric` amount
of recovery target to achieve for each conservation feature.
- [**target_conservation** (optional)]{style="color: #4385CD;"}<br>
`numeric` amount of conservation target to achieve for each
conservation feature.
- [**name** (optional)]{style="color: #4385CD;"}<br> `character` name
for each conservation feature.
:::
::: {style="float: right; width: 40%;"}
```{r inputs2, eval=TRUE, include=TRUE}
##id target_recovery name
## 1 11 feature1
## 2 16 feature2
## 3 8 feature3
## 4 9 feature4
```
:::
## #2 Features data (`features`)
### [**id**]{style="color: #4385CD;"} (Species) <br>
<center>{.class
width="45%" height="45%"}</center>
## #2 Features data (`features`)
### [**id**]{style="color: #4385CD;"} (Ecosystem Services)<br>
<center>{.class width="45%"
height="45%"}</center>
## #2 Features data (`features`)
### [**id**]{style="color: #4385CD;"} (Habitats) <br>
<br>
<center>{.class
width="70%" height="70%"}</center>
## #2 Features data (`features`)
### [**recovery_target**]{style="color: #4385CD;"}<br>
<br>
<center>{.class width="50%"
height="50%"}</center>
## #2 Features data (`features`)
### [**conservation_target (optional)**]{style="color: #4385CD;"}<br>
::: {style="float: left; width: 50%;"}
<br>
<center>{.class width="100%"
height="100%"}</center>
:::
::: {style="float: left; width: 50%;"}
<center>{.class width="100%"
height="100%"}</center>
:::
<center>Source: <https://marxansolutions.org></center>
## #3 Distribution Features data (`dist_features`)
#### `data.frame`
<br> Specifies the **spatial distribution of conservation features**
across planning units. Each row corresponds to a combination of
`planning unit` and `feature`. <br> <br>
::: {style="float: left; width: 50%;"}
- [**pu**]{style="color: #4385CD;"}<br> `integer` *id* of a planning
unit where the conservation feature listed on the same row occurs.
- [**feature**]{style="color: #4385CD;"}<br> `integer` *id* of each
conservation feature.
- [**amount**]{style="color: #4385CD;"}<br> `numeric` amount of the
feature in the planning unit. Set to `1` to work with
presence/absence.
:::
::: {style="float: right; width: 30%;"}
```{r inputs3, eval=TRUE, include=TRUE}
#pu feature amount
#1 3 1
#2 3 1
#3 3 1
#4 3 1
#5 3 1
#6 3 1
```
:::
## #3 Distribution Features data (`dist_features`)
### [**amount (binaries)**]{style="color: #4385CD;"}<br>
<center>{.class
width="50%" height="50%"}</center>
## #3 Distribution Features data (`dist_features`)
### [**amount (continuous)**]{style="color: #4385CD;"}<br>
<center>{.class
width="70%" height="70%"}</center>
## #4 Threats data (`threats`)
#### `data.frame`
<br> Specifies the **threats** to consider in the optimization exercise.
Each row corresponds to a different `threats`. <br> <br>
::: {style="float: left; width: 50%;"}
- [**id**]{style="color: #4385CD;"}<br> `integer` unique identifier
for each threat.
- [**blm_actions** (optional)]{style="color: #4385CD;"}<br> `numeric`
penalty of connectivity between threats. Default is `0`.
- [**name** (optional)]{style="color: #4385CD;"}<br> `character`
\`name for each conservation feature.
:::
::: {style="float: right; width: 35%;"}
```{r inputs4, eval=TRUE, include=TRUE}
#>id name blm_actions
#> 1 threat1 0
#> 2 threat2 0
```
:::
## #4 Threats data (`threats`)
### [**blm_actions**]{style="color: #4385CD;"}
<center>{.class
width="50%" height="50%"}</center>
## #5 Distribution Threats data (`dist_threats`)
#### `data.frame`
<br> specifies the **spatial distribution of threats** across planning
units. Each row corresponds to a combination of `planning unit` and
`threat`. <br> <br>
::: {style="float: left; width: 60%;"}
- [**pu**]{style="color: #4385CD;"}<br> `integer` *id* of a planning
unit where the threat listed on the same row occurs.
- [**threat**]{style="color: #4385CD;"}<br> `integer` *id* of each
conservation feature.
- [**amount**]{style="color: #4385CD;"}<br> `numeric` amount of the
threat in the planning unit. Set to `1` to work with
presence/absence. Continuous amount values require that feature
`sensitivities` to threats be established.
:::
::: {style="float: right; width: 30%;"}
```{r inputs5, eval=TRUE, include=TRUE}
#>pu threat amount action_cost status
#> 8 2 1 2 0
#> 9 2 1 2 0
#>10 2 1 2 0
#>11 1 1 3 0
#>11 2 1 4 0
#>12 1 1 3 0
```
:::
## #5 Distribution Threats data (`dist_threats`)
#### `data.frame`
<br> specifies the **spatial distribution of threats** across planning
units. Each row corresponds to a combination of `planning unit` and
`threat`. <br> <br>
- [**action_cost**]{style="color: #4385CD;"}<br> `numeric` cost of an
action to abate the threat in each planning unit.
- [**status** (optional)]{style="color: #4385CD;"}<br> `integer` value
that indicate if each planning unit should be available to be
selected (`0`), locked-in (`2`) as part of the solution, or
locked-out (`3`) and excluded from the solution. :::
## #5 Distribution Threats data (`dist_threats`)
### [**amount (continuous)**]{style="color: #4385CD;"}<br>
<center>{.class width="70%"
height="70%"}</center>
## #6 Sensitivity data (`sensitivity`)
### Optional
#### `data.frame`
<br> specifies the **sensitivity** of each `feature` to each `threat`.
Each row corresponds to a combination of `feature` and `threat`. <br>
<br>
::: {style="float: left; width: 70%;"}
- [**feature**]{style="color: #4385CD;"}<br> `integer` *id* of each
conservation feature.
- [**threat**]{style="color: #4385CD;"}<br> `integer` *id* of each
threat.
- [**a** (optional)]{style="color: #4385CD;"}<br> `numeric` the
minimum intensity of the threat at which the features probability of
persistence starts to decline.
- [**b** (optional)]{style="color: #4385CD;"}<br> `numeric` the value
of intensity of the threat over which the feature has a probability
of persistence of `0`.
:::
::: {style="float: right; width: 30%;"}
```{r inputs7, eval=TRUE, include=TRUE}
#>feature threat
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 1 2
#> 2 2
```
:::
## #6 Sensitivity data (`sensitivity`)
### Optional
#### `data.frame`
<br> specifies the **sensitivity** of each `feature` to each `threat`.
Each row corresponds to a combination of `feature` and `threat`. <br>
<br>
- [**c** (optional)]{style="color: #4385CD;"}<br> `numeric` minimum
probability of persistence of a features when a threat reaches its
maximum intensity value.
- [**d** (optional)]{style="color: #4385CD;"}<br> `numeric` maximum
probability of persistence of a features in absence threats. :::
## #6 Sensitivity data (`sensitivity`)
### [**a, b, c and d**]{style="color: #4385CD;"}<br>
<center>{.class width="70%"
height="70%"}</center>
## #6 Sensitivity data (`sensitivity`)
### [**a, b, c and d**]{style="color: #4385CD;"}<br>
<center>{.class width="60%"
height="60%"}</center>
## #7 Boundary data (`boundary`)
### Optional
#### `data.frame`
<br> Specifies the **spatial relationship** between pair of
`planning units`. Each row corresponds to a combination of
`planning unit`. <br> <br>
::: {style="float: left; width: 60%;"}
- [**id1**]{style="color: #4385CD;"}<br> `integer` *id* of each
planning unit.
- [**id2**]{style="color: #4385CD;"}<br> `integer` *id* of each
planning unit.
- [**boundary**]{style="color: #4385CD;"}<br> `numeric` penalty
applied in the objective function when only one of the planning
units is present in the solution.
:::
::: {style="float: right; width: 30%;"}
```{r inputs8, eval=TRUE, include=TRUE}
#>id1 id2 boundary
#> 1 1 0
#> 2 1 1
#> 3 1 2
#> 4 1 3
#> 5 1 4
#> 6 1 5
```
:::
## Workflow
<center>{.class width="80%"
height="80%"}</center>
## [**Q3.**]{style="color: #B93600;"} Optimization objective?
#### Minimize Costs (**`minimizeCosts`**) vs Maximize Benefits (**`maximizeBenefits`**)
<br>
<center>{.class width="80%"
height="80%"}</center>
$x_{ik}$ is the decisions variable that specifies whether an action to
abate threat $k$ in planning unit $i$ has been selected $(1)$ or not
$(0)$.
$c_{ik}$ is the cost of the action to abate the threat $k$ in the
planning unit $i$.
$b_{is}(x)$ is the benefit of the feature $s$ in the planning unit $i$
(ranging between $0$ and $1$).
$t_s$ is the recovery target for feature $s$.
$f(x)$ is the function of connectivity penalty.
## [**Q3.**]{style="color: #B93600;"} Optimization objective?
#### Minimize Costs (**`minimizeCosts`**) vs Maximize Benefits (**`maximizeBenefits`**)
<br>
<center>{.class width="80%"
height="80%"}</center>
$x_{ik}$ is the decisions variable that specifies whether an action to
abate threat $k$ in planning unit $i$ has been selected $(1)$ or not
$(0)$.
$c_{ik}$ is the cost of the action to abate the threat $k$ in the
planning unit $i$.
$b_{is}(x)$ is the benefit of the feature $s$ in the planning unit $i$
(ranging between $0$ and $1$).
$B$ is the budget available.
$f(x)$ is the function of connectivity penalty.
## Workflow
<center>{.class width="80%"
height="80%"}</center>
# II) Create mathematical model
## Create the model (`problem()`)
Create an **optimization model** for the multi-action conservation
planning problem, following the mathematical formulations used in
Salgado-Rojas et al. (2020).
<br>
```{r model1, eval=FALSE, include=TRUE}
problem(
x,
model_type = "minimizeCosts",
budget = 0,
blm = 0
)
```
## Workflow
<center>{.class width="80%"
height="80%"}</center>
# III) Solve model
## Solve model (`solve()`)
Solves the **optimization model** associated with the multi-action
conservation planning problem. This function is used to solve the
mathematical model created by the `problem()` function.
<br>
```{r solve1, eval=FALSE, include=TRUE}
solve(
a,
solver = "",
gap_limit = 0,
time_limit = .Machine$integer.max,
solution_limit = FALSE,
cores = 2,
verbose = TRUE,
name_output_file = "output",
output_file = TRUE
)
```
## Solve model (`solve()`)
### [**solver**]{style="color: #4385CD;"} (`gurobi, cplex, symphony`)<br>
<br>
<div>
<center>
**Rsymphony** <br>
<center>
<img src="Figures/cplex-gurobi.webp" style="vertical-align:middle" width="50%" height="50%"/>
</div>
## Solve model (`solve()`)
### [**gap limit**]{style="color: #4385CD;"} (percentage) <br>
<center>{.class
width="60%" height="60%"}</center>
## Solve model (`solve()`)
### [**time limit**]{style="color: #4385CD;"} (seconds) <br>
### [**solution_limit**]{style="color: #4385CD;"} (First solution?) <br>
### [**verbose**]{style="color: #4385CD;"} (Information on screen?) <br>
## Overview
<center>{.class width="80%"
height="80%"}</center>
# Getting information of solutions
## `getActions()`
Returns the spatial deployment of the **actions for each planning unit**
of the corresponding solution.
<br>
```{r info1, eval=FALSE, include=TRUE}
getActions(x, format = "wide")
```
Output example,
```{r info2, eval=FALSE, include=TRUE}
## solution_name pu 1 2 conservation connectivity
## 1 sol 1 0 0 0 0
## 2 sol 2 0 0 0 0
## 3 sol 3 0 0 0 0
## 4 sol 4 0 0 0 0
## 5 sol 5 0 0 0 0
## 6 sol 6 0 0 0 0
```
## `getSolutionBenefit()`
Returns the **total benefit** induced by the corresponding solution. The
total benefit is computed as the sum of the benefits obtained, for all
features, across all the units in the planning area.
<br>
```{r info3, eval=FALSE, include=TRUE}
getSolutionBenefit(x, type = "total")
```
Output example,
```{r info4, eval=FALSE, include=TRUE}
## solution_name feature benefit.conservation benefit.recovery benefit.total
## 1 sol 1 0 11 11
## 2 sol 2 0 16 16
## 3 sol 3 0 10 10
## 4 sol 4 0 9 9
```
## `getCost()`
Provides the sum of **costs** to actions and monitoring applied in a
solution.
<br>
```{r info5, eval=FALSE, include=TRUE}
getCost(x)
```
Output example,
```{r info6, eval=FALSE, include=TRUE}
## solution_name monitoring threat_1 threat_2
## 1 sol 61 20 65
```
# Outline
- Features
- ::: white
**Toy example**
:::
- Overview
- Actual case study: The Mitchell River, Australia
- Shiny Application
## Package installation and documentation
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[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/prioriactions/prioriactions/actions)
[](https://cran.rstudio.com/web/packages/prioriactions/index.html)
<!-- badges: end -->
Package `prioriactions` can be found at
[CRAN](https://cloud.r-project.org/web/packages/prioriactions/index.html),
so it can be installed using:
```{r toyExample1, eval=FALSE, include=TRUE}
install.packages("prioriactions")
```
Latest stable versions can be downloaded and installed from GitHub as
follows (package `remotes` should be installed first):
```{r toyExample2, eval=FALSE, include=TRUE}
if (!require(remotes)) install.packages("remotes")
remotes::install_github("prioriactions/prioriactions")
```
So, we load the `prioriactions` package.
```{r toyExample3, eval=TRUE, include=TRUE}
# load package
library(prioriactions)
```
## Toy Example
##### (Available in the [Get Started](https://prioriactions.github.io/prioriactions/articles/prioriactions.html) vignette on the `prioriactions` website)
<br />
- This example contains **100 planning units**, **4 features** and **2
threats**.
- The distribution of features and threats can be plotted on a grid of
10 x 10 units.
- `prioriactions` contains this simulated example inside the setup
files. You can extract it by the `data()` function.
## #1 Planning units data
The monitoring cost values ranging from 1 to 10 and all status of 0 (not
locked).
```{r toyExample4, eval=TRUE, message=FALSE, include=TRUE}
# load planning unit data from prioriactions
data(sim_pu_data) #To load simulated data
#head(sim_pu_data)
```
```{r toyExample5, echo=FALSE, message=FALSE}
library(DT)
datatable(sim_pu_data, rownames = FALSE, options = list(pageLength = 5))
```
## #1 Planning units data
A `RasterLayer` object can be used to present this spatial information.
The pixel values correspond to the **monitoring costs** of each planning
unit.
```{r toyExample6, eval=TRUE, message=FALSE, include=TRUE, out.width="60%"}
library(raster) #To plot rasters
r <- raster(ncol=10, nrow=10, xmn=0, xmx=10, ymn=0, ymx=10)
values(r) <- sim_pu_data$monitoring_cost
plot(r)
```
## #2 Features data
Contains information about the **features** such as its *id* and
*targets* (mandatory when `minimizing costs`).
```{r toyExample7, eval=TRUE, message=FALSE, include=TRUE}
# load features data from prioriactions
data(sim_features_data)
#head(sim_features_data)
```
```{r toyExample8, echo=FALSE, message=FALSE}
datatable(sim_features_data, rownames = FALSE, options = list(pageLength = 5))
```
## #3 Features distribution data
Contains information on the **spatial distribution of these features**
across planning units.
```{r toyExample9, eval=TRUE, message=FALSE, include=TRUE}
# load features data from prioriactions
data(sim_dist_features_data)
#head(sim_features_data)
```
```{r toyExample10, echo=FALSE, message=FALSE}
datatable(sim_dist_features_data, rownames = FALSE, options = list(pageLength = 5))
```
## #3 Features distribution data
To plot the spatial distribution of the first feature,
```{r toyExample10_1, eval=TRUE, message=FALSE, include=TRUE}
# load amount of features data
features <- reshape2::dcast(sim_dist_features_data,
pu~feature,
value.var = "amount",
fill = 0)
values(r) <- features$`1`
plot(r)
```
## #3 Features distribution data
<center>{.class width="80%"
height="80%"}</center>
## #4 Threats data
Provides information about the threats such as their `id` and `name`.
```{r toyExample11, eval=TRUE, message=FALSE, include=TRUE}
# load threats data from prioriactions
data(sim_threats_data)
```
```{r toyExample12, echo=FALSE, message=FALSE}
datatable(sim_threats_data, rownames = FALSE, options = list(pageLength = 5))
```
## #5 Threats distribution data
Provides information on the **spatial distribution** of these threats
```{r toyExample13, eval=TRUE, message=FALSE, include=TRUE}
# load threats data from prioriactions
data(sim_dist_threats_data)
```
```{r toyExample14, echo=FALSE, message=FALSE}
datatable(sim_dist_threats_data, rownames = FALSE, options = list(pageLength = 5))
```
## #5 Threats distribution data
<center>{.class width="80%"
height="80%"}</center>
## #6 Sensitivity data
Indicates which features is sensitive to what threat.
```{r toyExample15, eval=TRUE, message=FALSE, include=TRUE}
# load threats data from prioriactions
data(sim_sensitivity_data)