First of all, thanks for thinking about contributing to the prioritizr R package! This is an open source project, and contributions are extremely welcome.

## How you can contribute

There are several ways you can contribute to this project. If you want to know more about why and how to contribute to open source projects like this one, see this Open Source Guide.

### Ask a question

Using the package and got stuck? Browse the documentation to see if you can find a solution. Still stuck? Post your question as an issue on GitHub. While we cannot offer user support, we’ll try to do our best to address it, as questions often lead to better documentation or the discovery of bugs.

Want to ask a question in private? Please feel free to email the package developers Jeffrey Hanson or Richard Schuster.

### Propose an idea

Have an idea for a new feature? Take a look at the documentation and issue tracker to see if it isn’t included or suggested yet. If not, suggest your idea as an issue on GitHub. While we can’t promise to implement your idea, it helps to:

• Explain in detail how it would work.
• Keep the scope as narrow as possible.

See below if you want to contribute code for your idea as well.

### Report a bug

Using the package and discovered a bug? That’s annoying! Don’t let others have the same experience and report it as an issue on GitHub so we can fix it. A good bug report makes it easier for us to do so, so please include:

• Your operating system name and version (e.g., Mac OS 10.13.6).
• Details about your local setup that might be helpful in troubleshooting, such as your session information (via sessionInfo()).
• Detailed steps to reproduce the bug.

### Improve the documentation

Noticed a typo on the website? Think a function could use a better example? Good documentation makes all the difference, so your help to improve it is very welcome!

#### The website

This website is generated using the pkgdown R package package. That means we don’t have to write any html: content is pulled together from documentation in the code, vignettes, Markdown files, the package DESCRIPTION and _pkgdown.yml settings. If you know your way around pkgdown, you can propose a file change to improve documentation. If not, report an issue and we can point you in the right direction.

## Learning resources

If you’re new too R package development, we recommend reading the Advanced R book. Since a large part of the code base is written in the C++ programming language using the Rcpp R package, we also recommend reading the Rcpp for everyone book. Both of these are books are free, online, and absolutely fantastic resources.

#### Function documentation

Functions are described as comments near their code and translated to documentation using the roxygen2 R package package. If you want to improve a function description:

1. Go to R/ directory in the code repository.
2. Look for the file with the name of the function.
3. Propose a file change to update the function documentation in the roxygen comments (starting with #').

### Contribute code

Care to fix bugs or implement new functionality? That’s brilliant! Have a look at the issue list and leave a comment on the things you want to work on. See also the development guidelines below. Please note that we use American spelling for the package, and follow the tidyverse style guide.

## Development guidelines

We try to follow the GitHub flow for development.

1. Fork this repo and clone it to your computer. To learn more about this process, see this guide.
2. If you have forked and cloned the project before and it has been a while since you worked on it, pull changes from the original repo to your clone by using git pull upstream master.
3. Open the RStudio project file (.Rproj).
4. Make your changes:
• Write your code.
• Test your code (bonus points for adding unit tests).
• Document your code (see function documentation above).
• Check your code with devtools::check() and aim for 0 errors and warnings.
5. Commit and push your changes.
6. Submit a pull request.

## Internal design

Here we describe the internal design of the package. A ConservationProblem class (defined in R/ConservationProblem-class.R) is used to store the data and problem formulation of the conservation planning problem. Additionally, an OptimizationProblem class (defined in R/OptimizationProblem-class.R) is used to store the precise mathematical formulation of a conservation planning problem. When a ConservationProblem object is solved (using the solve() function), the ConservationProblem object is first compiled into an OptimizationProblem object (using the compile() function) and the OptimizationProblem object is then solved (using the solve() function). This distinction is important because if you want to add a new objective, constraint, penalty, or decision function, then you’ll need to write code that adds the mathematical formulation of the function to a OptimizationProblem object.

All OptimizationProblem objects are simply a wrapper to an external pointer pointer (XPtr) that points to a C++ OPTIMIZATIONPROBLEM class object (defined in src/optimization_problem.h). The C++ OPTIMIZATIONPROBLEM class contains the standard data needed to formulate a mixed integer linear programming problem (i.e., model sense, problem objective , constraint matrix, constraint senses, right-hand-side values, variable decision types, and variable bounds). This class also contains additional data that pertain to conservation problems (i.e., number of features, planning units, and zones). If you’re is unfamiliar with the standard representation of a mixed integer linear programming problem, we encourage them to read the documentation for the gurobi R package. The fields A_i, A_j, and A_x correspond the row, column, and cells values for the constraint matrix of the optimization problem (respectively). The other fields follow standard conventions. Note that the constraint matrix is, ultimately, constructed as a Matrix::sparseMatrix() so row and column indices do not need to be sequential. Additionally, all C++ constraint and penalty functions should be independent. In other words, though it may be computationally efficient to reuse constraints and variables encoded in other functions, each C++ constraint and penalty function define its own constraints and variables. All conservation planning problems are defined following one of two standard mathematical formulations: the compressed and the expanded formulation.

The compressed formulation defines a problem which assumes that all instances of a conserved feature are used to achieve the target or count towards the benefit of a solution. Although the expanded formulation can provide identical solutions to the compressed formulation, the compressed formulation is provided because it is simpler and can be used with much larger sized problems. Currently, all constraints use the compressed formulation except for the add_feature_contiguity_constraints() function. Under this formulation, the first set of decision variables (the first number of planning units × number of zones) always pertain to the state of the planning units. Thus in a problem containing 3 planning units and 2 zones, the first six variables indicate the allocation of: planning unit 1 in zone 1, planning unit 2 in zone 1, planning unit 3 in zone 1, planning unit 1 in zone 2, planning unit 2 in zone 2, planning unit 3 in zone 2. The first set of constraints (rows) always correspond to each target (noting that some objectives use “fake” targets to initialize the feature by planning unit data, see R/compile.R). These rows, which each correspond to a single target, contain the amount of each feature in each zone for which the target pertains. Thus rows for targets which pertain to a single zone will only contain feature abundance data for planning units (columns) in a single zone, and rows for targets which pertain to a single feature in multiple zones will contain feature abundance data for planning units (columns) in multiple zones.

To help illustrate the compressed formulation, consider the following problem:

# simulate data
pu <- data.frame(id = 1:3, cost_1 = 4:6, cost_2 = 7:9)
zone <- data.frame(id = 1:2, name = c("z1", "z2"))
feature <- data.frame(id = 1:2, name = c("f1", "f2"))
rij <- expand.grid(pu = 1:3, species = 1:2, zone = 1:2)
rij$amount <- seq_len(nrow(rij)) + nrow(rij) targets <- matrix(1, nrow = 2, ncol = 2) # create problem p <- problem(pu, feature, rij, c("cost_1", "cost_2"), zone) %>% add_absolute_targets(targets) # print problem print(p) ## A conservation problem (<ConservationProblem>) ## ├•data ## │├•zones: "z1" and "z2" (2 total) ## │├•features: "f1" and "f2" (2 total) ## │└•planning units: ## │ ├•data: <data.frame> (3 total) ## │ ├•costs: continuous values (between 4 and 9) ## │ ├•extent: NA ## │ └•CRS: NA ## └•formulation ## ├•objective: none specified ## ├•penalties: none specified ## ├•targets: absolute targets (between 1 and 1) ## ├•constraints: none specified ## ├•decisions: binary decision ## ├•portfolio: shuffle portfolio (number_solutions = 1, …) ## └•solver: gurobi solver (gap = 0.1, time_limit = 2147483647, first_feasible = FALSE, …) ## # ℹ Use summary(...) to see complete formulation. The compressed formulation expresses the planning unit and feature data using the following constraint matrix. Here, each variable (column) corresponds to a different planning unit and a different zone allocation, each constraint (row) corresponds to a different target, and each cell corresponds to the amount of a each feature in each planning unit given a different zone (based on the rij data and the targets). ## 4 x 6 sparse Matrix of class "dgCMatrix" ## pu1_z1 pu2_z1 pu3_z1 pu1_z2 pu2_z2 pu3_z2 ## target_f1_z1 13 14 15 . . . ## target_f2_z1 16 17 18 . . . ## target_f1_z2 . . . 19 20 21 ## target_f2_z2 . . . 22 23 24 The expanded formulation, on the other hand, defines a problem which can allow for some instances of conserved features to not be used for achieving the targets or maximizing the conservation benefit. This formulation is a generalized version of the compressed formulation. It contains additional variables (columns) and constraints (rows) for each combination of feature, planning unit, and zone that indicate if a given planning unit allocated to a specific zone is also allocated to conserve a given feature. Given the previous problem, the expanded formulation expresses the planning unit and feature data in the constraint matrix as: # generate targets targets2 <- p$targets$output() # create matrix m <- matrix( NA, ncol = (p$number_of_zones() * p$number_of_planning_units()) + (p$number_of_zones() * p$number_of_planning_units() * p$number_of_features()),
nrow =
(p$number_of_zones() * p$number_of_planning_units() *
p$number_of_features()) + (p$number_of_features() * p$number_of_zones()) ) # add row names rownames(m) <- c( paste0( "pu", rep( seq_len(p$number_of_planning_units()),
p$number_of_zones() * p$number_of_features()
),
"_",
rep(
rep(p$feature_names(), each = p$number_of_planning_units()),
p$number_of_zones() ), "_", rep( p$zone_names(),
each = p$number_of_planning_units() * p$number_of_features()
)
),
paste0(
"target_",
rep(
p$feature_names(), p$number_of_zones()), "_", rep(p$zone_names(), each = p$number_of_features()
)
)
)

# add column names
colnames(m) <- c(
paste0(
"pu",
rep(seq_len(p$number_of_planning_units()), p$number_of_zones()),
"_",
rep(p$zone_names(), each = p$number_of_planning_units())
),
paste0(
"pu",
rep(
seq_len(p$number_of_planning_units()), p$number_of_zones() * p$number_of_features() ), "_", rep( rep(p$feature_names(), each = p$number_of_planning_units()), p$number_of_zones()
),
"_",
rep(
p$zone_names(), each = p$number_of_planning_units() *
p$number_of_features() ) ) ) # add in indicator variables and constraints curr_row <- 0 for (z in seq_len(p$number_of_zones())) {
for (i in seq_len(p$number_of_features())) { for (j in seq_len(p$number_of_planning_units())) {
curr_row <- curr_row + 1
curr_col1 <- ((z - 1) * p$number_of_planning_units()) + j curr_col2 <- (p$number_of_planning_units() * p$number_of_zones()) + ((z - 1) * p$number_of_features() * p$number_of_planning_units()) + ((i - 1) * p$number_of_planning_units()) + j
m[curr_row, curr_col1] <- -1
m[curr_row, curr_col2] <- 1
}
}
}

# add in targets
for (i in seq_len(nrow(targets2))) {
# extract indices
curr_row <- curr_row + 1
curr_feature <- targets2$feature[i] curr_zone <- targets2$zone[i][[1]]
curr_cols <-
(p$number_of_planning_units() * p$number_of_zones()) +
((curr_zone - 1) * p$number_of_features() * p$number_of_planning_units()) +
((curr_feature - 1) * p$number_of_planning_units()) + seq_len(p$number_of_planning_units())
curr_amount <-
rij$amount[unlist(rij$zone) == curr_zone & rij\$species == curr_feature]
# set matrix values
m[curr_row, curr_cols] <- curr_amount
}

# convert to sparse matrix
m[is.na(m)] <- 0
m <- as(m, "sparseMatrix")

# print matrix
print(m)
## 16 x 18 sparse Matrix of class "dgCMatrix"

##   [[ suppressing 18 column names 'pu1_z1', 'pu2_z1', 'pu3_z1' ... ]]

##
## pu1_f1_z1    -1  .  .  .  .  .  1  .  .  .  .  .  .  .  .  .  .  .
## pu2_f1_z1     . -1  .  .  .  .  .  1  .  .  .  .  .  .  .  .  .  .
## pu3_f1_z1     .  . -1  .  .  .  .  .  1  .  .  .  .  .  .  .  .  .
## pu1_f2_z1    -1  .  .  .  .  .  .  .  .  1  .  .  .  .  .  .  .  .
## pu2_f2_z1     . -1  .  .  .  .  .  .  .  .  1  .  .  .  .  .  .  .
## pu3_f2_z1     .  . -1  .  .  .  .  .  .  .  .  1  .  .  .  .  .  .
## pu1_f1_z2     .  .  . -1  .  .  .  .  .  .  .  .  1  .  .  .  .  .
## pu2_f1_z2     .  .  .  . -1  .  .  .  .  .  .  .  .  1  .  .  .  .
## pu3_f1_z2     .  .  .  .  . -1  .  .  .  .  .  .  .  .  1  .  .  .
## pu1_f2_z2     .  .  . -1  .  .  .  .  .  .  .  .  .  .  .  1  .  .
## pu2_f2_z2     .  .  .  . -1  .  .  .  .  .  .  .  .  .  .  .  1  .
## pu3_f2_z2     .  .  .  .  . -1  .  .  .  .  .  .  .  .  .  .  .  1
## target_f1_z1  .  .  .  .  .  . 13 14 15  .  .  .  .  .  .  .  .  .
## target_f2_z1  .  .  .  .  .  .  .  .  . 16 17 18  .  .  .  .  .  .
## target_f1_z2  .  .  .  .  .  .  .  .  .  .  .  . 19 20 21  .  .  .
## target_f2_z2  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 22 23 24