Make the process of generating tables from output easier! Provide a few default tables that handle all of the output post-processing in the background.

Currently, loss computations for economic parameters provided via input list (ie, displacement, business_income, rental_income, and sc) are hard-coded:

pv_loss <- function(model, p) {
    ## Purpose:
    ## Calculate losses for each model
    return(
        model %>%
        ## NB: assumes total_area column has been created
        dplyr::mutate(
                   displacement=p$displacement*p$tenant*re_occupancy_time*total_area,
                   business_income=(1 - p$recapture)*p$bi*functional_recovery_time*total_area,
                   rental_income=p$ri*functional_recovery_time*total_area
               ) %>%
        dplyr::mutate(sc=(p$sc * business_income))
        )
}

To allow flexibility:

1. the input list should include an econ sub-list with desired economic parameters
2. the computations would iterate through the named econ sub-list and extract the names from the list

Current function plot_frbca includes code to post-process output to prepare for plotting. This could might be useful to have in a standalone function as it might help to have the output in a specific format for tables.

Currently, the package expects explicit input formats for two tables: one for EALs (e.g., eal.csv) and one for costs (e.g., cost.csv).

Not only do the expected input tables need to be added to documentation, but also the expected formats (minimum amount of information, column names, and format/types).

Current plot functions are kludgey. Needs

• Wrapper to streamline plotting as inputs are the same (same goes for postprocessing code)
• Global plot theme (so this isn't getting done ad hoc with each plot) #14
• Plotting other metrics
• EDIT: filtering by model list should actually be by design (intervention); need to update preprocessing to create this column, with plain english names, for easy plotting (as this is getting done ad hoc for plotting from the outputs)

Current preprocessing_model nests systems [j] within stories [i].

As a result, when running the analysis for multiple systems:

out <- frbca(eal_multiple_systems, cost_multiple_systems, params)

the output out is nested by stories rather than systems. In other words, out is a list with the length length(out) = number of unique story heights represented in the data.

It should be the other way around: nest stories within systems, so that length(out) = number of unique structural systems.

1. In addition to BCR and NPV, add (a) AROI and (b) IRR
2. Generalize NPV calculation to be reusable (ie, wrap in a function, parameterize, and use to calculate IRR)

Add separate function to plot (or create a table of) cost deltas

Updating input schema to include:

• system: structural system name (eg, RCMF)
• num_stories: number of stories (eg, 4)
• design_s: explicit designation of structural intervention (eg, RCIV)
• design_ns: explicit designation of nonstructural intervention (eg, ns16 for the 16th nonstructural design iteration documented in the paper)
• model: concatenation of system-num_stories-design_s-design_ns (eg, RCMF-4-RCIV-baseline)

Need to update:

• preprocess_model function, which assumes a different schema
• README

Need to decide if analyses will continue to be run for a fixed system (the current implicit assumption) or whether to add something to preprocessing to allow a single analysis of multiple systems:

• This would likely necessitate preprocess_model creates nested submodels for analyses; currently submodels are separated by num_stories but can also nest that within system:

## OLD:
    stories <- dat |> dplyr::distinct(num_stories) |> pull()
    for (i in 1:length(stories)) {
        models[[i]] <- dat |>
            dplyr::filter(num_stories == stories[i])
    }

## NEW:
    systems <- dat |> dplyr::distinct(system) |> pull()
    stories <- dat |> dplyr::distinct(num_stories) |> pull()
    for (j in 1:length(systems)) {
        for (i in 1:length(stories)) {
            models[[j]][[i]] <- dat |>
                dplyr::filter(system == systems[j] & num_stories == stories[i])
        }
    }

Will need to check if any of the remaining functions assume a single-level submodel structure, in which case they will need to be modified to iterate through all levels, or whether they work as is (unlikely)

eg, via function: https://stackoverflow.com/questions/50741128/set-a-theme-and-palette-for-all-plots

also consider adding textures via ggpattern package: https://coolbutuseless.github.io/package/ggpattern/articles/geom-gallery-geometry.html#b-w-example

Default parameter list should be provided, as the model expects a lot of parameters.

Code is currently relying on maggrittr pipe %>% but not needed anywhere. Use base R pipe |> introduced in R 4.1

Related to #9, add computation for a new loss category (loss of value added, analogous to business income):

## old
  } else if (loss_name == 'loss_business_income') {

## new
  } else if (grepl('(business_income|value_added)', loss_name) {

Provide example input files (EALs and Costs), as well as reasonable defaults for parameters (see #4) in order to demonstrate use (either in README or in a vignette)

While IRR results in 0 for status quo (per error handling), AROI results in NAN.

Should both result in NAN or in 0?

Building on #6 , need separate functions to plot bar charts of EALs under (a) baseline and (b) interventions.

Placeholder added: e5d1516

Rental income depends on reoccupancy time (assuming owners can collect rent even if tenant has not returned) but displacement depends on recovery time (tenants likely won't return until building is functional). Currently it's reversed so it needs to be flipped.

Nonstructural costs are discounted relative to base year, but this discounting is hard-coded:

Similarly, the total cost then adjusts nonstructural costs for maintenance over the life of the building...but again, this is hard-coded (it should be 0.5 * T):

We now have (1) BCR (2) NPV (3) AROI (4) IRR. Need to tabulate each metric somehow (currently only doing BCR).

Depends on #4 and #5. Create a vignette to provide more illustrative documentation of how an end-to-end analysis might look.

Currently, model story height is pulled from the model_name column:

Input tables should have explicit story-height column and story height should be pulled from there.