run_calculations: Run calculations

View source: R/run_calculations.R

run_calculationsR Documentation

Run calculations

Description

Execute preliminary calculations in a conservation problem and store the results for later use. This function is useful when creating slightly different versions of the same conservation planning problem that involve the same pre-processing steps (e.g., calculating boundary data), because means that the same calculations will not be run multiple times.

Usage

run_calculations(x)

Arguments

x

problem() (i.e., ConservationProblem) object.

Details

This function is used for the effect of modifying the input ConservationProblem object. As such, it does not return anything. To use this function with pipe() operators, use the %T>% operator and not the %>% operator.

Value

Invisible TRUE indicating success.

Examples

## Not run: 
# Let us imagine a scenario where we wanted to understand the effect of
# setting different targets on our solution.

# create a conservation problem with no targets
p <- problem(sim_pu_raster, sim_features) %>%
     add_min_set_objective() %>%
     add_boundary_penalties(10, 0.5) %>%
     add_binary_decisions() %>%
     add_default_solver(verbose = FALSE)

# create a copies of p and add targets
p1 <- p %>% add_relative_targets(0.1)
p2 <- p %>% add_relative_targets(0.2)
p3 <- p %>% add_relative_targets(0.3)

# now solve each of the different problems and record the time spent
# solving them
s1 <- system.time({solve(p1); solve(p2); solve(p3)})

# This approach is inefficient. Since these problems all share the same
# planning units it is actually performing the same calculations three times.
# To avoid this, we can use the "run_calculations" function before creating
# the copies. Normally, R runs the calculations just before solving the
# problem

# recreate a conservation problem with no targets and tell R run the
# preliminary calculations. Note how we use the %T>% operator here.
p <- problem(sim_pu_raster, sim_features) %>%
     add_min_set_objective() %>%
     add_boundary_penalties(10, 0.5) %>%
     add_binary_decisions() %>%
     add_default_solver(verbose = FALSE) %T>%
     run_calculations()

# create a copies of p and add targets just like before
p1 <- p %>% add_relative_targets(0.1)
p2 <- p %>% add_relative_targets(0.2)
p3 <- p %>% add_relative_targets(0.3)

# solve each of the different problems and record the time spent
# solving them
s2 <- system.time({solve(p1); solve(p2); solve(p3)})

# now lets compare the times
print(s1) # time spent without running preliminary calculations
print(s2) # time spent after running preliminary calculations

# As we can see, we can save time by running the preliminary
# calculations before making copies of the problem with slightly
# different constraints. Although the time saved in this example
# is rather small, this is because the example data are very small.
# We would expect larger time savings for larger datasets.

## End(Not run)

prioritizr documentation built on Sept. 18, 2022, 1:05 a.m.