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.

run_calculations(x)

Arguments

x

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

Value

Invisible TRUE indicating success.

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.

Examples

# \dontrun{
# 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
#>    user  system elapsed 
#>   1.267   0.001   1.319 
print(s2) # time spent after running preliminary calculations
#>    user  system elapsed 
#>   1.197   0.012   1.239 

# 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.
# }