things to add to summary output

[njtierney]

The reduction in average and SD distances

The amount of resources used? Is there any left over?

Maybe make something clearer about the facilities added and removed:

• Removed = facilities removed from existing locations
• Added = facilities added from proposed locations.

[njtierney]

OK so the previous version used the additional change in n_cov and pct_cov.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(maxcovr)
# subset to be the places with towers built on them.
york_selected <- york %>% filter(grade == "I")
york_unselected <- york %>% filter(grade != "I")
# OK, what if I just use some really crazy small data to optimise over.
#
mc_relocate <-  max_coverage_relocation(existing_facility = york_selected,
                                     proposed_facility = york_unselected,
                                     user = york_crime,
                                     distance_cutoff = 100,
                                     cost_install = 5000,
                                     cost_removal = 200,
                                     cost_total = 600000)
mc_relocate
#> 
#> ----------------------------------------- 
#> Model Fit: maxcovr relocation model 
#> ----------------------------------------- 
#> model_used:        max_coverage_relocation 
#> existing_facility: york_selected 
#> proposed_facility: york_unselected 
#> user:              york_crime 
#> distance_cutoff:   100 
#> cost_install:      5000 
#> cost_removal:     200 
#> cost_total:        6e+05 
#> solver:            lpSolve 
#> -----------------------------------------
summary(mc_relocate)
#> 
#> --------------------------------------- 
#> Model Fit: maxcovr relocation model 
#> --------------------------------------- 
#> Distance Cutoff: 100m 
#> Facilities: 
#>     Added:       103 
#>     Removed:     2 
#> Coverage (Additional): 
#>     # Users:     693   (354) 
#>     Proportion:  0.382 (0.1951) 
#> Distance (m) to Facility: 
#>        Avg:      560 
#>        SD:       719 
#> Costs: 
#>     Total:       6e+05 
#>     Install:     5000 
#>     Removal:  200 
#> ---------------------------------------

[njtierney]

And here is the new implementation, showing (previous).

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(maxcovr)
# subset to be the places with towers built on them.
york_selected <- york %>% filter(grade == "I")
york_unselected <- york %>% filter(grade != "I")
# OK, what if I just use some really crazy small data to optimise over.
#
mc_relocate <-  max_coverage_relocation(existing_facility = york_selected,
                                     proposed_facility = york_unselected,
                                     user = york_crime,
                                     distance_cutoff = 100,
                                     cost_install = 5000,
                                     cost_removal = 200,
                                     cost_total = 600000)
mc_relocate
#> 
#> ----------------------------------------- 
#> Model Fit: maxcovr relocation model 
#> ----------------------------------------- 
#> model_used:        max_coverage_relocation 
#> existing_facility: york_selected 
#> proposed_facility: york_unselected 
#> user:              york_crime 
#> distance_cutoff:   100 
#> cost_install:      5000 
#> cost_removal:     200 
#> cost_total:        6e+05 
#> solver:            lpSolve 
#> -----------------------------------------
summary(mc_relocate)
#> 
#> --------------------------------------- 
#> Model Fit: maxcovr relocation model 
#> --------------------------------------- 
#> Distance Cutoff: 100m 
#> Facilities: 
#>     Added:       103 
#>     Removed:     2 
#> Coverage (Previous): 
#>     # Users:     693   (339) 
#>     Proportion:  0.382 (0.1869) 
#> Distance (m) to Facility (Previous): 
#>        Avg:      560 (1400) 
#>        SD:       719 (1597) 
#> Costs: 
#>     Total:       6e+05 
#>     Install:     5000 
#>     Removal:  200 
#> ---------------------------------------