Pretty-print method for morie_tps_result objects.
Usage
# S3 method for class 'morie_tps_result'
print(x, ...)Examples
# \donttest{
set.seed(1)
df <- data.frame(OCC_YEAR = rep(2014:2023, each = 30),
OCC_MONTH = sample(month.name, 300, TRUE),
HOOD_158 = sample(sprintf("%03d", 1:20), 300, TRUE),
LAT_WGS84 = runif(300, 43.6, 43.8),
LONG_WGS84 = runif(300, -79.5, -79.2))
dfs <- list(a = df, b = df)
res <- try(morie_tps_composite_index(dfs))
if (!inherits(res, "try-error")) str(res, max.level = 1)
#> List of 8
#> $ title : chr "TPS -- composite crime-risk index per neighbourhood"
#> $ call : chr "morie_tps_composite_index(dfs)"
#> $ summary_lines :List of 5
#> $ tables :List of 2
#> $ warnings : chr(0)
#> $ interpretation: chr "Composite is the unweighted (or weighted) sum of z-standardised counts across all loaded TPS categories. Posit"| __truncated__
#> $ payload :List of 4
#> $ sections : list()
#> - attr(*, "class")= chr [1:3] "morie_tps_result" "morie_rich_result" "list"
print(res)
#> TPS -- composite crime-risk index per neighbourhood
#> ===================================================
#> Call: morie_tps_composite_index(dfs)
#>
#> Categories used 2
#> Neighbourhoods scored 20
#> Mean weight 1
#> Mean composite 0
#> Max composite 3.31
#>
#> Top 25 neighbourhoods (highest composite):
#> HOOD_158 Composite z-sum
#> 005 3.31
#> 019 2.364
#> 020 2.364
#> 013 1.891
#> 001 1.418
#> 004 1.418
#> 016 1.418
#> 002 0.946
#> 006 0.946
#> 017 0.946
#> 008 0
#> 003 -0.473
#> 015 -0.473
#> 011 -0.946
#> 014 -1.418
#> 009 -2.364
#> 012 -2.364
#> 007 -2.837
#> 018 -2.837
#> 010 -3.31
#>
#> Bottom 25 neighbourhoods (lowest composite):
#> HOOD_158 Composite z-sum
#> 010 -3.31
#> 018 -2.837
#> 007 -2.837
#> 012 -2.364
#> 009 -2.364
#> 014 -1.418
#> 011 -0.946
#> 015 -0.473
#> 003 -0.473
#> 008 0
#> 017 0.946
#> 006 0.946
#> 002 0.946
#> 016 1.418
#> 004 1.418
#> 001 1.418
#> 013 1.891
#> 020 2.364
#> 019 2.364
#> 005 3.31
#>
#> Composite is the unweighted (or weighted) sum of z-standardised counts across all loaded TPS categories. Positive = neighbourhood with elevated incidence across the included crime types; near-zero = average; negative = below-average exposure across categories.
# }