Wrapper around find_clusters() where only a data frame in the usual
location, date, count format, a threshold value (distance limit), and the
geographic resolution is required. The appropriate distance list will be
auto created based on detected the states/locations given the data frame
location column. The result can be returned as json, if desired
Arguments
- df
The data frame with location (char), date (IDate), and count (int) columns
- threshold_val
The cluster threshold (int) in miles
- level
Can be "zip" or "county"
- return_json
set to TRUE to return json
- ...
other arguments passed on to
find_clusters()
Examples
find_clusters_by_df(example_count_data, 50, "county", return_json = FALSE)
#> $cluster_alert_table
#> Key: <cluster_center>
#> cluster_center cluster_start_date cluster_end_date cluster_max_distance
#> <char> <Date> <Date> <num>
#> 1: 39003 2025-01-30 2025-02-05 0.00000
#> 2: 39005 2025-01-30 2025-02-05 0.00000
#> 3: 39009 2025-01-30 2025-02-05 0.00000
#> 4: 39015 2025-02-04 2025-02-05 17.32111
#> 5: 39017 2025-01-30 2025-02-05 0.00000
#> 6: 39039 2025-02-01 2025-02-05 0.00000
#> 7: 39061 2025-02-02 2025-02-05 0.00000
#> 8: 39081 2025-02-04 2025-02-05 0.00000
#> 9: 39109 2025-02-04 2025-02-05 24.96767
#> 10: 39141 2025-01-31 2025-02-05 22.46182
#> cluster_center_observed observed expected log_obs_exp threshold alert_gap
#> <int> <int> <num> <num> <num> <num>
#> 1: 335 335 191.48001 0.5593471 0.2113664 0.34798069
#> 2: 166 166 74.13597 0.8060870 0.3304438 0.47564318
#> 3: 215 215 84.90008 0.9291630 0.2769749 0.65218806
#> 4: 21 80 38.61254 0.7284495 0.4995393 0.22891024
#> 5: 280 280 209.67288 0.2892410 0.2330866 0.05615438
#> 6: 67 67 36.21820 0.6151308 0.5758212 0.03930963
#> 7: 287 287 197.56610 0.3734090 0.2297259 0.14368309
#> 8: 37 37 14.60289 0.9296987 0.8068677 0.12283098
#> 9: 36 399 262.76238 0.4177113 0.1928263 0.22488502
#> 10: 79 160 102.87914 0.4416189 0.3385027 0.10311621
#> alert_ratio n_cluster_locations
#> <num> <int>
#> 1: 2.646338 1
#> 2: 2.439407 1
#> 3: 3.354683 1
#> 4: 1.458243 2
#> 5: 1.240916 1
#> 6: 1.068267 1
#> 7: 1.625454 1
#> 8: 1.152232 1
#> 9: 2.166257 5
#> 10: 1.304625 3
#>
#> $cluster_location_counts
#> location count cluster_center
#> <char> <int> <char>
#> 1: 39009 215 39009
#> 2: 39005 166 39005
#> 3: 39003 335 39003
#> 4: 39015 21 39015
#> 5: 39025 59 39015
#> 6: 39021 25 39109
#> 7: 39037 26 39109
#> 8: 39109 36 39109
#> 9: 39113 299 39109
#> 10: 39149 13 39109
#> 11: 39061 287 39061
#> 12: 39081 37 39081
#> 13: 39129 59 39141
#> 14: 39131 22 39141
#> 15: 39141 79 39141
#> 16: 39017 280 39017
#> 17: 39039 67 39039
#>
#> attr(,"class")
#> [1] "list" "clusters"