latest_ny_times_counties_with_populations (view)
Data license: LICENSE · Data source: The New York Times · About: simonw/covid-19-datasette
67 rows where state = "Pennsylvania" sorted by cases_per_million descending
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Suggested facets: date (date)
state 1
- Pennsylvania · 67 ✖
date | county | state | fips | cases | deaths | population | deaths_per_million | cases_per_million ▲ |
---|---|---|---|---|---|---|---|---|
2022-05-13 | Forest | Pennsylvania | 42053 | 2250 | 35 | 7247 | 4829 | 310473 |
2022-05-13 | Fulton | Pennsylvania | 42057 | 4153 | 65 | 14530 | 4473 | 285822 |
2022-05-13 | Bradford | Pennsylvania | 42015 | 16219 | 207 | 60323 | 3431 | 268869 |
2022-05-13 | Cambria | Pennsylvania | 42021 | 34936 | 732 | 130192 | 5622 | 268342 |
2022-05-13 | York | Pennsylvania | 42133 | 120372 | 1502 | 449058 | 3344 | 268054 |
2022-05-13 | Northampton | Pennsylvania | 42095 | 81732 | 1097 | 305285 | 3593 | 267723 |
2022-05-13 | Mifflin | Pennsylvania | 42087 | 12348 | 276 | 46138 | 5982 | 267631 |
2022-05-13 | Union | Pennsylvania | 42119 | 11842 | 154 | 44923 | 3428 | 263606 |
2022-05-13 | Franklin | Pennsylvania | 42055 | 40694 | 695 | 155027 | 4483 | 262496 |
2022-05-13 | Lebanon | Pennsylvania | 42075 | 37011 | 518 | 141793 | 3653 | 261021 |
2022-05-13 | Huntingdon | Pennsylvania | 42061 | 11594 | 249 | 45144 | 5515 | 256822 |
2022-05-13 | Somerset | Pennsylvania | 42111 | 18829 | 408 | 73447 | 5555 | 256361 |
2022-05-13 | Northumberland | Pennsylvania | 42097 | 23172 | 534 | 90843 | 5878 | 255077 |
2022-05-13 | Lycoming | Pennsylvania | 42081 | 28894 | 522 | 113299 | 4607 | 255024 |
2022-05-13 | Montour | Pennsylvania | 42093 | 4637 | 93 | 18230 | 5101 | 254360 |
2022-05-13 | Carbon | Pennsylvania | 42025 | 16188 | 294 | 64182 | 4580 | 252220 |
2022-05-13 | Washington | Pennsylvania | 42125 | 51747 | 654 | 206865 | 3161 | 250148 |
2022-05-13 | Beaver | Pennsylvania | 42007 | 40744 | 745 | 163929 | 4544 | 248546 |
2022-05-13 | Lehigh | Pennsylvania | 42077 | 91539 | 1246 | 369318 | 3373 | 247859 |
2022-05-13 | Schuylkill | Pennsylvania | 42107 | 34853 | 675 | 141359 | 4775 | 246556 |
2022-05-13 | Clearfield | Pennsylvania | 42033 | 19533 | 348 | 79255 | 4390 | 246457 |
2022-05-13 | Berks | Pennsylvania | 42011 | 103652 | 1595 | 421164 | 3787 | 246108 |
2022-05-13 | Blair | Pennsylvania | 42013 | 29924 | 616 | 121829 | 5056 | 245622 |
2022-05-13 | Adams | Pennsylvania | 42001 | 25114 | 364 | 103009 | 3533 | 243803 |
2022-05-13 | Fayette | Pennsylvania | 42051 | 31332 | 674 | 129274 | 5213 | 242368 |
2022-05-13 | Elk | Pennsylvania | 42047 | 7193 | 101 | 29910 | 3376 | 240488 |
2022-05-13 | Butler | Pennsylvania | 42019 | 45173 | 743 | 187853 | 3955 | 240469 |
2022-05-13 | Columbia | Pennsylvania | 42037 | 15476 | 246 | 64964 | 3786 | 238224 |
2022-05-13 | Armstrong | Pennsylvania | 42005 | 15397 | 343 | 64735 | 5298 | 237846 |
2022-05-13 | Luzerne | Pennsylvania | 42079 | 75379 | 1366 | 317417 | 4303 | 237476 |
2022-05-13 | Crawford | Pennsylvania | 42039 | 20080 | 320 | 84629 | 3781 | 237270 |
2022-05-13 | Clinton | Pennsylvania | 42035 | 9157 | 127 | 38632 | 3287 | 237031 |
2022-05-13 | Greene | Pennsylvania | 42059 | 8513 | 104 | 36233 | 2870 | 234951 |
2022-05-13 | Westmoreland | Pennsylvania | 42129 | 81006 | 1378 | 348899 | 3949 | 232176 |
2022-05-13 | Bedford | Pennsylvania | 42009 | 11044 | 275 | 47888 | 5742 | 230621 |
2022-05-13 | Venango | Pennsylvania | 42121 | 11406 | 241 | 50668 | 4756 | 225112 |
2022-05-13 | Lancaster | Pennsylvania | 42071 | 122794 | 1890 | 545724 | 3463 | 225011 |
2022-05-13 | Lawrence | Pennsylvania | 42073 | 19109 | 418 | 85512 | 4888 | 223465 |
2022-05-13 | Allegheny | Pennsylvania | 42003 | 271427 | 3323 | 1216045 | 2732 | 223204 |
2022-05-13 | Monroe | Pennsylvania | 42089 | 37881 | 524 | 170271 | 3077 | 222474 |
2022-05-13 | Centre | Pennsylvania | 42027 | 36016 | 351 | 162385 | 2161 | 221793 |
2022-05-13 | Clarion | Pennsylvania | 42031 | 8323 | 202 | 38438 | 5255 | 216530 |
2022-05-13 | Erie | Pennsylvania | 42049 | 58218 | 760 | 269728 | 2817 | 215839 |
2022-05-13 | Mercer | Pennsylvania | 42085 | 23612 | 498 | 109424 | 4551 | 215784 |
2022-05-13 | Lackawanna | Pennsylvania | 42069 | 45137 | 776 | 209674 | 3700 | 215272 |
2022-05-13 | Dauphin | Pennsylvania | 42043 | 59830 | 964 | 278299 | 3463 | 214984 |
2022-05-13 | Indiana | Pennsylvania | 42063 | 17690 | 356 | 84073 | 4234 | 210412 |
2022-05-13 | Jefferson | Pennsylvania | 42065 | 9121 | 235 | 43425 | 5411 | 210040 |
2022-05-13 | Cumberland | Pennsylvania | 42041 | 51754 | 892 | 253370 | 3520 | 204262 |
2022-05-13 | McKean | Pennsylvania | 42083 | 8298 | 141 | 40625 | 3470 | 204258 |
2022-05-13 | Tioga | Pennsylvania | 42117 | 8270 | 193 | 40591 | 4754 | 203739 |
2022-05-13 | Wayne | Pennsylvania | 42127 | 10460 | 172 | 51361 | 3348 | 203656 |
2022-05-13 | Susquehanna | Pennsylvania | 42115 | 8188 | 110 | 40328 | 2727 | 203035 |
2022-05-13 | Snyder | Pennsylvania | 42109 | 8149 | 158 | 40372 | 3913 | 201847 |
2022-05-13 | Bucks | Pennsylvania | 42017 | 126504 | 1905 | 628270 | 3032 | 201352 |
2022-05-13 | Philadelphia | Pennsylvania | 42101 | 317808 | 5110 | 1584064 | 3225 | 200628 |
2022-05-13 | Delaware | Pennsylvania | 42045 | 113339 | 1875 | 566747 | 3308 | 199981 |
2022-05-13 | Potter | Pennsylvania | 42105 | 3256 | 92 | 16526 | 5566 | 197022 |
2022-05-13 | Wyoming | Pennsylvania | 42131 | 5257 | 106 | 26794 | 3956 | 196200 |
2022-05-13 | Juniata | Pennsylvania | 42067 | 4797 | 176 | 24763 | 7107 | 193716 |
2022-05-13 | Perry | Pennsylvania | 42099 | 8873 | 185 | 46272 | 3998 | 191757 |
2022-05-13 | Warren | Pennsylvania | 42123 | 7506 | 212 | 39191 | 5409 | 191523 |
2022-05-13 | Montgomery | Pennsylvania | 42091 | 158154 | 2329 | 830915 | 2802 | 190337 |
2022-05-13 | Pike | Pennsylvania | 42103 | 10557 | 98 | 55809 | 1755 | 189163 |
2022-05-13 | Cameron | Pennsylvania | 42023 | 822 | 21 | 4447 | 4722 | 184843 |
2022-05-13 | Chester | Pennsylvania | 42029 | 94968 | 1164 | 524989 | 2217 | 180895 |
2022-05-13 | Sullivan | Pennsylvania | 42113 | 1092 | 36 | 6066 | 5934 | 180019 |
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CREATE VIEW latest_ny_times_counties_with_populations AS select ny_times_us_counties.date, ny_times_us_counties.county, ny_times_us_counties.state, ny_times_us_counties.fips, ny_times_us_counties.cases, ny_times_us_counties.deaths, us_census_county_populations_2019.population, 1000000 * ny_times_us_counties.deaths / us_census_county_populations_2019.population as deaths_per_million, 1000000 * ny_times_us_counties.cases / us_census_county_populations_2019.population as cases_per_million from ny_times_us_counties join us_census_county_populations_2019 on ny_times_us_counties.fips = us_census_county_populations_2019.fips where "date" = ( select max(date) from ny_times_us_counties ) order by deaths_per_million desc;