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state_df <- get_estimates(geography = "state", product = "population", vintage = 2023,
geometry = TRUE,
keep_geo_vars = TRUE) |>
filter(variable == "POPESTIMATE") |>
tigris::shift_geometry()
#> Using the Vintage 2023 Population Estimates
state_df |>
select(STUSPS, variable, value) |>
arrange(desc(value))
#> Simple feature collection with 52 features and 3 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -3111747 ymin: -1697746 xmax: 2258200 ymax: 1565782
#> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic
#> # A tibble: 52 × 4
#> STUSPS variable value geometry
#> <chr> <chr> <dbl> <MULTIPOLYGON [m]>
#> 1 CA POPESTIMATE 38965193 (((-2066923 -203083.1, -2066434 -203272.1, -2065390 -203998.6, -2064847 -203983.1, -...
#> 2 TX POPESTIMATE 30503301 (((123936.1 -866655.9, 124036.5 -866293.1, 124101.4 -866136.8, 124302.4 -866192.1, 1...
#> 3 FL POPESTIMATE 22610726 (((1539355 -1253701, 1539399 -1253591, 1539568 -1253573, 1539645 -1253677, 1539670 -...
#> 4 NY POPESTIMATE 19571216 (((1971370 670201.1, 1971466 670819.8, 1971674 671037.9, 1971705 671652.5, 1971826 6...
#> 5 PA POPESTIMATE 12961683 (((1287712 486864, 1287647 487393.7, 1287516 488466, 1286933 492035.5, 1286933 49203...
#> 6 IL POPESTIMATE 12549689 (((378605.4 309417.4, 378724.8 310290.3, 378839.6 310581.1, 379159 311074.9, 379230....
#> 7 OH POPESTIMATE 11785935 (((1093937 536238.6, 1094689 536961.2, 1094790 537429.3, 1094700 537846.3, 1095036 5...
#> 8 GA POPESTIMATE 11029227 (((1390722 -584139.2, 1390875 -583744.3, 1391189 -583636.6, 1391495 -583725.5, 13914...
#> 9 NC POPESTIMATE 10835491 (((1799008 17387.01, 1799662 17972.25, 1800801 18183.4, 1802098 18181.65, 1802610 17...
#> 10 MI POPESTIMATE 10037261 (((1049981 578947.1, 1050107 579034, 1050201 578927.1, 1050205 578748.4, 1050058 578...
#> # ℹ 42 more rows
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