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State level data on population by age.

Usage

pop_age_2019

Format

A data frame with 2820 rows and 4 variables.

state

State as 2 letter abbreviation.

state_name

State name.

age

Age cohort for population.

population

Population of age cohort.

state_total_population

total estimated state population in 2019

Examples

library(dplyr)

# List age population for each state with percent of total
pop_age_2019 %>%
  group_by(state_name, age) %>%
  mutate(percent = population / state_total_population * 100) %>%
  select(state_name, age, population, percent)
#> # A tibble: 4,386 × 4
#> # Groups:   state_name, age [4,386]
#>    state_name age   population percent
#>    <chr>      <chr>      <int>   <dbl>
#>  1 Alabama    0          56901    1.16
#>  2 Alabama    1          58290    1.19
#>  3 Alabama    2          59073    1.20
#>  4 Alabama    3          59799    1.22
#>  5 Alabama    4          60294    1.23
#>  6 Alabama    5          59568    1.21
#>  7 Alabama    6          58599    1.20
#>  8 Alabama    7          59537    1.21
#>  9 Alabama    8          60023    1.22
#> 10 Alabama    9          60241    1.23
#> # ℹ 4,376 more rows

pop_age_2019 %>%
  select(state_name, state_total_population) %>%
  distinct() %>%
  arrange(desc(state_total_population))
#>              state_name state_total_population
#> 1            California               39512223
#> 2                 Texas               28995881
#> 3               Florida               21477737
#> 4              New York               19453561
#> 5          Pennsylvania               12801989
#> 6              Illinois               12671821
#> 7                  Ohio               11689100
#> 8               Georgia               10617423
#> 9        North Carolina               10488084
#> 10             Michigan                9986857
#> 11           New Jersey                8882190
#> 12             Virginia                8535519
#> 13           Washington                7614893
#> 14              Arizona                7278717
#> 15        Massachusetts                6892503
#> 16            Tennessee                6829174
#> 17              Indiana                6732219
#> 18             Missouri                6137428
#> 19             Maryland                6045680
#> 20            Wisconsin                5822434
#> 21             Colorado                5758736
#> 22            Minnesota                5639632
#> 23       South Carolina                5148714
#> 24              Alabama                4903185
#> 25            Louisiana                4648794
#> 26             Kentucky                4467673
#> 27               Oregon                4217737
#> 28             Oklahoma                3956971
#> 29          Connecticut                3565287
#> 30                 Utah                3205958
#> 31                 Iowa                3155070
#> 32               Nevada                3080156
#> 33             Arkansas                3017804
#> 34          Mississippi                2976149
#> 35               Kansas                2913314
#> 36           New Mexico                2096829
#> 37             Nebraska                1934408
#> 38        West Virginia                1792147
#> 39                Idaho                1787065
#> 40               Hawaii                1415872
#> 41        New Hampshire                1359711
#> 42                Maine                1344212
#> 43              Montana                1068778
#> 44         Rhode Island                1059361
#> 45             Delaware                 973764
#> 46         South Dakota                 884659
#> 47         North Dakota                 762062
#> 48               Alaska                 731545
#> 49 District of Columbia                 705749
#> 50              Vermont                 623989
#> 51              Wyoming                 578759