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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>      <dbl>   <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))
#> # A tibble: 51 × 2
#>    state_name     state_total_population
#>    <chr>                           <dbl>
#>  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
#> # ℹ 41 more rows