Format Estimates
format_estimates.RdFormat estimates of a county summary table with the option to include shares and share margins of error.
Arguments
- table
A data frame/tibble from `get_acs_recs()` for a single table and time period.
- type
A character, select either 'total' or 'share'.
- moe
A logical value, TRUE or FALSE to include or exclude margins of error.
Value
A data frame of ACS estimates by PSRC counties and region of either total estimates or proportions, with or without margins of error.
Examples
df <-get_acs_recs(geography = 'county',
table.names = c('B03002'),
years = c(2019),
acs.type = 'acs1')
#> Getting data from the 2019 1-year ACS
#> The 1-year ACS provides data for geographies with populations of 65,000 and greater.
#> Loading ACS1 variables for 2019 from table B03002. To cache this dataset for faster access to ACS tables in the future, run this function with `cache_table = TRUE`. You only need to do this once per ACS dataset.
#> Using FIPS code '53' for state 'Washington'
#> Using FIPS code '033' for 'King County'
#> Using FIPS code '035' for 'Kitsap County'
#> Using FIPS code '053' for 'Pierce County'
#> Using FIPS code '061' for 'Snohomish County'
format_estimates(df)
#> # A tibble: 21 × 30
#> variable label concept acs_type year King_cv King_estimate King_moe
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 B03002_001 Estimate!!… HISPAN… acs1 2019 NA 2252782 0
#> 2 B03002_002 Estimate!!… HISPAN… acs1 2019 NA 2030140 0
#> 3 B03002_003 Estimate!!… HISPAN… acs1 2019 0.00150 1302544 3208
#> 4 B03002_004 Estimate!!… HISPAN… acs1 2019 0.0192 147822 4678
#> 5 B03002_005 Estimate!!… HISPAN… acs1 2019 0.0908 13321 1990
#> 6 B03002_006 Estimate!!… HISPAN… acs1 2019 0.0101 424590 7085
#> 7 B03002_007 Estimate!!… HISPAN… acs1 2019 0.0709 15702 1831
#> 8 B03002_008 Estimate!!… HISPAN… acs1 2019 0.303 6574 3281
#> 9 B03002_009 Estimate!!… HISPAN… acs1 2019 0.0448 119587 8804
#> 10 B03002_010 Estimate!!… HISPAN… acs1 2019 0.402 2639 1744
#> # ℹ 11 more rows
#> # ℹ 22 more variables: King_reliability <chr>, King_se <dbl>, Kitsap_cv <dbl>,
#> # Kitsap_estimate <dbl>, Kitsap_moe <dbl>, Kitsap_reliability <chr>,
#> # Kitsap_se <dbl>, Pierce_cv <dbl>, Pierce_estimate <dbl>, Pierce_moe <dbl>,
#> # Pierce_reliability <chr>, Pierce_se <dbl>, Snohomish_cv <dbl>,
#> # Snohomish_estimate <dbl>, Snohomish_moe <dbl>, Snohomish_reliability <chr>,
#> # Snohomish_se <dbl>, Region_cv <dbl>, Region_estimate <dbl>, …
format_estimates(df, type = 'share', moe = FALSE)
#> # A tibble: 21 × 25
#> variable label concept acs_type year King_cv King_reliability King_se
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 B03002_001 Estimate… HISPAN… acs1 2019 NA missing or N/A NA
#> 2 B03002_002 Estimate… HISPAN… acs1 2019 NA missing or N/A NA
#> 3 B03002_003 Estimate… HISPAN… acs1 2019 0.00150 good 1950.
#> 4 B03002_004 Estimate… HISPAN… acs1 2019 0.0192 good 2844.
#> 5 B03002_005 Estimate… HISPAN… acs1 2019 0.0908 good 1210.
#> 6 B03002_006 Estimate… HISPAN… acs1 2019 0.0101 good 4307.
#> 7 B03002_007 Estimate… HISPAN… acs1 2019 0.0709 good 1113.
#> 8 B03002_008 Estimate… HISPAN… acs1 2019 0.303 use with caution 1995.
#> 9 B03002_009 Estimate… HISPAN… acs1 2019 0.0448 good 5352.
#> 10 B03002_010 Estimate… HISPAN… acs1 2019 0.402 use with caution 1060.
#> # ℹ 11 more rows
#> # ℹ 17 more variables: King_share <dbl>, Kitsap_cv <dbl>,
#> # Kitsap_reliability <chr>, Kitsap_se <dbl>, Kitsap_share <dbl>,
#> # Pierce_cv <dbl>, Pierce_reliability <chr>, Pierce_se <dbl>,
#> # Pierce_share <dbl>, Snohomish_cv <dbl>, Snohomish_reliability <chr>,
#> # Snohomish_se <dbl>, Snohomish_share <dbl>, Region_cv <dbl>,
#> # Region_reliability <chr>, Region_se <dbl>, Region_share <dbl>