Fetch and subset weights for DCWS data

This function returns the nested cws_full_wts data, meant as a counterpart to fetch_cws.

Usage

fetch_wts(..., .year = NULL, .name = NULL, .unnest = FALSE)

Arguments

...: Any number of conditions to filter by, which will be passed to dplyr::filter. These don't override the named options, so if you filter by year > 2020 but then set .year = 2015 you're not going to get any data.
.year: A vector of one or more year(s) to subset by. If this is a character that contains a separator ("_", "-", or a space character), it will be assumed to be a span of years, such as for multi-year pooled crosstabs (e.g. "2015_2024"). Otherwise it's assumed this is a single year of the survey. If NULL, no filtering is done by year.
.name: A vector of one or more strings giving the name(s) to subset by. If NULL, no filtering is done by name.
.unnest: Boolean: should data be returned nested into a column called data? Defaults to FALSE.

Value

A data frame, with either 3 columns (if .unnest = FALSE) or 4 columns (if .unnest = TRUE).

For .unnest = FALSE: columns are year, name, and a list of nested data frames weights
For .unnest = TRUE: year, name, group, and weight

Examples

# no filtering
fetch_wts()
#> # A tibble: 386 × 4
#>     year span  name               weights          
#>    <dbl> <chr> <chr>              <list>           
#>  1  2015 2015  5CT                <tibble [2 × 2]> 
#>  2  2015 2015  Bridgeport         <tibble [21 × 2]>
#>  3  2015 2015  Bristol            <tibble [14 × 2]>
#>  4  2015 2015  Greater Waterbury  <tibble [25 × 2]>
#>  5  2015 2015  Connecticut        <tibble [27 × 2]>
#>  6  2015 2015  Greater Hartford   <tibble [24 × 2]>
#>  7  2015 2015  Danbury            <tibble [18 × 2]>
#>  8  2015 2015  Fairfield County   <tibble [27 × 2]>
#>  9  2015 2015  Greater Bridgeport <tibble [25 × 2]>
#> 10  2015 2015  Greater New Haven  <tibble [25 × 2]>
#> # ℹ 376 more rows

# weights are generally useful in combination with actual data
# but unless you unnest in advance, this is messy
fetch_cws(code == "Q4E",
    .year = 2021,
    .name = c("Greater New Haven", "New Haven"), .unnest = TRUE
) |>
    dplyr::left_join(fetch_wts(.unnest = TRUE), by = c("year", "span", "name", "group"))
#> # A tibble: 216 × 10
#>     year span  name          code  question category group response value weight
#>    <dbl> <chr> <chr>         <chr> <chr>    <fct>    <fct> <fct>    <dbl>  <dbl>
#>  1  2021 2021  Greater New … Q4E   The abi… Total    Grea… Excelle…  0.09  1    
#>  2  2021 2021  Greater New … Q4E   The abi… Total    Grea… Good      0.39  1    
#>  3  2021 2021  Greater New … Q4E   The abi… Total    Grea… Fair      0.26  1    
#>  4  2021 2021  Greater New … Q4E   The abi… Total    Grea… Poor      0.07  1    
#>  5  2021 2021  Greater New … Q4E   The abi… Total    Grea… Don't k…  0.19  1    
#>  6  2021 2021  Greater New … Q4E   The abi… Total    Grea… Refused   0     1    
#>  7  2021 2021  Greater New … Q4E   The abi… Gender   Male  Excelle…  0.1   0.471
#>  8  2021 2021  Greater New … Q4E   The abi… Gender   Male  Good      0.41  0.471
#>  9  2021 2021  Greater New … Q4E   The abi… Gender   Male  Fair      0.24  0.471
#> 10  2021 2021  Greater New … Q4E   The abi… Gender   Male  Poor      0.08  0.471
#> # ℹ 206 more rows

# instead, use fetch_cws with .add_wts = TRUE to get these weights joined for you
fetch_cws(code == "Q4E",
    .year = 2021,
    .name = c("Greater New Haven", "New Haven"),
    .add_wts = TRUE
)
#> # A tibble: 2 × 6
#>    year span  name              code  question                          data    
#>   <dbl> <chr> <chr>             <chr> <chr>                             <list>  
#> 1  2021 2021  Greater New Haven Q4E   The ability of residents to obta… <tibble>
#> 2  2021 2021  New Haven         Q4E   The ability of residents to obta… <tibble>

Fetch and subset weights for DCWS data

Usage

Arguments

Value

See also

Examples