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Fetch and subset DCWS data

fetch_cws.Rd

This function returns the split data from cws_full_data in a nicer format, with options for subsetting. Filtering by year, location name, and category are named options, any of which take a vector of one or more values, but any valid conditions can be passed to ... for more flexible filtering. For any named options, NULL, the default, will mean no filtering is done by that column.

Usage

fetch_cws(
  ...,
  .year = NULL,
  .name = NULL,
  .category = NULL,
  .unnest = FALSE,
  .add_wts = FALSE,
  .drop_ct = TRUE,
  .incl_questions = TRUE
)

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.

.category

A vector of one or more strings giving the category(ies) to subset by. If NULL, no filtering is done by category.

.unnest

Boolean: should data be returned nested into a column called data? Defaults to FALSE.

.add_wts

Boolean: should groups' survey weights be attached, via a left-join with dcws::cws_full_wts? This is useful if you need to collapse groups later; otherwise you might get stuck in annoying tidyr::unnest messes.

.drop_ct

Boolean: should statewide totals be included for each crosstab extract? This can be useful for a single location in order to have Connecticut values to compare against, but becomes redundant with multiple locations. The default TRUE means statewide averages will not be included.

.incl_questions

Boolean: should the full text of each question be included? If FALSE, questions will be demarcated by just their codes, which take up less space but can change year to year. Defaults TRUE.

Value

A data frame, with between 5 and 10 columns, depending on arguments:

  • Columns year, span, name, and code are always included

  • If .incl_questions = TRUE, column question is included

  • If .unnest = TRUE, the crosstab data will be in columns category, group, response, and value

  • If .unnest = FALSE, crosstab data columns will be nested in a list-column called data

  • If .add_wts = TRUE, column weight is included Note that the span column, a new addition, is a string giving the span of years included in that set of survey data. For single years, this will be the same as year; in the case of a pooled dataset 2015-2024, year will be 2024 and span will be "2015_2024".

See also

fetch_wts() cws_full_data

Other accessing: fetch_wts(), read_xtabs(), xtab2df()

Examples

# no filtering
fetch_cws()
#> # A tibble: 38,446 × 6
#>     year span  name  code  question                                     data    
#>    <dbl> <chr> <chr> <chr> <chr>                                        <list>  
#>  1  2015 2015  5CT   Q1    Are you satisfied with the city or area whe… <tibble>
#>  2  2015 2015  5CT   Q2    As a place to live, is the city or area whe… <tibble>
#>  3  2015 2015  5CT   Q3A   How responsive local government is to the n… <tibble>
#>  4  2015 2015  5CT   Q3B   The availability of the goods and services … <tibble>
#>  5  2015 2015  5CT   Q3C   The job done by the police to keep resident… <tibble>
#>  6  2015 2015  5CT   Q3D   The ability of residents to obtain suitable… <tibble>
#>  7  2015 2015  5CT   Q3E   As a place to raise children                 <tibble>
#>  8  2015 2015  5CT   Q3F   The condition of public parks and other pub… <tibble>
#>  9  2015 2015  5CT   Q4    Over the past 12 months, have you volunteer… <tibble>
#> 10  2015 2015  5CT   Q5    How would you describe your ability to infl… <tibble>
#> # ℹ 38,436 more rows

# filter by year, name, and/or category
fetch_cws(.name = c("Greater New Haven", "New Haven")) # all years
#> # A tibble: 1,217 × 6
#>     year span  name              code  question                         data    
#>    <dbl> <chr> <chr>             <chr> <chr>                            <list>  
#>  1  2015 2015  Greater New Haven Q1    Are you satisfied with the city… <tibble>
#>  2  2015 2015  Greater New Haven Q2    As a place to live, is the city… <tibble>
#>  3  2015 2015  Greater New Haven Q3A   How responsive local government… <tibble>
#>  4  2015 2015  Greater New Haven Q3B   The availability of the goods a… <tibble>
#>  5  2015 2015  Greater New Haven Q3C   The job done by the police to k… <tibble>
#>  6  2015 2015  Greater New Haven Q3D   The ability of residents to obt… <tibble>
#>  7  2015 2015  Greater New Haven Q3E   As a place to raise children     <tibble>
#>  8  2015 2015  Greater New Haven Q3F   The condition of public parks a… <tibble>
#>  9  2015 2015  Greater New Haven Q4    Over the past 12 months, have y… <tibble>
#> 10  2015 2015  Greater New Haven Q5    How would you describe your abi… <tibble>
#> # ℹ 1,207 more rows
fetch_cws(.year = 2024, .name = c("Greater New Haven", "New Haven"))
#> # A tibble: 244 × 6
#>     year span  name              code   question                        data    
#>    <dbl> <chr> <chr>             <chr>  <chr>                           <list>  
#>  1  2024 2024  Greater New Haven Q1     Are you satisfied with the cit… <tibble>
#>  2  2024 2024  Greater New Haven Q2     As a place to live, is the cit… <tibble>
#>  3  2024 2024  Greater New Haven Q4A    [KEY: Now I'm going to ask you… <tibble>
#>  4  2024 2024  Greater New Haven Q4D    The job done by the police to … <tibble>
#>  5  2024 2024  Greater New Haven Q4E    The ability of residents to ob… <tibble>
#>  6  2024 2024  Greater New Haven Q4F    As a place to raise children    <tibble>
#>  7  2024 2024  Greater New Haven Q4G    The condition of public parks … <tibble>
#>  8  2024 2024  Greater New Haven Q4H    The availability of affordable… <tibble>
#>  9  2024 2024  Greater New Haven Q6     How would you describe your ab… <tibble>
#> 10  2024 2024  Greater New Haven TRUSTA How much trust do you have in … <tibble>
#> # ℹ 234 more rows
fetch_cws(.year = "2015_2024", .name = "New Haven", .category = c("Total", "Age", "Gender"))
#> # A tibble: 92 × 6
#>     year span      name      code   question                            data    
#>    <dbl> <chr>     <chr>     <chr>  <chr>                               <list>  
#>  1  2024 2015_2024 New Haven Q1     Are you satisfied with the city or… <tibble>
#>  2  2024 2015_2024 New Haven Q2     As a place to live, is the city or… <tibble>
#>  3  2024 2015_2024 New Haven Q4A    [KEY: Now I'm going to ask you to … <tibble>
#>  4  2024 2015_2024 New Haven Q4D    The job done by the police to keep… <tibble>
#>  5  2024 2015_2024 New Haven Q4E    The ability of residents to obtain… <tibble>
#>  6  2024 2015_2024 New Haven Q4F    As a place to raise children        <tibble>
#>  7  2024 2015_2024 New Haven Q4G    The condition of public parks and … <tibble>
#>  8  2024 2015_2024 New Haven Q4H    The availability of affordable, hi… <tibble>
#>  9  2024 2015_2024 New Haven Q6     How would you describe your abilit… <tibble>
#> 10  2024 2015_2024 New Haven TRUSTA How much trust do you have in each… <tibble>
#> # ℹ 82 more rows

# filter by conditions
fetch_cws(code == "Q4E", .year = 2018, .name = c("Greater New Haven", "New Haven"), .unnest = TRUE)
#> # A tibble: 240 × 9
#>     year span  name              code  question    category group response value
#>    <dbl> <chr> <chr>             <chr> <chr>       <fct>    <fct> <fct>    <dbl>
#>  1  2018 2018  Greater New Haven Q4E   The abilit… Total    Grea… Excelle…  0.08
#>  2  2018 2018  Greater New Haven Q4E   The abilit… Total    Grea… Good      0.34
#>  3  2018 2018  Greater New Haven Q4E   The abilit… Total    Grea… Fair      0.32
#>  4  2018 2018  Greater New Haven Q4E   The abilit… Total    Grea… Poor      0.11
#>  5  2018 2018  Greater New Haven Q4E   The abilit… Total    Grea… Don't k…  0.14
#>  6  2018 2018  Greater New Haven Q4E   The abilit… Total    Grea… Refused   0   
#>  7  2018 2018  Greater New Haven Q4E   The abilit… Gender   Male  Excelle…  0.08
#>  8  2018 2018  Greater New Haven Q4E   The abilit… Gender   Male  Good      0.35
#>  9  2018 2018  Greater New Haven Q4E   The abilit… Gender   Male  Fair      0.33
#> 10  2018 2018  Greater New Haven Q4E   The abilit… Gender   Male  Poor      0.12
#> # ℹ 230 more rows
fetch_cws(grepl("Q4[A-Z]", code), .year = 2018, .name = c("Greater New Haven", "New Haven"))
#> # A tibble: 12 × 6
#>     year span  name              code  question                         data    
#>    <dbl> <chr> <chr>             <chr> <chr>                            <list>  
#>  1  2018 2018  Greater New Haven Q4A   How responsive local government… <tibble>
#>  2  2018 2018  Greater New Haven Q4D   The job done by the police to k… <tibble>
#>  3  2018 2018  Greater New Haven Q4E   The ability of residents to obt… <tibble>
#>  4  2018 2018  Greater New Haven Q4F   As a place to raise children     <tibble>
#>  5  2018 2018  Greater New Haven Q4G   The condition of public parks a… <tibble>
#>  6  2018 2018  Greater New Haven Q4H   The availability of affordable,… <tibble>
#>  7  2018 2018  New Haven         Q4A   How responsive local government… <tibble>
#>  8  2018 2018  New Haven         Q4D   The job done by the police to k… <tibble>
#>  9  2018 2018  New Haven         Q4E   The ability of residents to obt… <tibble>
#> 10  2018 2018  New Haven         Q4F   As a place to raise children     <tibble>
#> 11  2018 2018  New Haven         Q4G   The condition of public parks a… <tibble>
#> 12  2018 2018  New Haven         Q4H   The availability of affordable,… <tibble>
fetch_cws(grepl("health insurance", question), year > 2015, .name = "New Haven")
#> # A tibble: 14 × 6
#>     year span      name      code   question                            data    
#>    <dbl> <chr>     <chr>     <chr>  <chr>                               <list>  
#>  1  2018 2018      New Haven Q26    Do you have health insurance?       <tibble>
#>  2  2018 2018      New Haven Q27    (If have health insurance) What ty… <tibble>
#>  3  2018 2018      New Haven Q30B   The doctor or hospital wouldn't ac… <tibble>
#>  4  2021 2021      New Haven Q26    Do you have health insurance?       <tibble>
#>  5  2021 2021      New Haven Q27    (If yes) What type of health insur… <tibble>
#>  6  2024 2015_2024 New Haven Q26    Do you have health insurance?       <tibble>
#>  7  2024 2015_2024 New Haven Q27    (If yes to Do you have health insu… <tibble>
#>  8  2024 2015_2024 New Haven Q30B   (Did you not get the medical care … <tibble>
#>  9  2024 2024      New Haven Q26    Do you have health insurance?       <tibble>
#> 10  2024 2024      New Haven Q27    (If yes to Do you have health insu… <tibble>
#> 11  2024 2024      New Haven Q30B   (Did you not get the medical care … <tibble>
#> 12  2024 2024      New Haven MENTHC (If yes to During the past 12 mont… <tibble>
#> 13  2024 2024      New Haven MENTHD (If yes to During the past 12 mont… <tibble>
#> 14  2024 2024      New Haven HCAS   CALCULATED: Health care access sco… <tibble>
fetch_cws(question %in% c("Diabetes", "Asthma"), .name = "Bridgeport")
#> # A tibble: 10 × 6
#>     year span      name       code  question data             
#>    <dbl> <chr>     <chr>      <chr> <chr>    <list>           
#>  1  2015 2015      Bridgeport Q23C  Diabetes <tibble [80 × 4]>
#>  2  2015 2015      Bridgeport Q23E  Asthma   <tibble [80 × 4]>
#>  3  2018 2018      Bridgeport Q23C  Diabetes <tibble [72 × 4]>
#>  4  2018 2018      Bridgeport Q23E  Asthma   <tibble [72 × 4]>
#>  5  2021 2021      Bridgeport Q23C  Diabetes <tibble [72 × 4]>
#>  6  2021 2021      Bridgeport Q23E  Asthma   <tibble [72 × 4]>
#>  7  2024 2015_2024 Bridgeport Q23C  Diabetes <tibble [72 × 4]>
#>  8  2024 2015_2024 Bridgeport Q23E  Asthma   <tibble [72 × 4]>
#>  9  2024 2024      Bridgeport Q23C  Diabetes <tibble [96 × 4]>
#> 10  2024 2024      Bridgeport Q23E  Asthma   <tibble [96 × 4]>

# how you might use this to make a beautiful table
fetch_cws(code == "Q1", .year = 2021, .category = c("Income", "Gender"), .unnest = TRUE) |>
    dplyr::group_by(name, category, group) |>
    # might want to remove refused, don't know responses
    sub_nonanswers() |>
    dplyr::filter(response == "Yes") |>
    tidyr::pivot_wider(id_cols = name, names_from = group, values_from = value)
#> # A tibble: 34 × 6
#> # Groups:   name [34]
#>    name               Male Female `<$30K` `$30K-$100K` `$100K+`
#>    <chr>             <dbl>  <dbl>   <dbl>        <dbl>    <dbl>
#>  1 Bridgeport        0.788  0.778   0.758        0.778    0.814
#>  2 Bristol           0.78   0.888  NA           NA       NA    
#>  3 Connecticut       0.888  0.889   0.798        0.879    0.919
#>  4 Danbury           0.94   0.929   0.99         0.94     0.929
#>  5 Fairfield County  0.87   0.899   0.798        0.889    0.929
#>  6 Greater Hartford  0.898  0.89    0.828        0.908    0.960
#>  7 Greater New Haven 0.869  0.879   0.828        0.899    0.87 
#>  8 Greater Waterbury 0.85   0.837   0.7          0.828    0.92 
#>  9 Greenwich         0.89   0.980   0.94         0.929    0.939
#> 10 Hartford          0.716  0.75    0.673        0.760    0.818
#> # ℹ 24 more rows

# adding weights to collapse groups (e.g. combining income brackets)
fetch_cws(code == "Q1", .year = 2021, .add_wts = TRUE)
#> # A tibble: 35 × 6
#>     year span  name              code  question                         data    
#>    <dbl> <chr> <chr>             <chr> <chr>                            <list>  
#>  1  2021 2021  Bridgeport        Q1    Are you satisfied with the city… <tibble>
#>  2  2021 2021  Bristol           Q1    Are you satisfied with the city… <tibble>
#>  3  2021 2021  Connecticut       Q1    Are you satisfied with the city… <tibble>
#>  4  2021 2021  Danbury           Q1    Are you satisfied with the city… <tibble>
#>  5  2021 2021  Fairfield County  Q1    Are you satisfied with the city… <tibble>
#>  6  2021 2021  Greater Hartford  Q1    Are you satisfied with the city… <tibble>
#>  7  2021 2021  Greater New Haven Q1    Are you satisfied with the city… <tibble>
#>  8  2021 2021  Greater Waterbury Q1    Are you satisfied with the city… <tibble>
#>  9  2021 2021  Greenwich         Q1    Are you satisfied with the city… <tibble>
#> 10  2021 2021  Hartford          Q1    Are you satisfied with the city… <tibble>
#> # ℹ 25 more rows
fetch_cws(
    .year = 2021, .name = "New Haven", .category = c("Total", "Age", "Income"),
    .add_wts = TRUE, .unnest = TRUE
)
#> # A tibble: 3,294 × 10
#>     year span  name      code  question     category group response value weight
#>    <dbl> <chr> <chr>     <chr> <chr>        <fct>    <fct> <fct>    <dbl>  <dbl>
#>  1  2021 2021  New Haven Q1    Are you sat… Total    New … Yes       0.82  1    
#>  2  2021 2021  New Haven Q1    Are you sat… Total    New … No        0.17  1    
#>  3  2021 2021  New Haven Q1    Are you sat… Total    New … Don't k…  0.01  1    
#>  4  2021 2021  New Haven Q1    Are you sat… Total    New … Refused   0     1    
#>  5  2021 2021  New Haven Q1    Are you sat… Age      Ages… Yes       0.8   0.456
#>  6  2021 2021  New Haven Q1    Are you sat… Age      Ages… No        0.2   0.456
#>  7  2021 2021  New Haven Q1    Are you sat… Age      Ages… Don't k…  0     0.456
#>  8  2021 2021  New Haven Q1    Are you sat… Age      Ages… Refused   0     0.456
#>  9  2021 2021  New Haven Q1    Are you sat… Age      Ages… Yes       0.82  0.219
#> 10  2021 2021  New Haven Q1    Are you sat… Age      Ages… No        0.18  0.219
#> # ℹ 3,284 more rows