Collapse CCES data to be analyzed in binomial model

Currently only is compatible with question of type "yesno".

build_counts(
  formula,
  data,
  keep_vars = NULL,
  name_ones_as = "yes",
  name_trls_as = "n_response",
  multiple_qIDs = FALSE,
  verbose = TRUE
)

Arguments

formula	the model formula used to fit the multilevel regression model. Should be of the form y ~ x1 + x2 + (1|x3) where y is a binary variable and only categorical variables should be used in the random effects notation.
data	A cleaned CCES dataset, e.g. from ccc_std_demographics which is then combined with outcome and contextual data in cces_join_slim. By default it expects the LHS outcome to be named response, and expects the dataset to have that variable. This variable must be binary or it must be a character vector that can be coerced by yesno_to_binary into a binary variable.
keep_vars	Variables that will be kept as a cell variable, regardless of whether it is specified in a formula. Input as character vector.
name_ones_as	What to name the variable that represents the number of successes in the binomial
name_trls_as	What to name the variable that represents the number of trials in the binomial.
multiple_qIDs	Does the data contain multiple outcomes in long form and therefore require the counts to be built for each outcome? Defaults to FALSE.
verbose	Show warning messages? Defaults to TRUE

Value

A dataframe of cells. The following variables have fixed names and will be assumed by ccesMRPrun::fit_brms_binomial:

• yes: the number of successes observed in the cell

• n_response the number of non-missing responses, representing the number of trials.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

ccc_samp_std <- ccc_samp %>%
  mutate(y = sample(c("For", "Against"), size = n(), replace = TRUE)) %>%
  ccc_std_demographics()
#> age variable modified to bins. Original age variable is now in age_orig. 

ccc_samp_out <- build_counts(y ~ age + gender + educ + (1|cd),
                             ccc_samp_std)

ccc_samp_out
#> # A tibble: 945 x 6
#>    age            gender educ         cd    n_response   yes
#>    <fct>          <fct>  <fct>        <chr>      <int> <dbl>
#>  1 18 to 24 years Male   HS or Less   CA-39          1     1
#>  2 18 to 24 years Male   HS or Less   FL-25          1     0
#>  3 18 to 24 years Male   HS or Less   GA-10          1     1
#>  4 18 to 24 years Male   HS or Less   IL-07          1     0
#>  5 18 to 24 years Male   HS or Less   LA-01          1     0
#>  6 18 to 24 years Male   HS or Less   MA-05          1     1
#>  7 18 to 24 years Male   HS or Less   NC-09          1     1
#>  8 18 to 24 years Male   HS or Less   NC-11          1     1
#>  9 18 to 24 years Male   HS or Less   PA-11          1     1
#> 10 18 to 24 years Male   Some College IA-02          1     1
#> # … with 935 more rows

# alternative options
build_counts(y ~ educ + (1|cd), ccc_samp_std,
             name_ones_as = "success", name_trls_as = "trials")
#> # A tibble: 716 x 4
#>    educ       cd    trials success
#>    <fct>      <chr>  <int>   <dbl>
#>  1 HS or Less AK-01      1       0
#>  2 HS or Less AL-02      3       2
#>  3 HS or Less AL-03      2       0
#>  4 HS or Less AL-05      1       0
#>  5 HS or Less AR-02      2       1
#>  6 HS or Less AR-03      2       2
#>  7 HS or Less AZ-04      1       1
#>  8 HS or Less AZ-06      1       0
#>  9 HS or Less AZ-08      1       0
#> 10 HS or Less CA-06      2       1
#> # … with 706 more rows
build_counts(y ~ educ + (1|cd), ccc_samp_std,
             keep_vars = "state")
#> # A tibble: 716 x 5
#>    educ       cd    state      n_response   yes
#>    <fct>      <chr> <chr>           <int> <dbl>
#>  1 HS or Less AK-01 Alaska              1     0
#>  2 HS or Less AL-02 Alabama             3     2
#>  3 HS or Less AL-03 Alabama             2     0
#>  4 HS or Less AL-05 Alabama             1     0
#>  5 HS or Less AR-02 Arkansas            2     1
#>  6 HS or Less AR-03 Arkansas            2     2
#>  7 HS or Less AZ-04 Arizona             1     1
#>  8 HS or Less AZ-06 Arizona             1     0
#>  9 HS or Less AZ-08 Arizona             1     0
#> 10 HS or Less CA-06 California          2     1
#> # … with 706 more rows