/
cces_join.R
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cces_join.R
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#' Build CCES Data by Joining Component Pieces
#'
#'
#' Produces a person-level dataset with question-level dataset. This will only
#' use the variables necessary for the model, thus its name "slim". Therefore,
#' is model dependent.
#'
#'
#' @param ccq_df dataframe of outcomes, taken from \link{get_cces_question}. We
#' currently assume the name for the outcome is named "response", although this
#' can be modified with the `y_named_as` argument
#' @param ccc_df dataframe of covariates, currently taken only from the cumulative
#' common content. It should have passed \link{ccc_std_demographics} to be
#' compatible with ACS.
#' @param cd_df dataframe of district-level predictors, see \link{cd_info_2018}
#' for a sample. Currently, we join this to the rest of the data on the column
#' called \code{"cd"} and \code{"year"}.
#' @param coerce_to_char Whether to coerce the case identifier to character class,
#' this enables the join.
#' @param keep_vars Variables that will be kept as a cell variable, regardless
#' of whether it is specified in a formula. Input as character vector.
#' @param subset_dist a character for the geography of cd to subset
#'
#' @inheritParams build_counts
#' @import dplyr
#' @importFrom rlang .data
#' @importFrom stats as.formula
#' @importFrom stringr str_detect
#'
#'
#' @examples
#' \dontrun{
#' # need data/input/cces/cces_2018.rds to run this
#' ccq_tcja <- get_cces_question("CC18_326", "2018", "TCJA")
#'
#' cces_join_slim(ccq_df = ccq_tcja,
#' ccc_df = filter(ccc_samp, year == 2018),
#' cd_df = cd_info_2018,
#' formula = "response ~ age + educ + (1|cd)")
#'
#' # alternative - cd and state not in the formula, but cells follow this
#' cces_join_slim(ccq_df = ccq_tcja,
#' ccc_df = filter(ccc_samp, year == 2018),
#' cd_df = cd_info_2018,
#' keep_vars = c("st", "cd"),
#' formula = "response ~ age + educ")
#'
#' # A tibble: 133 x 7
#' # year case_id qID response age educ cd
#' # <dbl> <chr> <chr> <fct> <dbl> <dbl+lbl> <chr>
#' # 1 2018 409942960 TCJA Support 36 4 [2-Year] VA-11
#' # 2 2018 410934028 TCJA Support 34 5 [4-Year] UT-04
#' # 3 2018 410946304 TCJA Support 62 5 [4-Year] OK-02
#' # 4 2018 411717742 TCJA Oppose 50 1 [No HS] IL-03
#' # 5 2018 412022838 TCJA Oppose 66 5 [4-Year] OH-08
#' # 6 2018 412123052 TCJA Oppose 35 3 [Some College] WA-04
#' # 7 2018 412161131 TCJA Support 32 3 [Some College] IN-03
#' # 8 2018 412260240 TCJA Support 66 5 [4-Year] VA-01
#' # 9 2018 412274191 TCJA Support 75 6 [Post-Grad] OR-02
#' }
#'
#' @export
cces_join_slim <- function(ccq_df,
ccc_df,
cd_df,
formula,
coerce_to_char = TRUE,
keep_vars = NULL,
subset_dist = NA) {
y_named_as <- all.vars(as.formula(formula))[1L]
vars <- c("year", "case_id", "qID",
y_named_as,
keep_vars,
# the outcome is reponse
all.vars(as.formula(formula))[-c(1)])
if (coerce_to_char) {
ccq_df$case_id <- as.character(ccq_df$case_id)
ccc_df$case_id <- as.character(ccc_df$case_id)
}
joined_df <- ungroup(ccc_df) %>%
inner_join(ungroup(ccq_df), by = c("case_id", "year")) %>%
left_join(cd_df, by = c("cd", "year"))
# if party reg, also update by only giving 1s to active registrants
if (attr(ccq_df, "question") == "CL_party") {
}
# select only necessary models ---
df_sel <- select(joined_df, !!!vars)
# subset to state
if (!is.na(subset_dist))
df_sel <- filter(df_sel, str_detect(.data$cd, subset_dist))
attr(df_sel, "question") <- attr(ccq_df, "question")
df_sel
}