R/specify.R

In infer: Tidy Statistical Inference

#' Specify response and explanatory variables
#'
#' @description
#'
#' `specify()` is used to specify which columns in the supplied data frame are
#' the relevant response (and, if applicable, explanatory) variables. Note that
#' character variables are converted to `factor`s.
#'
#' Learn more in `vignette("infer")`.
#'
#' @param x A data frame that can be coerced into a [tibble][tibble::tibble].
#' @param formula A formula with the response variable on the left and the
#'   explanatory on the right. Alternatively, a `response` and `explanatory`
#'   argument can be supplied.
#' @param response The variable name in `x` that will serve as the response.
#'   This is an alternative to using the `formula` argument.
#' @param explanatory The variable name in `x` that will serve as the
#'   explanatory variable. This is an alternative to using the formula argument.
#' @param success The level of `response` that will be considered a success, as
#'   a string. Needed for inference on one proportion, a difference in
#'   proportions, and corresponding z stats.
#'
#' @return A tibble containing the response (and explanatory, if specified)
#'   variable data.
#'
#' @examples
#' # specifying for a point estimate on one variable
#' gss %>%
#'    specify(response = age)
#'
#' # specify a relationship between variables as a formula...
#' gss %>%
#'   specify(age ~ partyid)
#'
#' # ...or with named arguments!
#' gss %>%
#'   specify(response = age, explanatory = partyid)
#'
#' # more in-depth explanation of how to use the infer package
#' \dontrun{
#' vignette("infer")
#' }
#'
#' @importFrom rlang f_lhs f_rhs get_expr caller_env abort warn inform
#' @importFrom dplyr select any_of across
#' @importFrom methods hasArg
#' @family core functions
#' @export
specify <- function(x, formula, response = NULL,
                    explanatory = NULL, success = NULL) {
  check_type(x, is.data.frame)

  # Standardize variable types
  x <- standardize_variable_types(x)

  # Parse response and explanatory variables
  response <- enquo(response)
  explanatory <- enquo(explanatory)

  x <- parse_variables(x, formula, response, explanatory)

  # Add attributes
  attr(x, "success") <- success
  attr(x, "generated") <- FALSE
  attr(x, "hypothesized") <- FALSE
  attr(x, "fitted") <- FALSE

  # Check the success argument
  check_success_arg(x, success)

  # Select variables
  x <- x %>%
    select(any_of(c(response_name(x), explanatory_name(x))))

  is_complete <- stats::complete.cases(x)
  if (!all(is_complete)) {
    x <- dplyr::filter(x, is_complete)
    warn(glue("Removed {sum(!is_complete)} rows containing missing values."))
  }

  # Add "infer" class
  append_infer_class(x)
}

parse_variables <- function(x, formula, response, explanatory, call = caller_env()) {
  if (methods::hasArg(formula)) {
    tryCatch(
      rlang::is_formula(formula),
      error = function(e) {
         abort(c("The argument you passed in for the formula does not exist.",
                  "Were you trying to pass in an unquoted column name?",
                  "Did you forget to name one or more arguments?"),
               call = call)
      }
    )
    if (!rlang::is_formula(formula)) {
      abort(glue("The first unnamed argument must be a formula. ",
                 "You passed in '{get_type(formula)}'. ",
                 "Did you forget to name one or more arguments?"),
            call = call)
    }
  }

  attr(x, "response")    <- get_expr(response)
  attr(x, "explanatory") <- get_expr(explanatory)
  attr(x, "formula") <- NULL

  if (methods::hasArg(formula)) {
    attr(x, "response")    <- f_lhs(formula)
    attr(x, "explanatory") <- f_rhs(formula)
    attr(x, "formula") <- formula
  }

  # Check response and explanatory variables to be appropriate for later use
  if (!has_response(x)) {
     abort(paste0("Please supply a response variable that is not `NULL`."),
           call = call)
  }

  check_var_correct(x, "response", call = call)
  check_var_correct(x, "explanatory", call = call)

  # If there's an explanatory var
  check_vars_different(x, call = call)

  if (!has_attr(x, "response")) {
    attr(x, "response_type") <- NULL
  } else {
    attr(x, "response_type") <- class(response_variable(x))
  }

  if (!has_attr(x, "explanatory")) {
    attr(x, "explanatory_type") <- NULL
  } else {
    attr(x, "explanatory_type") <-
      purrr::map_chr(as.data.frame(explanatory_variable(x)), class)
  }

  attr(x, "type_desc_response") <- determine_variable_type(x, "response")
  attr(x, "type_desc_explanatory") <- determine_variable_type(x, "explanatory")

  # Determine params for theoretical fit
  x <- set_params(x)

  x
}

check_success_arg <- function(x, success, call = caller_env()) {
  response_col <- response_variable(x)

  if (!is.null(success)) {
    if (!is.character(success)) {
       abort(paste0("`success` must be a string."), call = call)
    }
    if (!is.factor(response_col)) {
       abort(paste0(
        "`success` should only be specified if the response is a categorical ",
        "variable."
      ), call = call)
    }
    if (!(success %in% levels(response_col))) {
       abort(glue('{success} is not a valid level of {response_name(x)}.'),
             call = call)
    }
    if (sum(table(response_col) > 0) > 2) {
       abort(paste0(
        "`success` can only be used if the response has two levels. ",
        "`filter()` can reduce a variable to two levels."
      ), call = call)
    }
  }

  if ((attr(x, "response_type") == "factor" &&
      is.null(success) &&
      length(levels(response_variable(x))) == 2) &&
     ((!has_attr(x, "explanatory_type") ||
       length(levels(explanatory_variable(x))) == 2))) {
     abort(glue(
        'A level of the response variable `{response_name(x)}` needs to be ',
        'specified for the `success` argument in `specify()`.'
      ), call = call)
    }

}

check_var_correct <- function(x, var_name, call = caller_env()) {
  var <- attr(x, var_name)

  # Variable (if present) should be a symbolic column name
  if (!is.null(var)) {
    if (!rlang::is_symbolic(var)) {
       abort(glue(
        "The {var_name} should be a bare variable name (not a string in ",
        "quotation marks)."
      ), call = call)
    }

    if (any(!(all.vars(var) %in% names(x)))) {
       abort(glue(
        'The {var_name} variable `{var}` cannot be found in this dataframe.'
      ), call = call)
    }
  }

  TRUE
}

check_vars_different <- function(x, call = caller_env()) {
  if (has_response(x) && has_explanatory(x)) {
    if (identical(response_name(x), explanatory_name(x))) {
       abort(paste0(
        "The response and explanatory variables must be different from one ",
        "another."
      ), call = call)
    }
  }

  TRUE
}