R/ff_merge.R

In ewenharrison/finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

#' Merge a \code{\link{summary_factorlist}()} table with any number of model
#' results tables.
#'
#' A function that takes the output from \code{\link{summary_factorlist}(...,
#' fit_id=TRUE)} and merges with any number of model dataframes, usually
#' produced with a model wrapper followed by the \code{\link{fit2df}()} function
#' (see examples).
#'
#' @param factorlist Output from \code{\link{summary_factorlist}(...,
#'   fit_id=TRUE)}.
#' @param fit2df_df Output from model wrappers followed by
#'   \code{\link{fit2df}()}.
#' @param ref_symbol Reference symbol for model reference levels, typically "-"
#'   or "1.0".
#' @param estimate_name If you have chosen a new `estimate name` (e.g. "Odds
#'   ratio") when running a model wrapper (e.g. `glmuni`), then you need to pass
#'   this new name to `finalfit_merge` to generate correct table. Defaults to
#'   OR/HR/Coefficient
#' @param last_merge Logical. Set to try for the final merge in a series to
#'   remove index and fit_id columns.
#' @return Returns a dataframe of combined tables.
#'
#' @seealso \code{\link{summary_factorlist}} \code{\link{fit2df}}
#' @export
#'
#' @examples
#' library(finalfit)
#' library(dplyr)
#'
#' explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
#' explanatory_multi = c("age.factor", "obstruct.factor")
#' random_effect = "hospital"
#' dependent = "mort_5yr"
#'
#' # Create separate tables
#' colon_s %>%
#'  summary_factorlist(dependent, explanatory, fit_id=TRUE) -> example.summary
#'
#' colon_s %>%
#'  glmuni(dependent, explanatory) %>%
#'  fit2df(estimate_suffix=" (univariable)") -> example.univariable
#'
#' colon_s %>%
#'  glmmulti(dependent, explanatory) %>%
#'  fit2df(estimate_suffix=" (multivariable)") -> example.multivariable
#'
#' colon_s %>%
#'  glmmixed(dependent, explanatory, random_effect) %>%
#'  fit2df(estimate_suffix=" (multilevel)") -> example.multilevel
#'
#' # Pipe together
#' example.summary %>%
#'  ff_merge(example.univariable) %>%
#'  ff_merge(example.multivariable) %>%
#'  ff_merge(example.multilevel, last_merge = TRUE)
#'
#' # Using finalfit()
#' colon_s %>%
#'  finalfit(dependent, explanatory, keep_fit_id = TRUE) %>%
#'  ff_merge(example.multilevel, last_merge = TRUE)

ff_merge = function(factorlist, fit2df_df, ref_symbol = "-", estimate_name=NULL, last_merge = FALSE){
  if(is.null(factorlist$fit_id)) stop("Include fit_id=TRUE in summary_factorlist()")
	if(inherits(factorlist, "data.frame.ff") & "(Intercept)" %in% fit2df_df$explanatory){
		factorlist = dplyr::bind_rows(factorlist, dplyr::tibble(label = "Intercept", fit_id = "(Intercept)")) %>% 
			dplyr::mutate(index = ifelse(is.na(index), 0, index)) %>% 
			replace(is.na(.), "-") 
	}
  explanatory_name = names(fit2df_df)[1]
  estimate_col_id = ifelse(is.null(estimate_name), "Coefficient|OR|HR", paste0(estimate_name, "|Coefficient|OR|HR"))
  estimate_col = grep(estimate_col_id, names(fit2df_df), value=TRUE)
  df.out = merge(factorlist, fit2df_df, by.x = "fit_id", by.y = explanatory_name, all = TRUE)
  df.out[,estimate_col] = as.character(df.out[,estimate_col])
  df.out[is.na(df.out[,estimate_col]),estimate_col] = ref_symbol
  df.out = df.out[order(df.out$index),]
  if(last_merge == TRUE){
    df.out = df.out %>% 
      dplyr::select(-fit_id, -index)
  }
  return(df.out)
}

#' @rdname ff_merge
#' @export
finalfit_merge <- ff_merge