R/ff_glimpse.R

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

#' Descriptive statistics for dataframe
#'
#' Everyone has a funcion like this, str, glimpse, glance etc. This one is
#' specifically designed for use with \code{finalfit} language. It is different
#' in dividing variables by numeric vs factor.
#'
#' @param .data Dataframe.
#' @param dependent Optional character vector: name(s) of depdendent
#'   variable(s).
#' @param explanatory Optional character vector: name(s) of explanatory
#'   variable(s).
#' @param digits Significant digits for continuous variable summaries
#' @param levels_cut Max number of factor levels to include in factor levels
#'   summary (in order to avoid the long printing of variables with many
#'   factors).
#'
#' @return Dataframe on summary data.
#' @export
#' @importFrom stats median
#'
#' @examples
#' library(finalfit)
#' dependent = 'mort_5yr'
#' explanatory = c("age", "nodes", "age.factor", "extent.factor", "perfor.factor")
#' colon_s %>%
#'   finalfit_glimpse(dependent, explanatory)

ff_glimpse <- function(.data, dependent=NULL, explanatory=NULL, digits = 1,
											 levels_cut = 5){
	if(any(class(.data) %in% c("tbl_df", "tbl"))) .data = data.frame(.data)
	if(is.null(dependent) && is.null(explanatory)){
		df.in = .data
	} else {
		df.in = .data %>% dplyr::select(dependent, explanatory)
	}

	# Continuous
	df.in %>%
		dplyr::select_if(is.numeric) -> df.numeric

	if(dim(df.numeric)[2]!=0){
		df.numeric %>%
			missing_glimpse(digits=digits) -> df.numeric.out1

		df.numeric %>%
			purrr::map_df(function(x){
				mean = mean(x, na.rm = TRUE)
				sd = sd(x, na.rm = TRUE)
				min = min(x, na.rm = TRUE)
				quartile_25 = quantile(x, probs = 0.25, na.rm = TRUE)
				median =  median(x, na.rm = TRUE)
				quartile_75 = quantile(x, probs = 0.75, na.rm = TRUE)
				max = max(x, na.rm = TRUE)
				df.out = data.frame(mean, sd, min, quartile_25, median, quartile_75, max) %>%
					dplyr::mutate_all(round_tidy, digits=digits)
			}) -> df.numeric.out2

		df.numeric.out = data.frame(df.numeric.out1, df.numeric.out2)

	}else{
		df.numeric.out = df.numeric
	}

	# Factors
	df.in %>%
		dplyr::select_if(Negate(is.numeric)) -> df.factors

	if(dim(df.factors)[2]!=0){

		df.factors %>%
			missing_glimpse(digits=digits) -> df.factors.out1

		fac2char = function(., cut = levels_cut) {
			length(levels(.)) > cut
		}

		df.factors %>%
			dplyr::mutate_if(fac2char, as.character) -> df.factors


		df.factors %>%
			purrr::map_df(function(x){
				levels_n = length(levels(as.factor(x)))
				levels = ifelse(is.factor(x),
				                forcats::fct_na_value_to_level(x, level = "(Missing)") %>%
													levels() %>%
													paste0("\"", ., "\"", collapse = ", "),
												"-")
				levels_count = ifelse(is.factor(x),
															summary(x) %>%
																paste(collapse = ", "),
															"-")
				levels_percent = ifelse(is.factor(x),
																summary(x) %>%
																	prop.table() %>%
																	{. * 100} %>%
																	format(digits = 2) %>%
																	paste(collapse=", "),
																"-")
				df.out = tibble::tibble(levels_n, levels, levels_count, levels_percent) %>% data.frame()
			}) -> df.factors.out2

		df.factors.out = data.frame(df.factors.out1, df.factors.out2)

	}else{
		df.factors.out = df.factors
	}
	# Previous "always print" version
	## Change to standard
	# cat("Continuous\n")
	# print(df.numeric.out, row.names = TRUE)
	# cat("\nCategorical\n")
	# print(df.factors.out, row.names = TRUE)
	# 
	# return(invisible(
	# 	list(
	# 		continuous = df.numeric.out,
	# 		categorical = df.factors.out))
	# )
	# 
	return(
		list(
			Continuous = df.numeric.out,
			Categorical = df.factors.out)
	)
}


#' @rdname ff_glimpse
#' @export
finalfit_glimpse <- ff_glimpse