ff_row_totals: Add row totals to 'summary_factorlist()' output

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

Add row totals to summary_factorlist() output

Description

This adds a total and missing count to variables. This is useful for continuous variables. Compare this to summary_factorlist(total_col = TRUE) which includes a count for each dummy variable as a factor and mean (sd) or median (iqr) for continuous variables.

Usage

ff_row_totals(
  df.in,
  .data,
  dependent,
  explanatory,
  missing_column = TRUE,
  percent = TRUE,
  digits = 1,
  na_include_dependent = FALSE,
  na_complete_cases = FALSE,
  total_name = "Total N",
  na_name = "Missing N"
)

finalfit_row_totals(
  df.in,
  .data,
  dependent,
  explanatory,
  missing_column = TRUE,
  percent = TRUE,
  digits = 1,
  na_include_dependent = FALSE,
  na_complete_cases = FALSE,
  total_name = "Total N",
  na_name = "Missing N"
)

Arguments

df.in	summary_factorlist() output.
.data	Data frame used to create summary_factorlist().
dependent	Character. Name of dependent variable.
explanatory	Character vector of any length: name(s) of explanatory variables.
missing_column	Logical. Include a column of counts of missing data.
percent	Logical. Include percentage.
digits	Integer length 1. Number of digits for percentage.
na_include_dependent	Logical. When TRUE, missing data in the dependent variable is included in totals.
na_complete_cases	Logical. When TRUE, missing data counts for variables are for compelte cases across all included variables.
total_name	Character. Name of total column.
na_name	Character. Name of missing column.

Value

Data frame.

Examples

explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
 summary_factorlist(dependent, explanatory) %>%
	ff_row_totals(colon_s, dependent, explanatory)

finalfit documentation built on Nov. 17, 2023, 1:09 a.m.