ff_glimpse: Descriptive statistics for dataframe

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

Descriptive statistics for dataframe

Description

Everyone has a funcion like this, str, glimpse, glance etc. This one is specifically designed for use with finalfit language. It is different in dividing variables by numeric vs factor.

Usage

ff_glimpse(
  .data,
  dependent = NULL,
  explanatory = NULL,
  digits = 1,
  levels_cut = 5
)

finalfit_glimpse(
  .data,
  dependent = NULL,
  explanatory = NULL,
  digits = 1,
  levels_cut = 5
)

Arguments

.data	Dataframe.
dependent	Optional character vector: name(s) of depdendent variable(s).
explanatory	Optional character vector: name(s) of explanatory variable(s).
digits	Significant digits for continuous variable summaries
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).

Value

Dataframe on summary data.

Examples

library(finalfit)
dependent = 'mort_5yr'
explanatory = c("age", "nodes", "age.factor", "extent.factor", "perfor.factor")
colon_s %>%
  finalfit_glimpse(dependent, explanatory)

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