cpf_: *C*umulative *P*ercentile *F*requency Table

In rebelrebel04/xplor: Data Exploration Utilities

Description

cpf produces a frequency table by the specified variables, which should be categorical and of relatively low cardinality.

Usage

cpf_(data, ..., .dots, wt = NULL, sort = TRUE, margin = TRUE,
  chi_square = FALSE, kable = FALSE)

cpf(data, ..., wt = NULL, sort = TRUE, margin = TRUE,
  chi_square = FALSE, kable = FALSE)

has(data, ..., wt = NULL, sort = TRUE, margin = TRUE,
  chi_square = FALSE, kable = FALSE)

has_(data, ..., .dots, wt = NULL, sort = TRUE, margin = TRUE,
  chi_square = FALSE, kable = FALSE)

Arguments

data	A dataframe.
...	Variable names defining table groupings.
.dots	Vector of variable names defining table groupings.
wt	(Optional, character) variable name to use for weighting frequencies.
sort	Logical, whether to sort table in descending frequency. Setting to FALSE will sort table alphabetically by grouping variables.
margin	Logical, whether to add total row to end of table.
chi_square	Logical, whether to print chi-square test (has no effect on one-dimensional tables).
kable	Logical, whether to format table for Rmarkdown.

Details

has is a wrapper around cpf for the common pattern of counting the number of NA and non-NA values in each specified variable. It is equivalent to cpf(data, !is.na(x), !is.na(y), ...).

Value

A dataframe or kable.

Examples

data(mtcars)
cpf(mtcars, cyl, gear)

rebelrebel04/xplor documentation built on May 27, 2019, 4:01 a.m.