volker (version 2.1.0)

effect_counts: Output effect sizes and test statistics for count data

Description

The type of effect size depends on the number of selected columns:

• One categorical column: see effect_counts_one

• Multiple categorical columns: see effect_counts_items

Cross tabulations:

• One categorical column and one grouping column: see effect_counts_one_grouped

• Multiple categorical columns and one grouping column: see effect_counts_items_grouped (not yet implemented)

• Multiple categorical columns and multiple grouping columns: effect_counts_items_grouped_items (not yet implemented)

By default, if you provide two column selections, the second column is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

• One categorical column and one metric column: see effect_counts_one_cor (not yet implemented)

• Multiple categorical columns and one metric column: see effect_counts_items_cor (not yet implemented)

• Multiple categorical columns and multiple metric columns:effect_counts_items_cor_items (not yet implemented)

Usage

effect_counts(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Value

A volker tibble.

Arguments

data

A data frame.

cols

A tidy column selection, e.g. a single column (without quotes) or multiple columns selected by methods such as starts_with().

cross

Optional, a grouping column. The column name without quotes.

metric

When crossing variables, the cross column parameter can contain categorical or metric values. By default, the cross column selection is treated as categorical data. Set metric to TRUE, to treat it as metric and calculate correlations.

clean

Prepare data by data_clean.

...

Other parameters passed to the appropriate effect function.

Examples

library(volker)
data <- volker::chatgpt

effect_counts(data, sd_gender, adopter)