summarize_all_categorical: Summarize All Categorical Variables

In jjwillard/wfbmcphsr: WFBMC Public Health Sciences

Description

This function allows you to calculate counts, proportions, or a combination of both for all binary or multiple level categorical variables in a dataset. The function will take two vectors of categorical variables split by 'binary' and 'multiple' levels. NOTE: ALL CATEGORICAL VARIABLES MUST BE CONVERTED TO FACTORS BEFORE USING THIS FUNCTION. For binary variables, make sure vairables are coded as 0-1. This function will produce output for the case where the binary variable equals 1. If both levels of the binary variable are desired, include the binary variable in the 'multiple' level categorical variable vector. For 'display' options: 'CP' = counts and proportions, 'C' = counts, and 'P' = proportions (defaults to 'CP')

Usage

summarize_all_categorical(df, binary_cat_vars, multiple_cat_vars,
  grouping_var, display = "CP", show_pval = TRUE, digits = 1)

Arguments

df	Dataset containing covariates of interest
binary_cat_vars	Vector of binary variables (in 0-1 format)
multiple_cat_vars	Vector of multiple level categorical variables
grouping_var	Variable to group by (will be columns of table)
display	How to display results: 'CP' = counts and proportions, 'C' = counts, 'P' = proportions (defaults to 'CP')
digits	Number of digits to round decimals

Value

A data frame summarizing mean/sd of covariate at each level of grouping variable

Examples

## Not run: 
summarize_all_categorical(df = obpv_baseline, binary_cat_vars = binary_cat_vars,
multiple_cat_vars = multiple_cat_vars, grouping_var = obpv_quintile, display = 'CP', digits = 1)

## End(Not run)

jjwillard/wfbmcphsr documentation built on May 14, 2019, 5:01 a.m.