cheese (version 0.1.2)

univariate_associations: Compute association statistics between columns of a data frame

Description

Evaluate a list of scalar functions on any number of "response" columns by any number of "predictor" columns

Usage

univariate_associations(
    data,
    f,
    responses,
    predictors
)

Value

A tibble::tibble with the response/predictor columns down the rows and the results of the f across the columns. The names of the result columns will be the names provided in f.

Arguments

data

A data.frame.

f

A function or a list of functions (preferably named) that take a vector as input in the first two arguments and return a scalar.

responses

A vector of quoted/unquoted columns, positions, and/or tidyselect::select_helpers to be evaluated as the first argument. See the left argument in dish.

predictors

A vector of quoted/unquoted columns, positions, and/or tidyselect::select_helpers to be evaluated as the second argument. See the right argument in dish.

Author

Alex Zajichek

Examples

#Make a list of functions to evaluate
f <-
  list(
    
    #Compute a univariate p-value
    `P-value` =
      function(y, x) {
        if(some_type(x, c("factor", "character"))) {
          
          p <- fisher.test(factor(y), factor(x), simulate.p.value = TRUE)$p.value
          
        } else {
          
          p <- kruskal.test(x, factor(y))$p.value
          
        }
        
        ifelse(p < 0.001, "<0.001", as.character(round(p, 2)))
        
      },
    
    #Compute difference in AIC model between null model and one predictor model
    `AIC Difference` =
      function(y, x) {
        
        glm(factor(y)~1, family = "binomial")$aic -
          glm(factor(y)~x, family = "binomial")$aic
        
      }
  )

#Choose a couple binary outcomes
heart_disease %>% 
  univariate_associations(
    f = f,
    responses = c(ExerciseInducedAngina, HeartDisease)
  )

#Use a subset of predictors
heart_disease %>% 
  univariate_associations(
    f = f,
    responses = c(ExerciseInducedAngina, HeartDisease),
    predictors = c(Age, BP)
  )

#Numeric predictors only
heart_disease %>% 
  univariate_associations(
    f = f,
    responses = c(ExerciseInducedAngina, HeartDisease),
    predictors = is.numeric
  )