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Introduction

The cheese package contains tools for working with data during statistical analysis–promoting flexible, intuitive, and reproducible workflows. There are functions designated for specific statistical tasks such as

These are built on a collection of data manipulation tools designed for general use, many of which are motivated by the functional programming concept (i.e. purrr) and use non-standard evaluation for column selection as in dplyr::select. Here are a few:

  • depths(): Find the depth(s) of elements in a list structure that satisfy a predicate
  • divide() and fasten(): Split/bind data frames to/from any list depth
  • dish(): Evaluate a function with pairwise combinations of columns
  • stratiply(): Evaluate a function on subsets of a data frame
  • typly(): Evaluate a function on columns that inherit at least one (or none) of the specified classes

Usage

Variable Level Summary
Age 56 (48, 61)
Sex Female 97 (32.01%)
Male 206 (67.99%)
ChestPain Typical angina 23 (7.59%)
Atypical angina 50 (16.5%)
Non-anginal pain 86 (28.38%)
Asymptomatic 144 (47.52%)
BP 130 (120, 140)
Cholesterol 241 (211, 275)
MaximumHR 153 (133.5, 166)
ExerciseInducedAngina No 204 (67.33%)
Yes 99 (32.67%)
HeartDisease No 164 (54.13%)
Yes 139 (45.87%)
#Run some models
heart_disease %>%

  #Apply a function to subsets of the data
  stratiply(
    by = Sex,
    f =
      ~.x %>%
      
      #Apply a function to pairwise combinations of columns
      dish(
        left = c(ExerciseInducedAngina, HeartDisease),
        f = function(y, x) glm(y ~ x, family = "binomial") %>% purrr::pluck("coefficients") %>% tibble::enframe()
      )
  ) %>%
    
    #Bind rows up to a specified depth
    fasten(
      into = c("Outcome", "Predictor"),
      depth = 1
    )
#> $Female
#> # A tibble: 28 x 4
#>    Outcome               Predictor   name                  value
#>    <chr>                 <chr>       <chr>                 <dbl>
#>  1 ExerciseInducedAngina Age         (Intercept)        -1.46   
#>  2 ExerciseInducedAngina Age         x                   0.00416
#>  3 ExerciseInducedAngina ChestPain   (Intercept)       -17.6    
#>  4 ExerciseInducedAngina ChestPain   xAtypical angina   15.5    
#>  5 ExerciseInducedAngina ChestPain   xNon-anginal pain  14.8    
#>  6 ExerciseInducedAngina ChestPain   xAsymptomatic      17.4    
#>  7 ExerciseInducedAngina BP          (Intercept)        -6.47   
#>  8 ExerciseInducedAngina BP          x                   0.0383 
#>  9 ExerciseInducedAngina Cholesterol (Intercept)        -2.06   
#> 10 ExerciseInducedAngina Cholesterol x                   0.00315
#> # … with 18 more rows
#> 
#> $Male
#> # A tibble: 28 x 4
#>    Outcome               Predictor   name                 value
#>    <chr>                 <chr>       <chr>                <dbl>
#>  1 ExerciseInducedAngina Age         (Intercept)       -2.44   
#>  2 ExerciseInducedAngina Age         x                  0.0356 
#>  3 ExerciseInducedAngina ChestPain   (Intercept)       -1.32   
#>  4 ExerciseInducedAngina ChestPain   xAtypical angina  -1.39   
#>  5 ExerciseInducedAngina ChestPain   xNon-anginal pain -0.219  
#>  6 ExerciseInducedAngina ChestPain   xAsymptomatic      1.71   
#>  7 ExerciseInducedAngina BP          (Intercept)        0.0385 
#>  8 ExerciseInducedAngina BP          x                 -0.00424
#>  9 ExerciseInducedAngina Cholesterol (Intercept)       -1.70   
#> 10 ExerciseInducedAngina Cholesterol x                  0.00494
#> # … with 18 more rows

See the package vignettes and documentation for more thorough examples.