Add arbitrary summary statistics to a multiverse pipeline

Usage

add_model_descriptives(.df, desc_name, code)

Arguments

.df: The original data.frame(e.g., base data set). If part of set of add_* decision functions in a pipeline, the base data will be passed along as an attribute.
desc_name: a character string. A descriptive name for what the summary statistics you want to compute over the data passed to your model.
code: the literal code you would like to execute. For summary statistics, model.frame() will be called on the model object fit in the prior step. Your code should thus work with the variables that are used in your model.

Value

a data.frame with three columns: type, group, and code. Type indicates the decision type, group is a decision, and the code is the actual code that will be executed. If part of a pipe, the current set of decisions will be appended as new rows.

Examples


library(tidyverse)
library(multitool)

the_data <-
  data.frame(
    id   = 1:500,
    iv1  = rnorm(500),
    iv2  = rnorm(500),
    iv3  = rnorm(500),
    mod1 = rnorm(500),
    mod2 = rnorm(500),
    mod3 = rnorm(500),
    cov1 = rnorm(500),
    cov2 = rnorm(500),
    dv1  = rnorm(500),
    dv2  = rnorm(500),
    include1 = rbinom(500, size = 1, prob = .1),
    include2 = sample(1:3, size = 500, replace = TRUE),
    include3 = rnorm(500)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  add_preprocess("scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_model("linear model", lm({dvs} ~ {ivs} * {mods})) |>
  add_model_descriptives(
    "descriptives",
    summarize(body_mass_mean = mean({dvs}), .by = c(include2))
  )
#> # A tibble: 18 × 6
#>    type        group      code  model_coefs_fn model_fit_fn model_standardize_fn
#>    <chr>       <chr>      <glu> <chr>          <chr>        <chr>               
#>  1 filters     include1   incl… NA             NA           NA                  
#>  2 filters     include1   incl… NA             NA           NA                  
#>  3 filters     include2   incl… NA             NA           NA                  
#>  4 filters     include2   incl… NA             NA           NA                  
#>  5 filters     include2   incl… NA             NA           NA                  
#>  6 filters     include3   incl… NA             NA           NA                  
#>  7 filters     include3   incl… NA             NA           NA                  
#>  8 variables   ivs        iv1   NA             NA           NA                  
#>  9 variables   ivs        iv2   NA             NA           NA                  
#> 10 variables   ivs        iv3   NA             NA           NA                  
#> 11 variables   dvs        dv1   NA             NA           NA                  
#> 12 variables   dvs        dv2   NA             NA           NA                  
#> 13 variables   mods       mod1  NA             NA           NA                  
#> 14 variables   mods       mod2  NA             NA           NA                  
#> 15 variables   mods       mod3  NA             NA           NA                  
#> 16 preprocess  scale_iv   muta… NA             NA           NA                  
#> 17 models      linear mo… lm({… parameters::p… performance… parameters::standar…
#> 18 postprocess descripti… mode… NA             NA           NA