Expand a set of multiverse decisions into all possible combinations

Usage

expand_decisions(
  .pipeline,
  .collect_after = NULL,
  .pointer_path = NULL,
  .subgroup_in_path = FALSE
)

Arguments

.pipeline: a data.frame produced by calling a series of add_* functions.
.collect_after: default is NULL. Most of the time you will not use this argument. However, if your data come from a database, you can use this argument to call dplyr::collect() from dbplyr after a simple filter statements to speed up computations. Valid options are "subgroups", "filters", or "preprocess". Note that dbplyr does not support all expressions.
.pointer_path: a string specifying a path to create a external pointer object. This is only necessary if you are using data from an external source. Defaults to NULL.
.subgroup_in_path: logical, whether to place the subgroup filters in a file path. This is only relevant if you are using an external pointer (e.g., an Arrow filesystem database). Placing the subgroup filter in the path itself might provide a performance boost over reading the entire filesystem and then performing subgoup filtering.

Value

a nested data.frame containing all combinations of arbitrary decisions for a multiverse analysis. Decision types will become list columns matching the type of decisions called along the pipeline (e.g., filters, variables, etc.). Any decisions containing glue syntax will be populated with the relevant information.

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)
  )

full_pipeline <-
  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(process_name = "scale_iv", 'mutate({ivs} = scale({ivs}))') |>
  add_preprocess(process_name = "scale_mod", mutate({mods} := scale({mods}))) |>
  add_summary_stats("iv_stats", starts_with("iv"), c("mean", "sd")) |>
  add_summary_stats("dv_stats", starts_with("dv"), c("skewness", "kurtosis")) |>
  add_correlations("predictors", matches("iv|mod|cov"), focus_set = c(cov1,cov2)) |>
  add_correlations("outcomes", matches("dv|mod"), focus_set = matches("dv")) |>
  add_reliabilities("unp_scale", c(iv1,iv2,iv3)) |>
  add_model("no covariates", lm({dvs} ~ {ivs} * {mods})) |>
  add_model("with covariates", lm({dvs} ~ {ivs} * {mods} + cov1)) |>
  add_postprocess("aov", aov())

pipeline_expanded <- expand_decisions(full_pipeline)