run_descriptives {multitool}

Run a multiverse-style descriptive analysis based on a complete decision grid

Description

Run a multiverse-style descriptive analysis based on a complete decision grid

Usage

run_descriptives(.pipeline, show_progress = TRUE)

Arguments

.pipeline

a tibble produced by a series of add_* calls. Importantly, this needs to be a pre-expanded pipeline because descriptive analyses only change when the underlying cases change. Thus, only filtering decisions will be used and internally expanded before calculating various descriptive analyses.

show_progress

logical, whether to show a progress bar while running.

Value

single tibble containing tidied results for all descriptive analyses specified

Examples

library(tidyverse)
library(multitool)

# Simulate some data
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)
  )

# Decision pipeline
full_pipeline <-
  the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2,scale(include3) > -2.5) |>
  add_variables("ivs", iv1, iv2, iv3) |>
  add_variables("dvs", dv1, dv2) |>
  add_variables("mods", starts_with("mod")) |>
  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_reliabilities("vio_scale", starts_with("mod"))

run_descriptives(full_pipeline)