Add filtering/exclusion criteria to a multiverse pipeline

Usage

add_filters(.df, ...)

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.
...: logical expressions to be used with filter separated by commas. Expressions should not be quoted.

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)

# 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)
  )

the_data |>
  add_filters(include1 == 0,include2 != 3,include2 != 2, include3 > -2.5)
#> # A tibble: 7 × 3
#>   type    group    code                          
#>   <chr>   <chr>    <chr>                         
#> 1 filters include1 include1 == 0                 
#> 2 filters include1 include1 %in% unique(include1)
#> 3 filters include2 include2 != 3                 
#> 4 filters include2 include2 != 2                 
#> 5 filters include2 include2 %in% unique(include2)
#> 6 filters include3 include3 > -2.5               
#> 7 filters include3 include3 %in% unique(include3)