## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(rolescry)
set.seed(1)

## ----example------------------------------------------------------------------
d <- data.frame(
  arm  = rep(c(0, 1), each = 50),  # a balanced 2-level grouping
  pre  = rnorm(100, 10, 2),        # measured before ...
  post = rnorm(100, 11, 2),        # ... and after (paired)
  resp = rbinom(100, 1, 0.4)       # a binary response
)

res <- detect_roles(d)
res
summary(res)

## ----blind--------------------------------------------------------------------
d_blind <- setNames(d, paste0("col_", seq_along(d)))
pos <- function(r, dat) match(r$roles$paired_pairs$columns, names(dat))
identical(pos(detect_roles(d), d), pos(detect_roles(d_blind), d_blind))

## ----breakdown----------------------------------------------------------------
res$roles$paired_pairs$components[[1]]

## ----nmi----------------------------------------------------------------------
g <- sample(c("A", "B", "C"), 300, replace = TRUE)
y <- ifelse(g == "A", "event", sample(c("event", "none"), 300, replace = TRUE))
compute_nmi(g, y)          # > 0: g informs y
compute_nmi(g, sample(g))  # ~ 0: shuffled -> independent

## ----namebonus----------------------------------------------------------------
clin <- data.frame(
  male  = rbinom(120, 1, 0.5),      # a demographic binary (first)
  death = rbinom(120, 1, 0.3)       # the intended outcome
)
detect_roles(clin)$roles$outcome_binary$columns                                  # positional default
detect_roles(clin, name_bonus = rolescry_default_name_bonus())$roles$outcome_binary$columns  # "death"