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

## ----payload------------------------------------------------------------------
r = smart_cor(mtcars$mpg, mtcars$wt)

## ----payload-tidy-------------------------------------------------------------
library(tibble)  # for printing
infcols = c("estimate", "statistic", "p.value", "p_method", "null_hypothesis",
             "ci_lower", "ci_upper", "conf_level", "ci_method", "ci_source")
as.data.frame(tidy(r)[infcols])

## ----source-map---------------------------------------------------------------
set.seed(1)
colour = factor(sample(c("red", "blue", "green"), 90, replace = TRUE))
shape  = factor(sample(c("circle", "square", "triangle"), 90, replace = TRUE))

one = function(method, x, y) {
  tidy(smart_cor(x, y, method = method, verbose = FALSE))[
    c("method_label", "ci_method", "ci_source", "p_method", "null_hypothesis")]
}

spec = list(
  one("pearson",        mtcars$mpg,  mtcars$wt),
  one("spearman",       mtcars$mpg,  mtcars$wt),
  one("kendall",        mtcars$mpg,  mtcars$wt),
  one("point_biserial", mtcars$mpg,  mtcars$vs),
  one("phi",            mtcars$vs,   mtcars$am),
  one("tetrachoric",    mtcars$vs,   mtcars$am),
  one("yules_q",        mtcars$vs,   mtcars$am),
  one("polychoric",     mtcars$gear, mtcars$carb),
  one("polyserial",     mtcars$mpg,  mtcars$gear),
  one("gamma",          mtcars$gear, mtcars$carb),
  one("rank_biserial",  mtcars$vs,   mtcars$gear),
  one("cramers_v",      colour,      shape),
  one("theils_u",       colour,      shape),
  one("tschuprows_t",   colour,      shape)
)
map = do.call(rbind, spec)
knitr::kable(map[c("method_label", "ci_method", "ci_source")],
             row.names = FALSE, caption = "How each method's confidence interval is computed.")

## ----bootstrap-crosscheck-----------------------------------------------------
rb = smart_cor(mtcars$mpg, mtcars$wt, bootstrap = TRUE, n_boot = 1000, verbose = FALSE)
tidy(rb)[c("estimate", "ci_lower", "ci_upper", "ci_method", "ci_source")]

## ----conf-level---------------------------------------------------------------
rbind(
  `90%` = tidy(smart_cor(mtcars$mpg, mtcars$wt, conf_level = 0.90, verbose = FALSE))[c("ci_lower", "ci_upper")],
  `95%` = tidy(smart_cor(mtcars$mpg, mtcars$wt, conf_level = 0.95, verbose = FALSE))[c("ci_lower", "ci_upper")]
)

## ----nulls--------------------------------------------------------------------
knitr::kable(unique(map[c("method_label", "p_method", "null_hypothesis")]),
             row.names = FALSE, caption = "The null hypothesis and test behind each p-value.")

## ----compare------------------------------------------------------------------
compare_methods(mtcars$gear, mtcars$carb)