Graph-theoretic scagnostics values — pair

Calculates scagnostic values for every numeric variable pair in a dataset.

Usage

pair_scagnostics(
  d,
  scagnostic = c("Outlying", "Skewed", "Clumpy", "Sparse", "Striated", "Convex",
    "Skinny", "Stringy", "Monotonic"),
  handle.na = TRUE,
  ...
)

Arguments

d: A dataframe
scagnostic: a character vector for the scagnostic to be calculated. Subset of "Outlying", "Stringy", "Striated", "Clumpy", "Sparse", "Skewed", "Convex", "Skinny" or "Monotonic"
handle.na: If TRUE uses pairwise complete observations.
...: other arguments

Value

A tibble of class pairwise with scagnostic values for every numeric variable pair, or NULL if there are not at least two numeric variables

Details

The scagnostic values are calculated using scagnostics function from the scagnostics package.

References

Wilkinson, Leland, Anushka Anand, and Robert Grossman. "Graph-theoretic scagnostics." Information Visualization, IEEE Symposium on. IEEE Computer Society, 2005

Examples

 pair_scagnostics(iris)
#> # A tibble: 54 × 6
#>    x            y            score     group  value pair_type
#>    <chr>        <chr>        <chr>     <chr>  <dbl> <chr>    
#>  1 Petal.Length Petal.Width  Outlying  all   0.0484 nn       
#>  2 Petal.Length Petal.Width  Skewed    all   0.428  nn       
#>  3 Petal.Length Petal.Width  Clumpy    all   0.549  nn       
#>  4 Petal.Length Petal.Width  Sparse    all   0.0507 nn       
#>  5 Petal.Length Petal.Width  Striated  all   0.108  nn       
#>  6 Petal.Length Petal.Width  Convex    all   0.316  nn       
#>  7 Petal.Length Petal.Width  Skinny    all   0.662  nn       
#>  8 Petal.Length Petal.Width  Stringy   all   0.393  nn       
#>  9 Petal.Length Petal.Width  Monotonic all   0.889  nn       
#> 10 Petal.Length Sepal.Length Outlying  all   0.247  nn       
#> # ℹ 44 more rows