Tidy a(n) dist object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for dist
tidy(x, diagonal = attr(x, "Diag"), upper = attr(x,
  "Upper"), ...)

Arguments

x	A dist object returned from stats::dist().
diagonal	Logical indicating whether or not to tidy the diagonal elements of the distance matrix. Defaults to whatever was based to the diag argument of stats::dist().
upper	Logical indicating whether or not to tidy the upper half of the distance matrix. Defaults to whatever was based to the upper argument of stats::dist().
...	Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Value

A tibble::tibble with one row for each pair of items in the distance matrix, with columns:

item1

First item

item2

Second item

distance

Distance between items

Details

If the distance matrix does not include an upper triangle and/or diagonal, the tidied version will not either.

See also

tidy(), stats::dist()

Other stats tidiers: tidy.density, tidy.ftable

Examples

iris_dist <- dist(t(iris[, 1:4]))
iris_dist
#>              Sepal.Length Sepal.Width Petal.Length
#> Sepal.Width      36.15785                         
#> Petal.Length     28.96619    25.77809             
#> Petal.Width      57.18304    25.86407     33.86473

tidy(iris_dist)
#> # A tibble: 6 x 3
#>   item1        item2        distance
#>   <fct>        <fct>           <dbl>
#> 1 Sepal.Width  Sepal.Length     36.2
#> 2 Petal.Length Sepal.Length     29.0
#> 3 Petal.Width  Sepal.Length     57.2
#> 4 Petal.Length Sepal.Width      25.8
#> 5 Petal.Width  Sepal.Width      25.9
#> 6 Petal.Width  Petal.Length     33.9
tidy(iris_dist, upper = TRUE)
#> # A tibble: 12 x 3
#>    item1        item2        distance
#>    <fct>        <fct>           <dbl>
#>  1 Sepal.Width  Sepal.Length     36.2
#>  2 Petal.Length Sepal.Length     29.0
#>  3 Petal.Width  Sepal.Length     57.2
#>  4 Sepal.Length Sepal.Width      36.2
#>  5 Petal.Length Sepal.Width      25.8
#>  6 Petal.Width  Sepal.Width      25.9
#>  7 Sepal.Length Petal.Length     29.0
#>  8 Sepal.Width  Petal.Length     25.8
#>  9 Petal.Width  Petal.Length     33.9
#> 10 Sepal.Length Petal.Width      57.2
#> 11 Sepal.Width  Petal.Width      25.9
#> 12 Petal.Length Petal.Width      33.9
tidy(iris_dist, diagonal = TRUE)
#> # A tibble: 10 x 3
#>    item1        item2        distance
#>    <fct>        <fct>           <dbl>
#>  1 Sepal.Length Sepal.Length      0  
#>  2 Sepal.Width  Sepal.Length     36.2
#>  3 Petal.Length Sepal.Length     29.0
#>  4 Petal.Width  Sepal.Length     57.2
#>  5 Sepal.Width  Sepal.Width       0  
#>  6 Petal.Length Sepal.Width      25.8
#>  7 Petal.Width  Sepal.Width      25.9
#>  8 Petal.Length Petal.Length      0  
#>  9 Petal.Width  Petal.Length     33.9
#> 10 Petal.Width  Petal.Width       0