tests/testthat/_snaps/print.md

In tidyclust: A Common API to Clustering

print.cluster_spec() works

Code
  print(k_means(num_clusters = 3))
Output
  K Means Cluster Specification (partition)

  Main Arguments:
    num_clusters = 3

  Computational engine: stats

print.cluster_spec() works with engine set

Code
  print(set_engine(k_means(num_clusters = 3), "stats"))
Output
  K Means Cluster Specification (partition)

  Main Arguments:
    num_clusters = 3

  Computational engine: stats

print.cluster_fit() works

Code
  print(fit)
Output
  tidyclust cluster object

  K-means clustering with 3 clusters of sizes 7, 11, 14

  Cluster means:
         mpg cyl     disp        hp     drat       wt     qsec        vs
  1 19.74286   6 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286
  3 26.66364   4 105.1364  82.63636 4.070909 2.285727 19.13727 0.9090909
  2 15.10000   8 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
           am     gear     carb
  1 0.4285714 3.857143 3.428571
  3 0.7272727 4.090909 1.545455
  2 0.1428571 3.285714 3.500000

  Clustering vector:
            Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
                    1                   1                   2                   1 
    Hornet Sportabout             Valiant          Duster 360           Merc 240D 
                    3                   1                   3                   2 
             Merc 230            Merc 280           Merc 280C          Merc 450SE 
                    2                   1                   1                   3 
           Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
                    3                   3                   3                   3 
    Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
                    3                   2                   2                   2 
        Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
                    2                   3                   3                   3 
     Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
                    3                   2                   2                   2 
       Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
                    3                   1                   3                   2

  Within cluster sum of squares by cluster:
  [1] 13954.34 11848.37 93643.90
   (between_SS / total_SS =  80.8 %)

  Available components:

  [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
  [6] "betweenss"    "size"         "iter"         "ifault"

print.cluster_fit() works for hier_clust

Code
  print(fit)
Output
  tidyclust cluster object


  Call:
  stats::hclust(d = stats::as.dist(dmat), method = linkage_method)

  Cluster method   : complete 
  Number of objects: 32

print.cluster_fit() shows elapsed time when verbosity > 1

Code
  print(fit)
Output
  tidyclust cluster object

  Fit time: <scrubbed>
  K-means clustering with 3 clusters of sizes 7, 11, 14

  Cluster means:
         mpg cyl     disp        hp     drat       wt     qsec        vs
  1 19.74286   6 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286
  3 26.66364   4 105.1364  82.63636 4.070909 2.285727 19.13727 0.9090909
  2 15.10000   8 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
           am     gear     carb
  1 0.4285714 3.857143 3.428571
  3 0.7272727 4.090909 1.545455
  2 0.1428571 3.285714 3.500000

  Clustering vector:
            Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
                    1                   1                   2                   1 
    Hornet Sportabout             Valiant          Duster 360           Merc 240D 
                    3                   1                   3                   2 
             Merc 230            Merc 280           Merc 280C          Merc 450SE 
                    2                   1                   1                   3 
           Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
                    3                   3                   3                   3 
    Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
                    3                   2                   2                   2 
        Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
                    2                   3                   3                   3 
     Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
                    3                   2                   2                   2 
       Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
                    3                   1                   3                   2

  Within cluster sum of squares by cluster:
  [1] 13954.34 11848.37 93643.90
   (between_SS / total_SS =  80.8 %)

  Available components:

  [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
  [6] "betweenss"    "size"         "iter"         "ifault"

print.cluster_fit() handles try-error

Code
  print(fit)
Output
  tidyclust cluster object

  Cluster fit failed with error:
   Error in try(stop("intentional error for testing"), silent = TRUE) : 
    intentional error for testing

print.cluster_spec() works with translated spec

Code
  print(spec)
Output
  K Means Cluster Specification (partition)

  Main Arguments:
    num_clusters = 3

  Computational engine: stats

  Model fit template:
  tidyclust::.k_means_fit_stats(x = missing_arg(), centers = missing_arg(), 
      centers = 3)

tidyclust documentation built on June 20, 2026, 9:08 a.m.