Add k-prototype models via {clustMixType} to k_means()

ref: #63.
This method is pretty cool, and it doesn't force the user to only deal with numeric data
library(tidyclust)
library(modeldata)

k_means(num_clusters = 4) |>
  set_engine("clustMixType") |>
  fit(~., data = ames)
#> tidyclust cluster object
#> 
#> Distance type: standard 
#> 
#> Numeric predictors: 34 
#> Categorical predictors: 40 
#> Lambda: 538850067 
#> 
#> Number of Clusters: 4 
#> Cluster sizes: 1041 822 232 835 
#> Within cluster error: 5.811523e+12 5.100094e+12 2.366037e+12 4.376855e+12 
#> 
#> Cluster prototypes:
#>                           MS_SubClass               MS_Zoning Lot_Frontage
#> 1 One_Story_1946_and_Newer_All_Styles Residential_Low_Density     56.02402
#> 2 One_Story_1946_and_Newer_All_Styles Residential_Low_Density     53.33455
#> 3 One_Story_1946_and_Newer_All_Styles Residential_Low_Density     81.27155
#> 4            Two_Story_1946_and_Newer Residential_Low_Density     57.35449
#>    Lot_Area Street           Alley          Lot_Shape Land_Contour Utilities
#> 1  9696.003   Pave No_Alley_Access            Regular          Lvl    AllPub
#> 2  7997.472   Pave No_Alley_Access            Regular          Lvl    AllPub
#> 3 14861.828   Pave No_Alley_Access Slightly_Irregular          Lvl    AllPub
#> 4 11518.570   Pave No_Alley_Access Slightly_Irregular          Lvl    AllPub
#>   Lot_Config Land_Slope       Neighborhood Condition_1 Condition_2 Bldg_Type
#> 1     Inside        Gtl         North_Ames        Norm        Norm    OneFam
#> 2     Inside        Gtl           Old_Town        Norm        Norm    OneFam
#> 3     Inside        Gtl Northridge_Heights        Norm        Norm    OneFam
#> 4     Inside        Gtl      College_Creek        Norm        Norm    OneFam
#>   House_Style Overall_Cond Year_Built Year_Remod_Add Roof_Style Roof_Matl
#> 1   One_Story      Average   1968.405       1981.281      Gable   CompShg
#> 2   One_Story      Average   1945.224       1968.550      Gable   CompShg
#> 3   One_Story      Average   2000.034       2003.621        Hip   CompShg
#> 4   Two_Story      Average   1992.793       1998.083      Gable   CompShg
#>   Exterior_1st Exterior_2nd Mas_Vnr_Type Mas_Vnr_Area Exter_Cond Foundation
#> 1      MetalSd      MetalSd         None     81.90490    Typical     CBlock
#> 2      Wd Sdng      Wd Sdng         None     32.70681    Typical     CBlock
#> 3      VinylSd      VinylSd      BrkFace    359.86638    Typical      PConc
#> 4      VinylSd      VinylSd         None    120.45150    Typical      PConc
#>   Bsmt_Cond Bsmt_Exposure BsmtFin_Type_1 BsmtFin_SF_1 BsmtFin_Type_2
#> 1   Typical            No            Unf     3.882805            Unf
#> 2   Typical            No            Unf     4.807786            Unf
#> 3   Typical            Gd            GLQ     3.642241            Unf
#> 4   Typical            No            GLQ     4.073054            Unf
#>   BsmtFin_SF_2 Bsmt_Unf_SF Total_Bsmt_SF Heating Heating_QC Central_Air
#> 1     63.27185    510.0701     1010.8838    GasA  Excellent           Y
#> 2     43.27737    470.4745      761.1472    GasA    Typical           Y
#> 3     40.68534    675.5647     1703.6724    GasA  Excellent           Y
#> 4     41.62635    675.0132     1205.9090    GasA  Excellent           Y
#>   Electrical First_Flr_SF Second_Flr_SF Gr_Liv_Area Bsmt_Full_Bath
#> 1      SBrkr    1123.1268      250.3929    1377.184      0.4351585
#> 2      SBrkr     920.7153      224.7470    1154.627      0.2603406
#> 3      SBrkr    1749.4741      507.3233    2259.263      0.8189655
#> 4      SBrkr    1276.1952      502.7377    1781.068      0.4862275
#>   Bsmt_Half_Bath Full_Bath Half_Bath Bedroom_AbvGr Kitchen_AbvGr TotRms_AbvGrd
#> 1     0.09221902  1.449568 0.3563881      2.874159      1.040346      6.150817
#> 2     0.05717762  1.189781 0.1532847      2.684915      1.091241      5.729927
#> 3     0.02155172  2.060345 0.5905172      2.956897      1.000000      8.129310
#> 4     0.03712575  1.946108 0.5724551      2.967665      1.015569      7.040719
#>   Functional Fireplaces Garage_Type Garage_Finish Garage_Cars Garage_Area
#> 1        Typ  0.5590778      Attchd           Unf    1.672430    443.0221
#> 2        Typ  0.2323601      Detchd           Unf    1.172749    319.8139
#> 3        Typ  1.1939655      Attchd           Fin    2.793103    780.7759
#> 4        Typ  0.8455090      Attchd           RFn    2.182036    574.4623
#>   Garage_Cond Paved_Drive Wood_Deck_SF Open_Porch_SF Enclosed_Porch
#> 1     Typical       Paved     85.30548      37.92891      22.932757
#> 2     Typical       Paved     46.16423      23.73844      37.332117
#> 3     Typical       Paved    186.15517      86.65086       9.099138
#> 4     Typical       Paved    125.45509      72.06347      12.877844
#>   Three_season_porch Screen_Porch Pool_Area Pool_QC    Fence Misc_Feature
#> 1          3.4082613    20.084534  2.529299 No_Pool No_Fence         None
#> 2          0.7177616     7.569343  0.000000 No_Pool No_Fence         None
#> 3          3.6637931    23.030172  3.995690 No_Pool No_Fence         None
#> 4          3.1233533    17.261078  3.608383 No_Pool No_Fence         None
#>   Misc_Val  Mo_Sold Year_Sold Sale_Type Sale_Condition Sale_Price Longitude
#> 1 79.91066 6.139289  2007.777       WD          Normal   155827.5 -93.63832
#> 2 34.85888 6.131387  2007.832       WD          Normal   109511.9 -93.63427
#> 3  0.00000 6.547414  2007.668       WD          Normal   377841.8 -93.65087
#> 4 43.73653 6.302994  2007.800       WD          Normal   227350.8 -93.65488
#>   Latitude
#> 1 42.03415
#> 2 42.02832
#> 3 42.04770
#> 4 42.03729
tidymodels / tidyclust

Add k-prototype models via {clustMixType} to k_means() #155
