broomstick

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Convert decision tree objects into tidy data frames with broomstick.

The goal of broomstick is to extend the broom package to work with decision trees. It is currently borrowing heavily from the prototype package treezy.

Installation

You can install broomstick from github with:

# install.packages("remotes")
remotes::install_github("njtierney/broomstick")

Examples

rpart

library(rpart)
library(broomstick)

fit_rpart <- rpart(Kyphosis ~ Age + Number + Start, 
                   data = kyphosis)

tidy(fit_rpart)
#> # A tibble: 3 x 2
#>   variable importance
#>   <chr>         <dbl>
#> 1 Start          8.20
#> 2 Age            3.10
#> 3 Number         1.52
augment(fit_rpart)
#> # A tibble: 81 x 6
#>    Kyphosis   Age Number Start .fitted[,"absen… [,"present"] .resid[,"absent…
#>    <fct>    <int>  <int> <int>            <dbl>        <dbl>            <dbl>
#>  1 absent      71      3     5            0.421        0.579           -0.579
#>  2 absent     158      3    14            0.857        0.143           -0.143
#>  3 present    128      4     5            0.421        0.579           -1.58 
#>  4 absent       2      5     1            0.421        0.579           -0.579
#>  5 absent       1      4    15            1            0                0    
#>  6 absent       1      2    16            1            0                0    
#>  7 absent      61      2    17            1            0                0    
#>  8 absent      37      3    16            1            0                0    
#>  9 absent     113      2    16            1            0                0    
#> 10 present     59      6    12            0.429        0.571           -1.57 
#> # … with 71 more rows, and 1 more variable: [,"present"] <dbl>

gbm (Boosted Regression Tree)

library(gbm)
#> Loaded gbm 2.1.8
library(MASS)
fit_gbm <- gbm(calories ~., data = UScereal)
#> Distribution not specified, assuming gaussian ...

tidy(fit_gbm)
#> # A tibble: 10 x 2
#>    variable importance
#>    <chr>         <dbl>
#>  1 1            30.7  
#>  2 2            23.5  
#>  3 3            14.3  
#>  4 4            11.5  
#>  5 5             8.14 
#>  6 6             6.75 
#>  7 7             2.96 
#>  8 8             1.95 
#>  9 9             0.198
#> 10 10            0

random forest

library(randomForest)
#> randomForest 4.6-14
#> Type rfNews() to see new features/changes/bug fixes.
ozone_rf <- randomForest(Ozone ~ ., 
                         data = airquality, 
                         importance = TRUE,
                         na.action = na.omit)
tidy(ozone_rf)
#> Warning: This function is deprecated as of broom 0.7.0 and will be removed from
#> a future release. Please see tibble::as_tibble().
#> # A tibble: 5 x 4
#>   term    X.IncMSE IncNodePurity imp_sd
#>   <chr>      <dbl>         <dbl>  <dbl>
#> 1 Solar.R    151.         18103.   11.0
#> 2 Wind       326.         29153.   17.6
#> 3 Temp       485.         35513.   18.6
#> 4 Month      107.         10803.   10.4
#> 5 Day         62.5        16332.   10.6
glance(ozone_rf)
#>   mean_mse  mean_rsq
#> 1 342.6329 0.6877532
augment(ozone_rf)
#> Warning in augment.randomForest.method(x, data, ...): casewise importance
#> measures are not available. Run randomForest(..., localImp = TRUE) for more
#> detailed results.
#>     Ozone Solar.R Wind Temp Month Day .oob_times   .fitted
#> 1      41     190  7.4   67     5   1        188  42.25838
#> 2      36     118  8.0   72     5   2        197  26.11229
#> 3      12     149 12.6   74     5   3        197  24.96252
#> 4      18     313 11.5   62     5   4        168  24.20249
#> 5      NA      NA 14.3   56     5   5         NA        NA
#> 6      28      NA 14.9   66     5   6         NA        NA
#> 7      23     299  8.6   65     5   7        180  29.26588
#> 8      19      99 13.8   59     5   8        181  18.78945
#> 9       8      19 20.1   61     5   9        184  14.48941
#> 10     NA     194  8.6   69     5  10         NA        NA
#> 11      7      NA  6.9   74     5  11         NA        NA
#> 12     16     256  9.7   69     5  12        160  20.25724
#> 13     11     290  9.2   66     5  13        164  22.67090
#> 14     14     274 10.9   68     5  14        174  22.40194
#> 15     18      65 13.2   58     5  15        199  13.99352
#> 16     14     334 11.5   64     5  16        202  23.91045
#> 17     34     307 12.0   66     5  17        185  19.91413
#> 18      6      78 18.4   57     5  18        194  18.64684
#> 19     30     322 11.5   68     5  19        175  19.05428
#> 20     11      44  9.7   62     5  20        175  11.79332
#> 21      1       8  9.7   59     5  21        183  13.96870
#> 22     11     320 16.6   73     5  22        174  24.16417
#> 23      4      25  9.7   61     5  23        190  14.33131
#> 24     32      92 12.0   61     5  24        176  18.18554
#> 25     NA      66 16.6   57     5  25         NA        NA
#> 26     NA     266 14.9   58     5  26         NA        NA
#> 27     NA      NA  8.0   57     5  27         NA        NA
#> 28     23      13 12.0   67     5  28        180  20.88715
#> 29     45     252 14.9   81     5  29        189  46.32726
#> 30    115     223  5.7   79     5  30        191  55.56009
#> 31     37     279  7.4   76     5  31        186  46.92523
#> 32     NA     286  8.6   78     6   1         NA        NA
#> 33     NA     287  9.7   74     6   2         NA        NA
#> 34     NA     242 16.1   67     6   3         NA        NA
#> 35     NA     186  9.2   84     6   4         NA        NA
#> 36     NA     220  8.6   85     6   5         NA        NA
#> 37     NA     264 14.3   79     6   6         NA        NA
#> 38     29     127  9.7   82     6   7        188  27.59685
#> 39     NA     273  6.9   87     6   8         NA        NA
#> 40     71     291 13.8   90     6   9        191  49.68501
#> 41     39     323 11.5   87     6  10        168  54.05047
#> 42     NA     259 10.9   93     6  11         NA        NA
#> 43     NA     250  9.2   92     6  12         NA        NA
#> 44     23     148  8.0   82     6  13        188  34.53176
#> 45     NA     332 13.8   80     6  14         NA        NA
#> 46     NA     322 11.5   79     6  15         NA        NA
#> 47     21     191 14.9   77     6  16        179  26.24771
#> 48     37     284 20.7   72     6  17        171  22.30556
#> 49     20      37  9.2   65     6  18        191  15.30853
#> 50     12     120 11.5   73     6  19        170  22.07452
#> 51     13     137 10.3   76     6  20        183  22.79000
#> 52     NA     150  6.3   77     6  21         NA        NA
#> 53     NA      59  1.7   76     6  22         NA        NA
#> 54     NA      91  4.6   76     6  23         NA        NA
#> 55     NA     250  6.3   76     6  24         NA        NA
#> 56     NA     135  8.0   75     6  25         NA        NA
#> 57     NA     127  8.0   78     6  26         NA        NA
#> 58     NA      47 10.3   73     6  27         NA        NA
#> 59     NA      98 11.5   80     6  28         NA        NA
#> 60     NA      31 14.9   77     6  29         NA        NA
#> 61     NA     138  8.0   83     6  30         NA        NA
#> 62    135     269  4.1   84     7   1        184  71.89886
#> 63     49     248  9.2   85     7   2        167  64.37778
#> 64     32     236  9.2   81     7   3        187  45.91065
#> 65     NA     101 10.9   84     7   4         NA        NA
#> 66     64     175  4.6   83     7   5        174  75.24531
#> 67     40     314 10.9   83     7   6        183  49.88416
#> 68     77     276  5.1   88     7   7        200  85.00543
#> 69     97     267  6.3   92     7   8        173  86.20607
#> 70     97     272  5.7   92     7   9        173  87.64815
#> 71     85     175  7.4   89     7  10        153  73.97870
#> 72     NA     139  8.6   82     7  11         NA        NA
#> 73     10     264 14.3   73     7  12        178  28.75746
#> 74     27     175 14.9   81     7  13        164  39.57709
#> 75     NA     291 14.9   91     7  14         NA        NA
#> 76      7      48 14.3   80     7  15        187  25.77671
#> 77     48     260  6.9   81     7  16        184  46.63660
#> 78     35     274 10.3   82     7  17        194  37.74192
#> 79     61     285  6.3   84     7  18        192  69.86593
#> 80     79     187  5.1   87     7  19        184  73.84940
#> 81     63     220 11.5   85     7  20        195  51.65887
#> 82     16       7  6.9   74     7  21        187  27.20911
#> 83     NA     258  9.7   81     7  22         NA        NA
#> 84     NA     295 11.5   82     7  23         NA        NA
#> 85     80     294  8.6   86     7  24        197  58.13895
#> 86    108     223  8.0   85     7  25        177  72.65444
#> 87     20      81  8.6   82     7  26        181  45.41705
#> 88     52      82 12.0   86     7  27        176  43.16430
#> 89     82     213  7.4   88     7  28        178  73.00805
#> 90     50     275  7.4   86     7  29        190  73.45676
#> 91     64     253  7.4   83     7  30        188  62.06175
#> 92     59     254  9.2   81     7  31        169  52.61721
#> 93     39      83  6.9   81     8   1        171  40.68605
#> 94      9      24 13.8   81     8   2        181  27.34251
#> 95     16      77  7.4   82     8   3        184  37.33898
#> 96     78      NA  6.9   86     8   4         NA        NA
#> 97     35      NA  7.4   85     8   5         NA        NA
#> 98     66      NA  4.6   87     8   6         NA        NA
#> 99    122     255  4.0   89     8   7        179  91.82286
#> 100    89     229 10.3   90     8   8        191  66.99773
#> 101   110     207  8.0   90     8   9        200  75.82581
#> 102    NA     222  8.6   92     8  10         NA        NA
#> 103    NA     137 11.5   86     8  11         NA        NA
#> 104    44     192 11.5   86     8  12        208  56.88581
#> 105    28     273 11.5   82     8  13        193  38.49338
#> 106    65     157  9.7   80     8  14        195  30.41331
#> 107    NA      64 11.5   79     8  15         NA        NA
#> 108    22      71 10.3   77     8  16        200  21.41058
#> 109    59      51  6.3   79     8  17        193  37.36250
#> 110    23     115  7.4   76     8  18        184  29.75137
#> 111    31     244 10.9   78     8  19        196  34.67882
#> 112    44     190 10.3   78     8  20        206  34.74004
#> 113    21     259 15.5   77     8  21        176  25.74759
#> 114     9      36 14.3   72     8  22        181  16.11446
#> 115    NA     255 12.6   75     8  23         NA        NA
#> 116    45     212  9.7   79     8  24        203  56.69674
#> 117   168     238  3.4   81     8  25        194  67.14026
#> 118    73     215  8.0   86     8  26        166  85.68238
#> 119    NA     153  5.7   88     8  27         NA        NA
#> 120    76     203  9.7   97     8  28        191  74.62102
#> 121   118     225  2.3   94     8  29        182 104.91248
#> 122    84     237  6.3   96     8  30        186  90.26471
#> 123    85     188  6.3   94     8  31        192  81.15797
#> 124    96     167  6.9   91     9   1        181  72.59516
#> 125    78     197  5.1   92     9   2        179  81.48244
#> 126    73     183  2.8   93     9   3        183  93.03619
#> 127    91     189  4.6   93     9   4        173  77.15980
#> 128    47      95  7.4   87     9   5        181  52.03776
#> 129    32      92 15.5   84     9   6        174  37.75896
#> 130    20     252 10.9   80     9   7        184  40.05420
#> 131    23     220 10.3   78     9   8        196  35.00435
#> 132    21     230 10.9   75     9   9        188  28.14067
#> 133    24     259  9.7   73     9  10        187  28.94787
#> 134    44     236 14.9   81     9  11        174  29.27522
#> 135    21     259 15.5   76     9  12        183  22.60115
#> 136    28     238  6.3   77     9  13        193  46.98184
#> 137     9      24 10.9   71     9  14        200  16.55020
#> 138    13     112 11.5   71     9  15        173  21.91143
#> 139    46     237  6.9   78     9  16        186  33.19160
#> 140    18     224 13.8   67     9  17        181  22.12481
#> 141    13      27 10.3   76     9  18        190  16.28911
#> 142    24     238 10.3   68     9  19        181  22.70507
#> 143    16     201  8.0   82     9  20        170  43.56690
#> 144    13     238 12.6   64     9  21        178  22.78182
#> 145    23      14  9.2   71     9  22        196  15.44979
#> 146    36     139 10.3   81     9  23        171  25.90560
#> 147     7      49 10.3   69     9  24        187  20.33940
#> 148    14      20 16.6   63     9  25        188  21.48303
#> 149    30     193  6.9   70     9  26        175  36.76303
#> 150    NA     145 13.2   77     9  27         NA        NA
#> 151    14     191 14.3   75     9  28        181  25.86641
#> 152    18     131  8.0   76     9  29        188  32.89799
#> 153    20     223 11.5   68     9  30        174  34.24170

Broomstick

:evergreen_tree: broom helpers for decision tree methods (rpart, randomForest, and more!) :evergreen_tree:

Broomstick Info

⭐ Stars28
πŸ”— Homepagebroomstick.njtierney.com
πŸ”— Source Codegithub.com
πŸ•’ Last Update2 years ago
πŸ•’ Created5 years ago
🐞 Open Issues8
βž— Star-Issue Ratio4
😎 Authornjtierney