Data dummification is also known as one hot encoding or feature binarization. It turns each category to a distinct column with binary (numeric) values.
dummify(data, maxcat = 50L, select = NULL)
| data | input data |
|---|---|
| maxcat | maximum categories allowed for each discrete feature. Default is 50. |
| select | names of selected features to be dummified. Default is |
dummified dataset (discrete features only) preserving original features. However, column order might be different.
Continuous features will be ignored if added in select.
select features will be ignored if categories exceed maxcat.
This is different from model.matrix, where the latter aims to create a full rank matrix for regression-like use cases. If your intention is to create a design matrix, use model.matrix instead.
#> 'data.frame': 150 obs. of 7 variables: #> $ Sepal.Length : num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... #> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... #> $ Petal.Length : num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... #> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... #> $ Species_setosa : int 1 1 1 1 1 1 1 1 1 1 ... #> $ Species_versicolor: int 0 0 0 0 0 0 0 0 0 0 ... #> $ Species_virginica : int 0 0 0 0 0 0 0 0 0 0 ... #> - attr(*, ".internal.selfref")=<externalptr>## Dummify diamonds dataset ignoring features with more than 5 categories data("diamonds", package = "ggplot2") str(dummify(diamonds, maxcat = 5))#> #> #>#> tibble [53,940 × 14] (S3: tbl_df/tbl/data.frame) #> $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ... #> $ depth : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ... #> $ table : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ... #> $ price : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ... #> $ x : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ... #> $ y : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ... #> $ z : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ... #> $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ... #> $ clarity : Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ... #> $ cut_Fair : int [1:53940] 0 0 0 0 0 0 0 0 1 0 ... #> $ cut_Good : int [1:53940] 0 0 1 0 1 0 0 0 0 0 ... #> $ cut_Ideal : int [1:53940] 1 0 0 0 0 0 0 0 0 0 ... #> $ cut_Premium : int [1:53940] 0 1 0 1 0 0 0 0 0 0 ... #> $ cut_Very.Good: int [1:53940] 0 0 0 0 0 1 1 1 0 1 ... #> - attr(*, ".internal.selfref")=<externalptr>#> tibble [53,940 × 20] (S3: tbl_df/tbl/data.frame) #> $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ... #> $ depth : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ... #> $ table : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ... #> $ price : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ... #> $ x : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ... #> $ y : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ... #> $ z : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ... #> $ clarity : Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ... #> $ cut_Fair : int [1:53940] 0 0 0 0 0 0 0 0 1 0 ... #> $ cut_Good : int [1:53940] 0 0 1 0 1 0 0 0 0 0 ... #> $ cut_Ideal : int [1:53940] 1 0 0 0 0 0 0 0 0 0 ... #> $ cut_Premium : int [1:53940] 0 1 0 1 0 0 0 0 0 0 ... #> $ cut_Very.Good: int [1:53940] 0 0 0 0 0 1 1 1 0 1 ... #> $ color_D : int [1:53940] 0 0 0 0 0 0 0 0 0 0 ... #> $ color_E : int [1:53940] 1 1 1 0 0 0 0 0 1 0 ... #> $ color_F : int [1:53940] 0 0 0 0 0 0 0 0 0 0 ... #> $ color_G : int [1:53940] 0 0 0 0 0 0 0 0 0 0 ... #> $ color_H : int [1:53940] 0 0 0 0 0 0 0 1 0 1 ... #> $ color_I : int [1:53940] 0 0 0 1 0 0 1 0 0 0 ... #> $ color_J : int [1:53940] 0 0 0 0 1 1 0 0 0 0 ... #> - attr(*, ".internal.selfref")=<externalptr>