This document demonstrates some basic uses of recipes. First, some definitions are required:

variables are the original (raw) data columns in a
data frame or tibble. For example, in a traditional formula
Y ~ A + B + A:B
, the variables areA
,B
, andY
. 
roles define how variables will be used in the
model. Examples are:
predictor
(independent variables),response
, andcase weight
. This is meant to be openended and extensible. 
terms are columns in a design matrix such as
A
,B
, andA:B
. These can be other derived entities that are grouped, such as a set of principal components or a set of columns, that define a basis function for a variable. These are synonymous with features in machine learning. Variables that havepredictor
roles would automatically be main effect terms.
An Example
The packages contains a data set used to predict whether a person
will pay back a bank loan. It has 13 predictor columns and a factor
variable Status
(the outcome). We will first separate the
data into a training and test set:
library(recipes)
library(rsample)
library(modeldata)
data("credit_data")
set.seed(55)
train_test_split < initial_split(credit_data)
credit_train < training(train_test_split)
credit_test < testing(train_test_split)
Note that there are some missing values in these data:
vapply(credit_train, function(x) mean(!is.na(x)), numeric(1))
#> Status Seniority Home Time Age Marital Records
#> 1.000 1.000 0.998 1.000 1.000 1.000 1.000
#> Job Expenses Income Assets Debt Amount Price
#> 0.999 1.000 0.910 0.989 0.996 1.000 1.000
Rather than remove these, their values will be imputed.
The idea is that the preprocessing operations will all be created using the training set and then these steps will be applied to both the training and test set.
An Initial Recipe
First, we will create a recipe object from the original data and then specify the processing steps.
Recipes can be created manually by sequentially adding roles to variables in a data set.
If the analysis only requires outcomes and predictors, the easiest way to create the initial recipe is to use the standard formula method:
rec_obj < recipe(Status ~ ., data = credit_train)
rec_obj
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 13
The data contained in the data
argument need not be the
training set; this data is only used to catalog the names of the
variables and their types (e.g. numeric, etc.).
(Note that the formula method is used here to declare the variables,
their roles and nothing else. If you use inline functions
(e.g. log
) it will complain. These types of operations can
be added later.)
Preprocessing Steps
From here, preprocessing steps for some step X can be added sequentially in one of two ways:
< step_{X}(rec_obj, arguments) ## or
rec_obj < rec_obj %>% step_{X}(arguments) rec_obj
step_dummy
and the other functions will always return
updated recipes.
One other important facet of the code is the method for specifying
which variables should be used in different steps. The manual page
?selections
has more details but dplyr
like
selector functions can be used:
 use basic variable names (e.g.
x1, x2
), 
dplyr
functions for selecting variables:contains()
,ends_with()
,everything()
,matches()
,num_range()
, andstarts_with()
,  functions that subset on the role of the variables that have been
specified so far:
all_outcomes()
,all_predictors()
,has_role()
,
 similar functions for the type of data:
all_nominal()
,all_numeric()
, andhas_type()
, or  compound selectors such as
all_nominal_predictors()
orall_numeric_predictors()
.
Note that the methods listed above are the only ones that can be used to select variables inside the steps. Also, minus signs can be used to deselect variables.
For our data, we can add an operation to impute the predictors. There
are many ways to do this and recipes
includes a few steps
for this purpose:
grep("impute_", ls("package:recipes"), value = TRUE)
#> [1] "step_impute_bag" "step_impute_knn" "step_impute_linear"
#> [4] "step_impute_lower" "step_impute_mean" "step_impute_median"
#> [7] "step_impute_mode" "step_impute_roll"
Here, Knearest neighbor imputation will be used. This works for both numeric and nonnumeric predictors and defaults K to five To do this, it selects all predictors and then removes those that are numeric:
imputed < rec_obj %>%
step_impute_knn(all_predictors())
imputed
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 13
#>
#> Operations:
#>
#> Knearest neighbor imputation for all_predictors()
It is important to realize that the specific variables have not been declared yet (as shown when the recipe is printed above). In some preprocessing steps, variables will be added or removed from the current list of possible variables.
Since some predictors are categorical in nature (i.e. nominal), it
would make sense to convert these factor predictors into numeric dummy
variables (aka indicator variables) using step_dummy()
. To
do this, the step selects all nonnumeric predictors:
ind_vars < imputed %>%
step_dummy(all_nominal_predictors())
ind_vars
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 13
#>
#> Operations:
#>
#> Knearest neighbor imputation for all_predictors()
#> Dummy variables from all_nominal_predictors()
At this point in the recipe, all of the predictor should be encoded as numeric, we can further add more steps to center and scale them:
standardized < ind_vars %>%
step_center(all_numeric_predictors()) %>%
step_scale(all_numeric_predictors())
standardized
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 13
#>
#> Operations:
#>
#> Knearest neighbor imputation for all_predictors()
#> Dummy variables from all_nominal_predictors()
#> Centering for all_numeric_predictors()
#> Scaling for all_numeric_predictors()
If these are the only preprocessing steps for the predictors, we can
now estimate the means and standard deviations from the training set.
The prep
function is used with a recipe and a data set:
trained_rec < prep(standardized, training = credit_train)
trained_rec
#> Recipe
#>
#> Inputs:
#>
#> role #variables
#> outcome 1
#> predictor 13
#>
#> Training data contained 3340 data points and 322 incomplete rows.
#>
#> Operations:
#>
#> Knearest neighbor imputation for Seniority, Home, Time, Age, Marital, Rec... [trained]
#> Dummy variables from Home, Marital, Records, Job [trained]
#> Centering for Seniority, Time, Age, Expenses, Income, A... [trained]
#> Scaling for Seniority, Time, Age, Expenses, Income, A... [trained]
Note that the real variables are listed (e.g. Home
etc.)
instead of the selectors (all_numeric_predictors()
).
Now that the statistics have been estimated, the preprocessing can be applied to the training and test set:
train_data < bake(trained_rec, new_data = credit_train)
test_data < bake(trained_rec, new_data = credit_test)
bake
returns a tibble that, by default, includes all of
the variables:
class(test_data)
#> [1] "tbl_df" "tbl" "data.frame"
test_data
#> # A tibble: 1,114 × 23
#> Seniority Time Age Expenses Income Assets Debt Amount Price
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.09 0.924 1.88 0.385 0.131 0.488 0.295 0.0817 0.297
#> 2 0.977 0.924 0.459 1.77 0.437 0.845 0.295 0.333 0.760
#> 3 0.977 0.103 0.349 1.77 0.783 0.488 0.295 0.333 0.00254
#> 4 0.247 0.103 0.280 0.231 0.207 0.133 0.295 0.229 0.171
#> 5 0.125 0.718 0.729 0.231 0.258 0.222 0.295 0.807 0.854
#> 6 0.855 0.924 0.549 1.05 0.0539 0.488 0.295 0.436 0.331
#> 7 2.31 0.924 0.349 0.949 0.0155 0.488 0.295 0.185 0.0475
#> 8 0.848 0.718 0.529 1.00 1.40 0.133 0.295 1.58 1.69
#> 9 0.977 0.718 1.27 0.538 0.246 0.266 0.295 1.32 1.65
#> 10 0.855 0.514 0.100 0.744 0.540 0.488 0.295 0.185 0.800
#> # … with 1,104 more rows, and 14 more variables: Status <fct>,
#> # Home_other <dbl>, Home_owner <dbl>, Home_parents <dbl>,
#> # Home_priv <dbl>, Home_rent <dbl>, Marital_married <dbl>,
#> # Marital_separated <dbl>, Marital_single <dbl>, Marital_widow <dbl>,
#> # Records_yes <dbl>, Job_freelance <dbl>, Job_others <dbl>,
#> # Job_partime <dbl>
vapply(test_data, function(x) mean(!is.na(x)), numeric(1))
#> Seniority Time Age Expenses
#> 1 1 1 1
#> Income Assets Debt Amount
#> 1 1 1 1
#> Price Status Home_other Home_owner
#> 1 1 1 1
#> Home_parents Home_priv Home_rent Marital_married
#> 1 1 1 1
#> Marital_separated Marital_single Marital_widow Records_yes
#> 1 1 1 1
#> Job_freelance Job_others Job_partime
#> 1 1 1
Selectors can also be used. For example, if only the predictors are
needed, you can use
bake(object, new_data, all_predictors())
.
There are a number of other steps included in the package:
#> [1] "step_arrange" "step_bagimpute"
#> [3] "step_bin2factor" "step_BoxCox"
#> [5] "step_bs" "step_center"
#> [7] "step_classdist" "step_corr"
#> [9] "step_count" "step_cut"
#> [11] "step_date" "step_depth"
#> [13] "step_discretize" "step_dummy"
#> [15] "step_dummy_extract" "step_dummy_multi_choice"
#> [17] "step_factor2string" "step_filter"
#> [19] "step_filter_missing" "step_geodist"
#> [21] "step_harmonic" "step_holiday"
#> [23] "step_hyperbolic" "step_ica"
#> [25] "step_impute_bag" "step_impute_knn"
#> [27] "step_impute_linear" "step_impute_lower"
#> [29] "step_impute_mean" "step_impute_median"
#> [31] "step_impute_mode" "step_impute_roll"
#> [33] "step_indicate_na" "step_integer"
#> [35] "step_interact" "step_intercept"
#> [37] "step_inverse" "step_invlogit"
#> [39] "step_isomap" "step_knnimpute"
#> [41] "step_kpca" "step_kpca_poly"
#> [43] "step_kpca_rbf" "step_lag"
#> [45] "step_lincomb" "step_log"
#> [47] "step_logit" "step_lowerimpute"
#> [49] "step_meanimpute" "step_medianimpute"
#> [51] "step_modeimpute" "step_mutate"
#> [53] "step_mutate_at" "step_naomit"
#> [55] "step_nnmf" "step_nnmf_sparse"
#> [57] "step_normalize" "step_novel"
#> [59] "step_ns" "step_num2factor"
#> [61] "step_nzv" "step_ordinalscore"
#> [63] "step_other" "step_pca"
#> [65] "step_percentile" "step_pls"
#> [67] "step_poly" "step_poly_bernstein"
#> [69] "step_profile" "step_range"
#> [71] "step_ratio" "step_regex"
#> [73] "step_relevel" "step_relu"
#> [75] "step_rename" "step_rename_at"
#> [77] "step_rm" "step_rollimpute"
#> [79] "step_sample" "step_scale"
#> [81] "step_select" "step_shuffle"
#> [83] "step_slice" "step_spatialsign"
#> [85] "step_spline_b" "step_spline_convex"
#> [87] "step_spline_monotone" "step_spline_natural"
#> [89] "step_spline_nonnegative" "step_sqrt"
#> [91] "step_string2factor" "step_time"
#> [93] "step_unknown" "step_unorder"
#> [95] "step_window" "step_YeoJohnson"
#> [97] "step_zv"
Checks
Another type of operation that can be added to a recipes is a check. Checks conduct some sort of data validation and, if no issue is found, returns the data asis; otherwise, an error is thrown.
For example, check_missing
will fail if any of the
variables selected for validation have missing values. This check is
done when the recipe is prepared as well as when any data are baked.
Checks are added in the same way as steps:
trained_rec < trained_rec %>%
check_missing(contains("Marital"))
Currently, recipes
includes:
#> [1] "check_class" "check_cols" "check_missing"
#> [4] "check_name" "check_new_data" "check_new_values"
#> [7] "check_range" "check_type"