# recipes 0.1.10 2020-03-18

## Breaking Changes

• renamed yj_trans() to yj_transform() to avoid conflicts.

## Other Changes

• Added flexible naming options for new columns created by step_depth() and step_classdist() (#262).

• Small changes for base R’s stringsAsFactors change.

# recipes 0.1.9 2020-01-07

• Delayed S3 method registration for tune::tunable() methods that live in recipes will now work correctly on R >=4.0.0 (#439, tidymodels/tune#146).

• step_relevel() added.

# 2019-12-18 recipes 0.1.8

## Breaking Changes

• The imputation steps do not change the data type being imputed now. Previously, if the data were integer, the data would be changed to numeric (for some step types). The change is breaking since the underlying data of imputed values are now saved as a list instead of a vector (for some step types).

• The data sets were moved to the new modeldata package.

• step_num2factor() was rewritten due to a bug that ignored the user-supplied levels (#425). The results of the transform argument are now required to be a function and levels must now be supplied.

## Other Changes

• Using a minus in the formula to recipes() is no longer allowed (it didn’t remove variables anyway). step_rm() or update_role() can be used instead.

• When using a selector that returns no columns, juice() and bake() will now return a tibble with as many rows as the original template data or the new_data respectively. This is more consistent with how selectors work in dplyr (#411).

• Code was added to explicitly register tunable methods when recipes is loaded. This is required because of changes occurring in R 4.0.

• check_class() checks if a variable is of the designated class. Class is either learned from the train set or provided in the check. (contributed by Edwin Thoen)

• step_normalize() and step_scale() gained a factor argument with values of 1 or 2 that can scale the standard deviations used to transform the data. (#380)

• bake() now produces a tibble with columns in the same order as juice() (#365)

# 2019-09-15 recipes 0.1.7

Release driven by changes in tidyr (v 1.0.0).

## Breaking Changes

format_selector()’s wdth argument has been renamed to width (#250).

## New Operations

• step_mutate_at(), step_rename(), and step_rename_at() were added.

## Other Changes

• The use of varying() will be deprecated in favor of an upcoming function tune(). No changes are need in this version, but subsequent versions will work with tune().

• format_ch_vec() and format_selector() are now exported (#250).

• check_new_values breaks bake if variable contains values that were not observed in the train set (contributed by Edwin Thoen)

• When no outcomes are in the recipe, using juice(object, all_outcomes() and bake(object, new_data, all_outcomes() will return a tibble with zero rows and zero columns (instead of failing). (#298). This will also occur when the selectors select no columns.

• As alternatives to step_kpca(), two separate steps were added called step_kpca_rbf() and step_kpca_poly(). The use of step_kpca() will print a deprecation message that it will be going away.

• step_nzv() and step_poly() had arguments promoted out of their options slot. options can be used in the short term but is deprecated.

• step_downsample() will replace the ratio argument with under_ratio and step_upsample() will replace it with over_ratio. ratio still works (for now) but issues a deprecation message.

• step_discretize() has arguments moved out of options too; the main arguments are now num_breaks (instead of cuts) and min_unique. Again, deprecation messages are issued with the old argument structure.

• Models using the dimRed package (step_kpca(), step_isomap(), and step_nnmf()) would silently fail if the projection method failed. An error is issued now.

• Methods were added for a future generic called tunable(). This outlines which parameters in a step can/could be tuned.

# 2019-07-02 recipes 0.1.6

Release driven by changes in rlang.

## Breaking Changes

• Since 2018, a warning has been issued when the wrong argument was used in bake(recipe, newdata). The depredation period is over and new_data is officially required.

• Previously, if step_other() did not collapse any levels, it would still add an “other” level to the factor. This would lump new factor levels into “other” when data were baked (as step_novel() does). This no longer occurs since it was inconsistent with ?step_other, which said that

“If no pooling is done the data are unmodified”.

## New Operations

• step_normalize() centers and scales the data (if you are, like Max, too lazy to use two separate steps).
• step_unknown() will convert missing data in categorical columns to “unknown” and update factor levels.

## Other Changes

• If threshold argument of step_other is greater than one then it specifies the minimum sample size before the levels of the factor are collapsed into the “other” category. #289

• step_knnimpute() can now pass two options to the underlying knn code, including the number of threads (#323).

• Due to changes by CRAN, step_nnmf() only works on versions of R >= 3.6.0 due to dependency issues.

• step_dummy() and step_other() are now tolerant to cases where that step’s selectors do not capture any columns. In this case, no modifications to the data are made. (#290, #348)

• step_dummy() can now retain the original columns that are used to make the dummy variables. (#328)

• step_other()’s print method only reports the variables with collapsed levels (as opposed to any column that was tested to see if it needed collapsing). (#338)

• step_pca(), step_kpca(), step_ica(), step_nnmf(), step_pls(), and step_isomap() now accept zero components. In this case, the original data are returned.

# 2019-03-21 recipes 0.1.5

Small release driven by changes in sample() in the current r-devel.

## Other Changes

• A new vignette discussing roles has been added.

• To provide infrastructure for finalizing varying parameters, an update() method for recipe steps has been added. This allows users to alter information in steps that have not yet been trained.

• step_interact will no longer fail if an interaction contains an interaction using column that has been previously filtered from the data. A warning is issued when this happens and no interaction terms will be created.

• step_corr was made more fault tolerant for cases where the data contain a zero-variance column or columns with missing values.

• Set the embedded environment to NULL in prep.step_dummy to reduce the file size of serialized recipe class objects when using saveRDS.

## Breaking Changes

• The tidy method for step_dummy now returns the original variable and the levels of the future dummy variables.

## Bug Fixes

• Updating the role of new columns generated by a recipe step no longer also updates NA roles of existing columns (#296).

# 2018-11-19 recipes 0.1.4

## Breaking Changes

• Several argument names were changed to be consistent with other tidymodels packages (e.g. dials) and the general tidyverse naming conventions.
• K in step_knnimpute was changed to neighbors. step_isomap had the number of neighbors promoted to a main argument called neighbors
• step_pca, step_pls, step_kpca, step_ica now use num_comp instead of num. , step_isomap uses num_terms instead of num.
• step_bagimpute moved nbagg out of the options and into a main argument trees.
• step_bs and step_ns has degrees of freedom promoted to a main argument with name deg_free. Also, step_bs had degree promoted to a main argument.
• step_BoxCox and step_YeoJohnson had nunique change to num_unique.
• bake, juice and other functions has newdata changed to new_data. For this version only, using newdata will only result in a wanring.
• Several steps had na.rm changed to na_rm.
• prep and a few steps had stringsAsFactors changed to strings_as_factors.
• add_role() can now only add new additional roles. To alter existing roles, use update_role(). This change also allows for the possibility of having multiple roles/types for one variable. #221

• All steps gain an id field that will be used in the future to reference other steps.

• The retain option to prep is now defaulted to TRUE. If verbose = TRUE, the approximate size of the data set is printed. #207

## New Operations

• step_integer converts data to ordered integers similar to LabelEncoder #123 and #185
• step_geodist can be used to calculate the distance between geocodes and a single reference location.
• step_arrange, step_filter, step_mutate, step_sample, and step_slice implement their dplyr analogs.
• step_nnmf computes the non-negative matrix factorization for data.

## Other Changes

• The rsample function prepper was moved to recipes (issue).
• A number of packages were moved from “Imports” to “Suggests” to reduce the install footprint. A function was added to prompt the user to install the needed packages when the relevant steps are invoked.
• step_step_string2factor will now accept factors and leave them as-is.
• step_knnimpute now excludes missing data in the variable to be imputed from the nearest-neighbor calculation. This would have resulted in some missing data to not be imputed (i.e. return another missing value).
• step_dummy now produces a warning (instead of failing) when non-factor columns are selected. Only factor columns are used; no conversion is done for character data. issue #186
• dummy_names gained a separator argument. issue #183
• step_downsample and step_upsample now have seed arguments for more control over randomness.
• broom is no longer used to get the tidy generic. These are now contained in the generics package.
• When a recipe is prepared, a running list of all columns is created and the last known use of each column is kept. This is to avoid bugs when a step that is skipped removes columns. issue #239

# 2018-06-16 recipes 0.1.3

## New Operations

• check_range breaks bake if variable range in new data is outside the range that was learned from the train set (contributed by Edwin Thoen)
• step_lag can lag variables in the data set (contributed by Alex Hayes).

• step_naomit removes rows with missing data for specific columns (contributed by Alex Hayes).

• step_rollimpute can be used to impute data in a sequence or series by estimating their values within a moving window.

• step_pls can conduct supervised feature extraction for predictors.

## Other Changes

• step_log gained an offset argument.

• step_log gained a signed argument (contributed by Edwin Thoen).

• The internal functions sel2char and printer have been exported to enable other packages to contain steps.

• When training new steps after some steps have been previously trained, the retain = TRUE option should be set on previous invocations of prep.

• For step_dummy:

• It can now compute the entire set of dummy variables per factor predictor using the one_hot = TRUE option. Thanks to Davis Vaughan.
• The contrast option was removed. The step uses the global option for contrasts.
• The step also produces missing indicator variables when the original factor has a missing value
• step_other will now convert novel levels of the factor to the “other” level.
• step_bin2factor now has an option to choose how the values are translated to the levels (contributed by Michael Levy).
• bake and juice can now export basic data frames.
• The okc data were updated with two additional columns.

## Bug Fixes

• issue 125 that prevented several steps from working with dplyr grouped data frames. (contributed by Jeffrey Arnold)

• issue 127 where options to step_discretize were not being passed to discretize.

# 2018-01-11 recipes 0.1.2

## General Changes

• Edwin Thoen suggested adding validation checks for certain data characteristics. This fed into the existing notion of expanding recipes beyond steps (see the non-step steps project). A new set of operations, called checks, can now be used. These should throw an informative error when the check conditions are not met and return the existing data otherwise.

• Steps now have a skip option that will not apply preprocessing when bake is used. See the article on skipping steps for more information.

## New Operations

• check_missing will validate that none of the specified variables contain missing data.

• detect_step can be used to check if a recipe contains a particular preprocessing operation.

• step_num2factor can be used to convert numeric data (especially integers) to factors.

• step_novel adds a new factor level to nominal variables that will be used when new data contain a level that did not exist when the recipe was prepared.

• step_profile can be used to generate design matrix grids for prediction profile plots of additive models where one variable is varied over a grid and all of the others are fixed at a single value.

• step_downsample and step_upsample can be used to change the number of rows in the data based on the frequency distributions of a factor variable in the training set. By default, this operation is only applied to the training set; bake ignores this operation.

• step_naomit drops rows when specified columns contain NA, similar to tidyr::drop_na.

• step_lag allows for the creation of lagged predictor columns.

## Other Changes

• step_spatialsign now has the option of removing missing data prior to computing the norm.

# 2017-11-20 recipes 0.1.1

• The default selectors for bake was changed from all_predictors() to everything().
• The verbose option for prep is now defaulted to FALSE
• A bug in step_dummy was fixed that makes sure that the correct binary variables are generated despite the levels or values of the incoming factor. Also, step_dummy now requires factor inputs.
• step_dummy also has a new default naming function that works better for factors. However, there is an extra argument (ordinal) now to the functions that can be passed to step_dummy.
• step_interact now allows for selectors (e.g. all_predictors() or starts_with("prefix") to be used in the interaction formula.
• step_YeoJohnson gained an na.rm option.
• dplyr::one_of was added to the list of selectors.
• step_bs adds B-spline basis functions.
• step_unorder converts ordered factors to unordered factors.
• step_count counts the number of instances that a pattern exists in a string.
• step_string2factor and step_factor2string can be used to move between encodings.
• step_lowerimpute is for numeric data where the values cannot be measured below a specific value. For these cases, random uniform values are used for the truncated values.
• A step to remove simple zero-variance variables was added (step_zv).
• A series of tidy methods were added for recipes and many (but not all) steps.
• In bake.recipe, the argument newdata is now without a default.
• bake and juice can now save the final processed data set in sparse format. Note that, as the steps are processed, a non-sparse data frame is used to store the results.
• A formula method was added for recipes to get a formula with the outcome(s) and predictors based on the trained recipe.

# 2017-07-27 recipes 0.1.0

First CRAN release.

• Changed prepare to prep per issue #59

# Unreleased recipes 0.0.1.9003

• Two of the main functions changed names. learn has become prepare and process has become bake

# Unreleased recipes 0.0.1.9002

## New steps

• step_lincomb removes variables involved in linear combinations to resolve them.
• A step for converting binary variables to factors (step_bin2factor)
• step_regex applies a regular expression to a character or factor vector to create dummy variables.

## Other changes

• step_dummy and step_interact do a better job of respecting missing values in the data set.

# Unreleased recipes 0.0.1.9001

• The class system for recipe objects was changed so that pipes can be used to create the recipe with a formula.
• process.recipe lost the role` argument in factor of a general set of selectors. If no selector is used, all the predictors are returned.
• Two steps for simple imputation using the mean or mode were added.