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datawizard 0.4.x

BREAKING

  • Bumps the needed minimum R version to 3.5.

  • Removed deprecated function data_findcols(). Please use its replacement, data_find().

  • Removed alias extract() for data_extract() function since it collided with tidyr::extract().

  • Argument training_proportion in data_partition() is deprecated. Please use proportion now.

MAJOR CHANGES

  • Given his continued and significant contributions to the package, Etienne Bacher (@etiennebacher) is now included as an author.

  • unstandardise() now works for center(x)

  • unnormalize() now works for change_scale(x)

  • reshape_wider() now follows more consistently tidyr::pivot_wider() syntax. Arguments colnames_from, sep, and rows_from are deprecated and should be replaced by names_from, names_sep, and id_cols respectively (#182).

CHANGES

  • Some of the text formatting helpers (like text_concatenate()) gain an enclose argument, to wrap text elements with surrounding characters.

  • winsorize now accepts “raw” and “zscore” methods (in addition to “percentile”). Additionally, when robust is set to TRUE together with method = "zscore", winsorizes via the median and median absolute deviation (MAD); else via the mean and standard deviation. (@rempsyc, #177, #49, #47).

  • data_partition() now allows to create multiple partitions from the data, returning multiple training and a remaining test set.

NEW FUNCTIONS

BUG FIXES

datawizard 0.4.1

CRAN release: 2022-05-16

BREAKING

  • Added the standardize.default() method (moved from package effectsize), to be consistent in that the default-method now is in the same package as the generic. standardize.default() behaves exactly like in effectsize and particularly works for regression model objects. effectsize now re-exports standardize() from datawizard.

NEW FUNCTIONS

CHANGES

  • data_find() was added as alias to find_colums(), to have consistent name patterns for the datawizard functions. data_findcols() will be removed in a future update and usage is discouraged.

  • The select argument (and thus, also the exclude argument) now also accepts functions testing for logical conditions, e.g. is.numeric() (or is.numeric), or any user-defined function that selects the variables for which the function returns TRUE (like: foo <- function(x) mean(x) > 3).

  • Arguments select and exclude now allow the negation of select-helpers, like -ends_with(""), -is.numeric or -Sepal.Width:Petal.Length.

  • Many functions now get a .default method, to capture unsupported classes. This now yields a message and returns the original input, and hence, the .data.frame methods won’t stop due to an error.

  • The filter argument in data_filter() can also be a numeric vector, to indicate row indices of those rows that should be returned.

  • convert_to_na() gets methods for variables of class logical and Date.

  • convert_to_na() for factors (and data frames) gains a drop_levels argument, to drop unused levels that have been replaced by NA.

  • data_to_numeric() gains two more arguments, preserve_levels and lowest, to give better control of conversion of factors.

BUG FIXES

  • When logicals were passed to center() or standardize() and force = TRUE, these were not properly converted to numeric variables.

datawizard 0.4.0

CRAN release: 2022-03-30

MAJOR CHANGES

NEW FUNCTIONS

MINOR CHANGES

  • The select argument in several functions (like data_remove(), reshape_longer(), or data_extract()) now allows the use of select-helpers for selecting variables based on specific patterns.

  • data_extract() gains new arguments to allow type-safe return values, i.e. always return a vector or a data frame. Thus, data_extract() can now be used to select multiple variables or pull a single variable from data frames.

  • data_match() gains a match argument, to indicate with which logical operation matching results should be combined.

  • Improved support for labelled data for many functions, i.e. returned data frame will preserve value and variable label attributes, where possible and applicable.

  • describe_distribution() now works with lists (@etiennebacher, #105).

  • data_rename() doesn’t use pattern anymore to rename the columns if replacement is not provided (@etiennebacher, #103).

  • data_rename() now adds a suffix to duplicated names in replacement (@etiennebacher, #103).

BUG FIXES

  • data_to_numeric() produced wrong results for factors when dummy_factors = TRUE and factor contained missing values.

  • data_match() produced wrong results when data contained missing values.

  • Fixed CRAN check issues in data_extract() when more than one variable was extracted from a data frame.

datawizard 0.3.0

CRAN release: 2022-03-02

NEW FUNCTIONS

datawizard 0.2.3

CRAN release: 2022-01-26

datawizard 0.2.2

CRAN release: 2022-01-04

  • New function data_extract() (or its alias extract()) to pull single variables from a data frame, possibly naming each value by the row names of that data frame.

  • reshape_ci() gains a ci_type argument, to reshape data frames where CI-columns have prefixes other than "CI".

  • standardize() and center() gain arguments center and scale, to define references for centrality and deviation that are used when centering or standardizing variables.

  • center() gains the arguments force and reference, similar to standardize().

  • The functionality of the append argument in center() and standardize() was revised. This made the suffix argument redundant, and thus it was removed.

  • Fixed issue in standardize().

  • Fixed issue in data_findcols().

datawizard 0.2.1

CRAN release: 2021-10-04

datawizard 0.2.0.1

CRAN release: 2021-09-02

  • This is mainly a maintenance release that addresses some issues with conflicting namespaces.

datawizard 0.2.0

CRAN release: 2021-08-17

  • New function: visualisation_recipe().

  • The following function has now moved to performance package: check_multimodal().

  • Minor updates to documentation, including a new vignette about demean().

datawizard 0.1.0

CRAN release: 2021-06-18

  • First release.