datawizard 0.4.1

BREAKING CHANGES

• 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

• data_shift() to shift the value range of numeric variables.

• data_recode() to recode old into new values.

• data_to_factor() as counterpart to data_to_numeric().

• data_tabulate() to create frequency tables of variables.

• data_read() to read (import) data files (from text, or foreign statistical
  packages).

• unnormalize() as counterpart to normalize(). This function only works for
  variables that have been normalized with normalize().

• data_group() and data_ungroup() to create grouped data frames, or to remove
  the grouping information from grouped data frames.

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  to drop unused levels that have been replaced by NA.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

MAJOR CHANGES

• data_match() now returns filtered data by default. Old behavior (returning
  rows indices) can be set by setting return_indices = TRUE.

• The following functions are now re-exported from {insight} package:
  object_has_names(), object_has_rownames(), is_empty_object(),
  compact_list(), compact_character()

• data_findcols() will become deprecated in future updates. Please use the
  new replacements find_columns() and get_columns().

• The vignette Analysing Longitudinal or Panel Data has now moved to
  parameters package.

NEW FUNCTIONS

• To convert rownames to a column, and vice versa: rownames_as_column()
  and column_as_rownames() (@etiennebacher, #80).

• find_columns() and get_columns() to find column names or retrieve
  subsets of data frames, based on various select-methods (including
  select-helpers). These function will supersede data_findcols() in the
  future.

• data_filter() as complement for data_match(), which works with logical
  expressions for filtering rows of data frames.

• For computing weighted centrality measures and dispersion: weighted_mean(),
  weighted_median(), weighted_sd() and weighted_mad().

• To replace NA in vectors and dataframes: convert_na_to() (@etiennebacher, #111).

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

• New functions:

  • To find or remove empty rows and columns in a data frame: empty_rows(),
    empty_columns(), remove_empty_rows(), remove_empty_columns(), and
    remove_empty.

  • To check for names: object_has_names() and object_has_rownames().

  • To rotate data frames: data_rotate().

  • To reverse score variables: data_reverse().

  • To merge/join multiple data frames: data_merge() (or its alias
    data_join()).

  • To cut (recode) data into groups: data_cut().

  • To replace specific values with NAs: convert_to_na().

  • To replace Inf and NaN values with NAs: replace_nan_inf().

• Arguments cols, before and after in data_relocate() can now also be
  numeric values, indicating the position of the destination column.

datawizard 0.2.3

• New functions:

  • to work with lists: is_empty_object() and compact_list()

  • to work with strings: compact_character()

Patch release: maintenance and bug fixes

• 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

• Exports plot method for visualisation_recipe() objects from {see}
  package.

• centre(), standardise(), unstandardise() are exported as aliases for
  center(), standardize(), unstandardize(), respectively.

datawizard 0.2.0.1

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

datawizard 0.2.0

• 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

• First release.