datawizard 0.12.0

BREAKING CHANGES

• The argument include_na in data_tabulate() and data_summary() has been
  renamed into remove_na. Consequently, to mimic former behaviour, FALSE and
  TRUE need to be switched (i.e. remove_na = TRUE is equivalent to the former
  include_na = FALSE).

• Class names for objects returned by data_tabulate() have been changed to
  datawizard_table and datawizard_crosstable (resp. the plural forms,
  *_tables), to provide a clearer and more consistent naming scheme.

CHANGES

• data_select() can directly rename selected variables when a named vector
  is provided in select, e.g. data_select(mtcars, c(new1 = "mpg", new2 = "cyl")).

• data_tabulate() gains an as.data.frame() method, to return the frequency
  table as a data frame. The structure of the returned object is a nested data
  frame, where the first column contains name of the variable for which
  frequencies were calculated, and the second column contains the frequency table.

• demean() (and degroup()) now also work for cross-classified designs, or
  more generally, for data with multiple grouping or cluster variables (i.e.
  by can now specify more than one variable).

datawizard 0.11.0

BREAKING CHANGES

• Arguments named group or group_by are deprecated and will be removed
  in a future release. Please use by instead. This affects the following
  functions in datawizard (#502).

  • data_partition()
  • demean() and degroup()
  • means_by_group()
  • rescale_weights()

• Following aliases are deprecated and will be removed in a future release (#504):

  • get_columns(), use data_select() instead.
  • data_find() and find_columns(), use extract_column_names() instead.
  • format_text(), use text_format() instead.

CHANGES

• recode_into() is more relaxed regarding checking the type of NA values.
  If you recode into a numeric variable, and one of the recode values is NA,
  you no longer need to use NA_real_ for numeric NA values.

• Improved documentation for some functions.

BUG FIXES

• data_to_long() did not work for data frame where columns had attributes
  (like labelled data).

datawizard 0.10.0

BREAKING CHANGES

• The following arguments were deprecated in 0.5.0 and are now removed:

  • in data_to_wide(): colnames_from, rows_from, sep
  • in data_to_long(): colnames_to
  • in data_partition(): training_proportion

NEW FUNCTIONS

• data_summary(), to compute summary statistics of (grouped) data frames.

• data_replicate(), to expand a data frame by replicating rows based on another
  variable that contains the counts of replications per row.

CHANGES

• data_modify() gets three new arguments, .at, .if and .modify, to modify
  variables at specific positions or based on logical conditions.

• data_tabulate() was revised and gets several new arguments: a weights
  argument, to compute weighted frequency tables. include_na allows to include
  or omit missing values from the table. Furthermore, a by argument was added,
  to compute crosstables (#479, #481).

0.9.1

datawizard 0.9.1

CHANGES

• rescale() gains multiply and add arguments, to expand ranges by a given
  factor or value.

• to_factor() and to_numeric() now support class haven_labelled.

BUG FIXES

• to_numeric() now correctly deals with inversed factor levels when
  preserve_levels = TRUE.

• to_numeric() inversed order of value labels when dummy_factors = FALSE.

• convert_to_na() now preserves attributes for factors when drop_levels = TRUE.

datawizard 0.9.0

NEW FUNCTIONS

• row_means(), to compute row means, optionally only for the rows with at
  least min_valid non-missing values.

• contr.deviation() for sum-deviation contrast coding of factors.

• means_by_group(), to compute mean values of variables, grouped by levels
  of specified factors.

• data_seek(), to seek for variables in a data frame, based on their
  column names, variables labels, value labels or factor levels. Searching for
  labels only works for "labelled" data, i.e. when variables have a label or
  labels attribute.

CHANGES

• recode_into() gains an overwrite argument to skip overwriting already
  recoded cases when multiple recode patterns apply to the same case.

• recode_into() gains an preserve_na argument to preserve NA values
  when recoding.

• data_read() now passes the encoding argument to data.table::fread().
  This allows to read files with non-ASCII characters.

• datawizard moves from the GPL-3 license to the MIT license.

• unnormalize() and unstandardize() now work with grouped data (#415).

• unnormalize() now errors instead of emitting a warning if it doesn't have the
  necessary info (#415).

BUG FIXES

• Fixed issue in labels_to_levels() when values of labels were not in sorted
  order and values were not sequentially numbered.

• Fixed issues in data_write() when writing labelled data into SPSS format
  and vectors were of different type as value labels.

• Fixed issue in recode_into() with probably wrong case number printed in the
  warning when several recode patterns match to one case.

• Fixed issue in recode_into() when original data contained NA values and
  NA was not included in the recode pattern.

• Fixed issue in data_filter() where functions containing a = (e.g. when
  naming arguments, like grepl(pattern, x = a)) were mistakenly seen as
  faulty syntax.

• Fixed issue in empty_column() for strings with invalid multibyte strings.
  For such data frames or files, empty_column() or data_read() no longer
  fails.

datawizard 0.8.0

BREAKING CHANGES

• The following re-exported functions from {insight} have now been removed:
  object_has_names(), object_has_rownames(), is_empty_object(),
  compact_list(), compact_character().

• Argument na.rm was renamed to remove_na throughout {datawizard} functions.
  na.rm is kept for backward compatibility, but will be deprecated and later
  removed in future updates.

• The way expressions are defined in data_filter() was revised. The filter
  argument was replaced by ..., allowing to separate multiple expression with
  a comma (which are then combined with &). Furthermore, expressions can now also be
  defined as strings, or be provided as character vectors, to allow string-friendly
  programming.

CHANGES

• Weighted-functions (weighted_sd(), weighted_mean(), ...) gain a remove_na
  argument, to remove or keep missing and infinite values. By default,
  remove_na = TRUE, i.e. missing and infinite values are removed by default.

• reverse_scale(), normalize() and rescale() gain an append argument
  (similar to other data frame methods of transformation functions), to append
  recoded variables to the input data frame instead of overwriting existing
  variables.

NEW FUNCTIONS

• rowid_as_column() to complement rownames_as_column() (and to mimic
  tibble::rowid_to_column()). Note that its behavior is different from
  tibble::rowid_to_column() for grouped data. See the Details section in the
  docs.

• data_unite(), to merge values of multiple variables into one new variable.

• data_separate(), as counterpart to data_unite(), to separate a single
  variable into multiple new variables.

• data_modify(), to create new variables, or modify or remove existing
  variables in a data frame.

MINOR CHANGES

• to_numeric() for variables of type Date, POSIXct and POSIXlt now
  includes the class name in the warning message.

• Added a print() method for center(), standardize(), normalize() and
  rescale().

BUG FIXES

• standardize_parameters() now works when the package namespace is in the model
  formula (#401).

• data_merge() no longer yields a warning for tibbles when join = "bind".

• center() and standardize() did not work for grouped data frames (of class
  grouped_df) when force = TRUE.

• The data.frame method of describe_distribution() returns NULL instead of
  an error if no valid variable were passed (for example a factor variable with
  include_factors = FALSE) (#421).

datawizard 0.7.1

BREAKING CHANGES

• add_labs() was renamed into assign_labels(). Since add_labs() existed
  only for a few days, there will be no alias for backwards compatibility.

NEW FUNCTIONS

• labels_to_levels(), to use value labels of factors as their levels.

MINOR CHANGES

• data_read() now checks if the imported object actually is a data frame (or
  coercible to a data frame), and if not, no longer errors, but gives an
  informative warning of the type of object that was imported.

BUG FIXES

• Fix test for CRAN check on Mac OS arm64

datawizard 0.7.0

BREAKING CHANGES

• In selection patterns, expressions like -var1:var3 to exclude all variables
  between var1 and var3 are no longer accepted. The correct expression is
  -(var1:var3). This is for 2 reasons:

  • to be consistent with the behavior for numerics (-1:2 is not accepted but
    -(1:2) is);
  • to be consistent with dplyr::select(), which throws a warning and only
    uses the first variable in the first expression.

NEW FUNCTIONS

• recode_into(), similar to dplyr::case_when(), to recode values from one
  or more variables into a new variable.

• mean_sd() and median_mad() for summarizing vectors to their mean (or
  median) and a range of one SD (or MAD) above and below.

• data_write() as counterpart to data_read(), to write data frames into
  CSV, SPSS, SAS, Stata files and many other file types. One advantage over
  existing functions to write data in other packages is that labelled (numeric)
  data can be converted into factors (with values labels used as factor levels)
  even for text formats like CSV and similar. This allows exporting "labelled"
  data into those file formats, too.

• add_labs(), to manually add value and variable labels as attributes to
  variables. These attributes are stored as "label" and "labels" attributes,
  similar to the labelled class from the haven package.

MINOR CHANGES

• data_rename() gets a verbose argument.
• winsorize() now errors if the threshold is incorrect (previously, it provided
  a warning and returned the unchanged data). The argument verbose is now
  useless but is kept for backward compatibility. The documentation now contains
  details about the valid values for threshold (#357).
• In all functions that have arguments select and/or exclude, there is now
  one warning per misspelled variable. The previous behavior was to have only one
  warning.
• Fixed inconsistent behaviour in standardize() when only one of the arguments
  center or scale were provided (#365).
• unstandardize() and replace_nan_inf() now work with select helpers (#376).
• Added informative warning and error messages to reverse(). Furthermore, the
  docs now describe the range argument more clearly (#380).
• unnormalize() errors with unexpected inputs (#383).

BUG FIXES

• empty_columns() (and therefore remove_empty_columns()) now correctly detects
  columns containing only NA_character_ (#349).
• Select helpers now work in custom functions when argument is called select
  (#356).
• Fix unexpected warning in convert_na_to() when select is a list (#352).
• Fixed issue with correct labelling of numeric variables with more than nine
  unique values and associated value labels.

datawizard 0.6.5

MAJOR CHANGES

• Etienne Bacher is the new maintainer.

MINOR CHANGES

• standardize(), center(), normalize() and rescale() can be used in
  model formulas, similar to base::scale().

• data_codebook() now includes the proportion for each category/value, in
  addition to the counts. Furthermore, if data contains tagged NA values,
  these are included in the frequency table.

BUG FIXES

• center(x) now works correctly when x is a single value and either
  reference or center is specified (#324).

• Fixed issue in data_codebook(), which failed for labelled vectors when
  values of labels were not in sorted order.

datawizard 0.6.4

NEW FUNCTIONS

• data_codebook(): to generate codebooks of data frames.

• New functions to deal with duplicates: data_duplicated() (keep all duplicates,
  including the first occurrence) and data_unique() (returns the data, excluding
  all duplicates except one instance of each, based on the selected method).

MINOR CHANGES

• .data.frame methods should now preserve custom attributes.

• The include_bounds argument in normalize() can now also be a numeric
  value, defining the limit to the upper and lower bound (i.e. the distance
  to 1 and 0).

• data_filter() now works with grouped data.

BUG FIXES

• data_read() no longer prints message for empty columns when the data
  actually had no empty columns.

• data_to_wide() now drops columns that are not in id_cols (if specified),
  names_from, or values_from. This is the behaviour observed in tidyr::pivot_wider().