datawizard 0.6.3

MAJOR CHANGES

• There is a new publication about the {datawizard} package:
  https://joss.theoj.org/papers/10.21105/joss.04684

• Fixes failing tests due to changes in R-devel.

• data_to_long() and data_to_wide() have had significant performance
  improvements, sometimes as high as a ten-fold speedup.

MINOR CHANGES

• When column names are misspelled, most functions now suggest which existing
  columns possibly could be meant.

• Miscellaneous performance gains.

• convert_to_na() now requires argument na to be of class 'Date' to convert
  specific dates to NA. For example, convert_to_na(x, na = "2022-10-17")
  must be changed to convert_to_na(x, na = as.Date("2022-10-17")).

BUG FIXES

• data_to_long() and data_to_wide() now correctly keep the date format.

datawizard 0.6.2

BREAKING CHANGES

• Methods for grouped data frames (.grouped_df) no longer support
  dplyr::group_by() for {dplyr} before version 0.8.0.

• empty_columns() and remove_empty_columns() now also remove columns that
  contain only empty characters. Likewise, empty_rows() and
  remove_empty_rows() remove observations that completely have missing or
  empty character values.

CHANGES

• data_arrange() now works with data frames that were grouped using
  data_group() (#274).

• data_read() gains a convert_factors argument, to turn off automatic
  conversion from numeric variables into factors.

datawizard 0.6.1

• Updates tests for upcoming changes in the {tidyselect} package (#267).

datawizard 0.6.0

BREAKING CHANGES

• The minimum needed R version has been bumped to 3.6.

• Following deprecated functions have been removed:

  data_cut(), data_recode(), data_shift(), data_reverse(), data_rescale(),
  data_to_factor(), data_to_numeric()

• New text_format() alias is introduced for format_text(), latter of which
  will be removed in the next release.

• New recode_values() alias is introduced for change_code(), latter of which
  will be removed in the next release.

• data_merge() now errors if columns specified in by are not in both datasets.

• Using negative values in arguments select and exclude now removes the columns
  from the selection/exclusion. The previous behavior was to start the
  selection/exclusion from the end of the dataset, which was inconsistent with
  the use of "-" with other selecting possibilities.

NEW FUNCTIONS

• data_peek(): to peek at values and type of variables in a data frame.

• coef_var(): to compute the coefficient of variation.

CHANGES

• data_filter() will give more informative messages on malformed syntax of
  the filter argument.

• It is now possible to use curly brackets to pass variable names to data_filter(),
  like the following example. See examples section in the documentation of
  data_filter().

• The regex argument was added to functions that use select-helpers and did
  not already have this argument.

• Select helpers starts_with(), ends_with(), and contains() now accept
  several patterns, e.g starts_with("Sep", "Petal").

• Arguments select and exclude that are present in most functions have been
  improved to work in loops and in custom functions. For example, the following
  code now works:

foo <- function(data) {
  i <- "Sep"
  find_columns(data, select = starts_with(i))
}
foo(iris)

for (i in c("Sepal", "Sp")) {
  head(iris) |>
    find_columns(select = starts_with(i)) |>
    print()
}

• There is now a vignette summarizing the various ways to select or exclude
  variables in most {datawizard} functions.

datawizard 0.5.1

• Fixes tests for {poorman} update

datawizard 0.5.0

MAJOR CHANGES

• Following statistical transformation functions have been renamed to not have
  data_*() prefix, since they do not work exclusively with data frames, but
  are typically first of all used with vectors, and therefore had misleading
  names:

  • data_cut() -> categorize()
  • data_recode() -> change_code()
  • data_shift() -> slide()
  • data_reverse() -> reverse()
  • data_rescale() -> rescale()
  • data_to_factor() -> to_factor()
  • data_to_numeric() -> to_numeric()

  Note that these functions also have .data.frame() methods and still work
  for data frames as well. Former function names are still available as aliases,
  but will be deprecated and removed in a future release.

• 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.

• 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.
  reshape_wider() also gains an argument names_glue (#182, #198).

• Similarly, reshape_longer() now follows more consistently
  tidyr::pivot_longer() syntax. Argument colnames_to is deprecated and
  should be replaced by names_to. reshape_longer() also gains new arguments:
  names_prefix, names_sep, names_pattern, and values_drop_na (#189).

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.

• Functions like center(), normalize() or standardize() no longer fail
  when data contains infinite values (Inf).

NEW FUNCTIONS

• row_to_colnames() and colnames_to_row() to move a row to column names, and
  column names to row (@etiennebacher, #169).

BUG FIXES

• Fixed wrong column names in data_to_wide() (#173).

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()