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().

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).