Magic potions to clean and transform your data 🧙
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
data_to_wide() (#173).BREAKING CHANGES
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
center() or standardize() and force = TRUE,
these were not properly converted to numeric variables.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.
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.
New functions:
to work with lists: is_empty_object() and compact_list()
to work with strings: compact_character()
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().
Exports plot method for visualisation_recipe() objects from {see}
package.
centre(), standardise(), unstandardise() are exported as aliases for
center(), standardize(), unstandardize(), respectively.
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().