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
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().