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BREAKING CHANGES
data_rename() now errors when the
replacement argument contains NA values or
empty strings (#539).
Removed deprecated functions get_columns(),
data_find(), format_text() (#546).
Removed deprecated arguments group and
na.rm in multiple functions. Use by and
remove_na instead (#546).
The default value for the argument dummy_factors in
to_numeric() has changed from TRUE to
FALSE (#544).
CHANGES
The pattern argument in data_rename()
can also be a named vector. In this case, names are used as values for
the replacement argument (i.e. pattern can be
a character vector using
<new name> = "<old name>").
categorize() gains a new breaks
argument, to decide whether breaks are inclusive or exclusive
(#548).
The labels argument in categorize()
gets two new options, "range" and "observed",
to use the range of categorized values as labels (i.e. factor levels)
(#548).
Minor additions to reshape_ci() to work with
forthcoming changes in the {bayestestR} package.
CHANGES
demean() (and degroup()) now also work
for nested designs, if argument nested = TRUE and
by specifies more than one variable (#533).
Vignettes are no longer provided in the package, they are now only available on the website. There is only one “Overview” vignette available in the package, it contains links to the other vignettes on the website. This is because there are CRAN errors occurring when building vignettes on macOS and we couldn’t determine the cause after multiple patch releases (#534).
htmltools from Suggests in an
attempt of fixing an error in CRAN checks due to failures to build a
vignette (#528).This is a patch release to fix one error on CRAN checks occurring because of a missing package namespace in one of the vignettes.
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).
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).BREAKING CHANGES
The following arguments were deprecated in 0.5.0 and are now removed:
data_to_wide(): colnames_from,
rows_from, sepdata_to_long(): colnames_todata_partition():
training_proportionNEW 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).
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.
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 issues in data_write() when writing labelled
data into SPSS format for character vectors with missing value labels,
but existing variable 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.
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).
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
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:
-1:2
is not accepted but -(1:2) is);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).select and/or
exclude, there is now one warning per misspelled variable.
The previous behavior was to have only one warning.standardize() when only
one of the arguments center or scale were
provided (#365).unstandardize() and replace_nan_inf() now
work with select helpers (#376).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 (#356).convert_na_to() when
select is a list (#352).MAJOR CHANGES
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.
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().
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.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.
MINOR CHANGES
data_read() gains a convert_factors
argument, to turn off automatic conversion from numeric variables into
factors.BUG FIXES
data_arrange() now works with data frames that were
grouped using data_group() (#274).{tidyselect}
package (#267).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()
}{datawizard} functions.{poorman} update.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).
convert_na_to now accepts numeric replacements on
character vectors and single replacement for multiple vector classes.
(@rempsyc,
#214).
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).
data_arrange() to sort the rows of a dataframe
according to the values of the selected columns.
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().
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.