The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 containsthreshold (#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.
Health stats visible at Monitor.