NEWS.md
rec() now keeps the labels of the old values with the new ones when there are no labels specified and there is a 1 to 1 correspondence between old and new values.
format() for frq() was revised, and now allows to format the frequency table to prepare for printing in text, markdown and HTML-format. To do so, use the methods print(), print_md() or print_html().
merge_df() preserves more attributes related to labelled data.
to.factor is an alias for the argument as.num.
move_columns() (using a variable as value for argument .after didn’t work).flat_table() gains a weights-argument.descr() calculated wrong percentage of missing values for weighted data.rec() when min, max, lo or hi was used to recode a numeric into a character vector, and the new recode string contained one of these four strings as pattern.rec() when max or hi was used to recode a value which maximum values was lower than a defined range, e.g. 4:max when the maximum values was lower than 4.as.data.frame() for frq().typical_value() now returns the median for integer-values (instead of mean), to preserve the integer-type of a variable.rec() now also works for character variables with whitespaces.rec() now warns explicitely for possible non-intended multiple assignment of identical new recode-values.frq().merge_imputations() now returns the plot-object as well.to_numeric() as alias for to_value().find_variables() (alias for find_var()) was renamed to find_in_data(), to avoid conflicts with package insight.rename_variables() and rename_columns() are aliases for var_rename().frq() now also prints frequencies of logical conditions, e.g. how many values are lower or greater than a certain threshold.frq() gets a min.frq-argument, indicating the minimum frequency for which a value will be shown in the output.descr() gets a show argument to show selected columns only.descr() gets a file-argument to write the output as HTML file.var_rename() now also accepts a named vector with multiple elements as ellipses-argument.de_mean().merge_df() now removes columns with identical column names inside a data frame before merging, to avoid errors.frq(), where first element was empty, and vectors were not provided as data frame argument.word_wrap() when processing expressions.rec() with token rec = "rev", when reversing labelled vectors with more value labels than values.find_variables() as alias for find_var().reshape_longer(), as alternative to to_long(), probably easier to remember (function and argument-names).frq() displayed labels as NA in some situations for grouped data frames with more than one group, when data were not labelled.str_pos() was renamed into str_find().typical_value(), which was formerly located in package sjstats.is_whole() now automatically removes missing values from vectors.is_empty() now also checks lists with only NULL-elements.merge_imputations(), which previously could result in NA-values when merging imputed values into one variable.is_empty() in case the vector had non-missing values, but first element of vector was NA.frq() for grouped data frame, when grouping variable was a character vector. In this case, group titles were mixed up.tidy_values() to “clean” values (i.e. remove special chars) of character vectors or levels of factors.add_id() to quickly add an ID variable to (grouped) data frames.frq() gets a show.na-argument, to (automatically) show or hide the information for NA-values from the output.weights-argument in frq() now also accepts vectors, and is not limited to variable names. Note that these vectors must be part of a data frame.rec(), dicho(), …), if suffix = "" and append = TRUE, existing variables will be replaced by the new, recoded variables.group_str().var_rename() now supports quasi-quotation (see Examples).row_sums() and row_means() now return the input data frame when this data frame only had one column and no row means or sums were calculated. The returned data frame still gets the new variable name defined in var.complete_cases() returned an empty vector instead of all indexes if all cases (rows) of a data frame were complete.to_dummy() for character-vector input.group_str().frq() when grp.strings = TRUE.frq() gets a file and encoding argument, to save the HTML output as file.add_variables() and move_columns() now preserve the attributes of a data frame.de_mean() to compute group-meaned and de-meaned variables.add_variables() and add_case() to add columns or rows in a convenient way to a data frame.move_columns() to move one or more columns to another position in a data frame.is_num_chr() to check whether a character vector has only numeric strings.seq_col() and seq_row() as convenient wrapper to create a regular sequence for column or row numbers.descr() gets a weights-argument, to print weighted descriptive statistics.n-argument in row_means() and row_sums() now also may be Inf, to compute means or sums only if all values in a row are valid (i.e. non-missing).weight.by in frq() was renamed into weights.frq() gets a title-argument, to specify an alternative title to the variable label.round_num() preserves data frame attributes.frq() printed frequencies of grouping-variable for grouped data frames, when weights was not NULL.frq() for grouped data frames, when grouping variable was an unlabelled factor.has_na() to check if variables or observations in a data frame contain NA, NaN or Inf values. Convenient shortcuts for this function are complete_cases(), incomplete_cases(), complete_vars() and incomplete_vars().total_mean() to compute the overall mean of all values from all columns in a data frame.prcn() to convert numeric scalars between 0 and 1 into a character-percentage value.numeric_to_factor() to convert numeric variables into factors, using associated value labels as factor levels.set_na() now also replaces different values per variable into NA.row_sums() and missing values. row_sums() gets a n-argument and now computes row sums if a row has at least n non-missing values.CITATION to the publication in the Journal of Open Source Software.is_cross_classified() to check whether two factors are partially crossed.rec() with direct labelling for certain locales.count_na(), which did not print labels of tagged NA values since the last revision of frq().merge_imputation() for cases where original data frame had less columns than imputed data frames.find_var() for fuzzy-matching in all elements (i.e. when fuzzy = TRUE and search = "all").round_num() to round only numeric values in a data frame.merge_df(). Furthermore, add_rows() was added as alias for merge_df().merge_df() resp. add_rows() now create a unique id-name instead of dropping the ID-variable, in case id has the same name of any existing variables in the provided data frames.descr() and minor changes to the output.mids-objects (package mice)Following functions now also work on mids-objects, as returned by the mice()-function: * row_count(), row_sums(), row_means(), rec(), dicho(), center(), std(), recode_to() and to_long().
weight.by-argument in frq() now should be a variable name from a variable in x, and no longer a separate vector.descr() does not work with character vectors, so these are being removed now.The recoding and transformation functions get scoped variants, allowing to select variables based on logical conditions described in a function:
rec_if() as scoped variant of rec().dicho_if() as scoped variant of dicho().center_if() as scoped variant of center().std_if() as scoped variant of std().split_var_if() as scoped variant of split_var().group_var_if() and group_label_if() as scoped variant of group_var() and group_label().recode_to_if() as scoped variant of recode_to().set_na_if() as scoped variant of set_na().remove_cols() as alias for remove_var().std() gets a new robust-option, robust = "2sd", which divides the centered variables by two standard deviations.set_na().frq() now removes empty columns before computing frequencies, because applying frq() on empty vectors caused an error.empty_cols() and empty_rows() (and hence, remove_empty_cols() and remove_empty_rows()) caused an error for data frames with only one column resp. row, or if x was a vector and no data frame.frq() now removes missing values from input when weights are applied, to ensure that input and weights have same length.append-argument in recode and transformation functions like rec(), dicho(), split_var(), group_var(), center(), std(), recode_to(), row_sums(), row_count(), col_count() and row_means() now defaults to TRUE.print()-method for descr() now accepts a digits-argument, to specify the rounding of the output.dplyr::select_helpers were updated to tidyselect::select_helpers.is_whole() as counterpart to is_float().frq() now prints variable names for non-labelled data, adds variable names in braces for labelled data and omits the label column for non-labelled data.frq() now prints mean and standard deviation in the header line of the output.frq() now gets a auto.grp-argument to automatically group variables with many unique values.frq() now gets a show.strings-argument to omit string variables (character vectors) from being printed as frequency table.frq() now gets a grp.strings-argument to group similar string values in the frequency table.frq() gets an out-argument, to print output to console, or as HTML table in the viewer or web browser.descr() gets an out-argument, to print output to console, or as HTML table in the viewer or web browser.is_empty() returned TRUE for single vectors with NA being the first element.remove_empty_rows() did no longer remove empty rows, but columns.frq(), descr() and flat_table().rec() now also recodes doubles with floating points, if a range of values is specified.std() and center() now use include.fac = FALSE as default option.std() gets a robust-argument, to divide variables either by standard deviation, or - in case of asymmetrically distributed variables - median absolute deviation or Gini’s mean difference.frq() now shows total and valid N in output.center(), std(), dicho(), split_var() and group_var() did not work correctly for grouped data frames.frq() did not print multiple variables when applied on grouped data frames.as.df and as.varlab in function find_var() are now deprecated. Please use out instead.rotate_df() preserves attributes.is_float() is now exported as function.to_label(), when x was a character vector and argument drop.levels was TRUE.frq() did not correctly calculate valid and cumulative percentages when using weights.remove_var() as pipe-friendly function to remove variables from data frames.var_type() as pipe-friendly function to determine the type of variables.all_na() to check whether a vector only consists of NA values.rotate_df() to rotate data frames (switch columns and rows).shorten_string(), to shorten strings to a certain maxium number of chars.dicho(), split_var(), group_var(), std() and center().groupcount in split_var(), group_var() and group_labels() is now named n.groupsize in group_var() and group_labels() is now named size.frq() gets a revised print-method, which does not print the result to console when captured in an object (i.e., x <- frq(x) no longer prints the result).frq() no longer prints (redundant) labels for factors w/o value label attributes.frq() adds information about the variable type in the table caption (only for variables with variable labels).frq() adds information about groups when printing grouped, non-labelled variables.descr() now also prints information about the variable type.to_character() now preserves variable labels.one_of() or contains()) no longer need to be prefixed with a ~ when used as argument within sjmisc-functions.row_count() to count specific values in a data frame per observation.col_count() to count specific values in a data frame per variable.str_start() and str_end() to find starting and end indices of patterns inside strings.frq() now always includes a NA-row, but no longer prints a value for the NA-row.merge_imputations() gets a summary-argument to plot a graphical summary of the quality of the merging process.add_columns() and replace_columns() crashed R when no data frame was specified in ...-ellipses argument.descr() and frq() used wrong variable labels when processing grouped data frames for specific situations, where the grouping variable had no sequences values.descr() did not work for large data frames, because internally, because psych::describe() switched to fast mode by default then (removing columns from the output).