adjust                  Adjust data for the effect of other variable(s)
assign_labels           Assign variable and value labels
categorize              Recode (or "cut" / "bin") data into groups of
                        values.
center                  Centering (Grand-Mean Centering)
coef_var                Compute the coefficient of variation
coerce_to_numeric       Convert to Numeric (if possible)
contr.deviation         Deviation Contrast Matrix
convert_na_to           Replace missing values in a variable or a data
                        frame.
convert_to_na           Convert non-missing values in a variable into
                        missing values.
data_addprefix          Rename columns and variable names
data_arrange            Arrange rows by column values
data_codebook           Generate a codebook of a data frame.
data_duplicated         Extract all duplicates
data_extract            Extract one or more columns or elements from an
                        object
data_group              Create a grouped data frame
data_match              Return filtered or sliced data frame, or row
                        indices
data_merge              Merge (join) two data frames, or a list of data
                        frames
data_modify             Create new variables in a data frame
data_partition          Partition data
data_peek               Peek at values and type of variables in a data
                        frame
data_read               Read (import) data files from various sources
data_relocate           Relocate (reorder) columns of a data frame
data_restoretype        Restore the type of columns according to a
                        reference data frame
data_rotate             Rotate a data frame
data_seek               Find variables by their names, variable or
                        value labels
data_separate           Separate single variable into multiple
                        variables
data_tabulate           Create frequency tables of variables
data_to_long            Reshape (pivot) data from wide to long
data_to_wide            Reshape (pivot) data from long to wide
data_unique             Keep only one row from all with duplicated IDs
data_unite              Unite ("merge") multiple variables
demean                  Compute group-meaned and de-meaned variables
describe_distribution   Describe a distribution
distribution_mode       Compute mode for a statistical distribution
efc                     Sample dataset from the EFC Survey
find_columns            Find or get columns in a data frame based on
                        search patterns
labels_to_levels        Convert value labels into factor levels
makepredictcall.dw_transformer
                        Utility Function for Safe Prediction with
                        'datawizard' transformers
mean_sd                 Summary Helpers
means_by_group          Summary of mean values by group
nhanes_sample           Sample dataset from the National Health and
                        Nutrition Examination Survey
normalize               Normalize numeric variable to 0-1 range
ranktransform           (Signed) rank transformation
recode_into             Recode values from one or more variables into a
                        new variable
recode_values           Recode old values of variables into new values
remove_empty            Return or remove variables or observations that
                        are completely missing
replace_nan_inf         Convert infinite or 'NaN' values into 'NA'
rescale                 Rescale Variables to a New Range
rescale_weights         Rescale design weights for multilevel analysis
reshape_ci              Reshape CI between wide/long formats
reverse                 Reverse-Score Variables
row_means               Row means (optionally with minimum amount of
                        valid values)
row_to_colnames         Tools for working with column names
rownames_as_column      Tools for working with row names or row ids
skewness                Compute Skewness and (Excess) Kurtosis
slide                   Shift numeric value range
smoothness              Quantify the smoothness of a vector
standardize             Standardization (Z-scoring)
standardize.default     Re-fit a model with standardized data
text_format             Convenient text formatting functionalities
to_factor               Convert data to factors
to_numeric              Convert data to numeric
visualisation_recipe    Prepare objects for visualisation
weighted_mean           Weighted Mean, Median, SD, and MAD
winsorize               Winsorize data