Adjust data for the effect of other variable(s)
Convert a crosstable to a frequency or a propensity table
Recode (or "cut" / "bin") data into groups of values.
Replace missing values in a variable or a data frame.
Convert non-missing values in a variable into missing values.
Extract one or more columns or elements from an object
Return filtered or sliced data frame, or row indices
Merge (join) two data frames, or a list of data frames
Peek at values and type of variables in a data frame
Read (import) data files from various sources
Relocate (reorder) columns of a data frame
Expand (i.e. replicate rows) a data frame
Restore the type of columns according to a reference data frame
Find variables by their names, variable or value labels
Separate single variable into multiple variables
Create frequency and crosstables of variables
Keep only one row from all with duplicated IDs
datawizard: Easy Data Wrangling and Statistical Transformations
Compute group-meaned and de-meaned variables
Compute mode for a statistical distribution
Find or get columns in a data frame based on search patterns
Utility Function for Safe Prediction with 'datawizard' transformers
Sample dataset from the National Health and Nutrition Examination Survey
Recode values from one or more variables into a new variable
Recode old values of variables into new values
Return or remove variables or observations that are completely missing
Convert infinite or 'NaN' values into 'NA'
Rescale design weights for multilevel analysis
Row means or sums (optionally with minimum amount of valid values)
Tools for working with row names or row ids