Easy Data Wrangling and Statistical Transformations
Adjust data for the effect of other variable(s)
Assign variable and value labels
Recode (or "cut" / "bin") data into groups of values.
Centering (Grand-Mean Centering)
Compute the coefficient of variation
Convert to Numeric (if possible)
Tools for working with column names
Deviation Contrast Matrix
Replace missing values in a variable or a data frame.
Convert non-missing values in a variable into missing values.
Arrange rows by column values
Generate a codebook of a data frame.
Extract all duplicates
Extract one or more columns or elements from an object
Create a grouped data frame
Return filtered or sliced data frame, or row indices
Merge (join) two data frames, or a list of data frames
Create new variables in a data frame
Partition data
Peek at values and type of variables in a data frame
Add a prefix or suffix to column names
Read (import) data files from various sources
Relocate (reorder) columns of a data frame
Rename columns and variable names
Expand (i.e. replicate rows) a data frame
Restore the type of columns according to a reference data frame
Rotate a data frame
Find variables by their names, variable or value labels
Separate single variable into multiple variables
Summarize data
Create frequency and crosstables of variables
Reshape (pivot) data from wide to long
Reshape (pivot) data from long to wide
Keep only one row from all with duplicated IDs
Unite ("merge") multiple variables
datawizard: Easy Data Wrangling and Statistical Transformations
Compute group-meaned and de-meaned variables
Describe a distribution
Compute mode for a statistical distribution
Print a message saying that an argument is deprecated and that the use...
Find or get columns in a data frame based on search patterns
Convert value labels into factor levels
Utility Function for Safe Prediction with datawizard transformers
Summary Helpers
Summary of mean values by group
Normalize numeric variable to 0-1 range
(Signed) rank transformation
Recode values from one or more variables into a new variable
Recode old values of variables into new values
Objects exported from other packages
Return or remove variables or observations that are completely missing
Convert infinite or NaN values into NA
Rescale design weights for multilevel analysis
Rescale Variables to a New Range
Reshape CI between wide/long formats
Reverse-Score Variables
Count specific values row-wise
Row means or sums (optionally with minimum amount of valid values)
Tools for working with row names or row ids
Compute Skewness and (Excess) Kurtosis
Shift numeric value range
Quantify the smoothness of a vector
Re-fit a model with standardized data
Standardization (Z-scoring)
Convenient text formatting functionalities
Convert data to factors
Convert data to numeric
Prepare objects for visualisation
Weighted Mean, Median, SD, and MAD
Winsorize data
A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.