Especially if users lack a programming-oriented skill set or even more basic, formulae knowledge. : 2.Dealing with the necessity of performing transformations of various types when working with tabular data can be challenging. : 4.3702 #> Catholic Infant.Mortality #> Min. :26.60 # after summary ( standardize ( swiss ) ) #> Fertility Agriculture Examination Education #> Min. :53.00 #> Catholic Infant.Mortality #> Min. # before summary ( swiss ) #> Fertility Agriculture Examination Education #> Min.

Statistical transformations: datawizard also has powerful functions to easily apply common data transformations, including standardization, normalization, rescaling, rank-transformation, scale reversing, recoding, binning, etc. Finally, datawizard is super lightweight (no dependencies, similar to poorman), which makes it awesome for developers to use in their packages. 2) Although most functions can be used exactly as their tidyverse equivalents, they are also string-friendly (which makes them easy to program with and use inside functions). 1) All data manipulation functions start with the prefix data_* (which makes them easy to identify). It covers two aspects of data preparation:ĭata manipulation: datawizard offers a very similar set of functions to that of the tidyverse packages, such as a dplyr and tidyr, to select, filter and reshape data, with a few key differences.

It is part of the easystats ecosystem, a suite of R packages to deal with your entire statistical analysis, from cleaning the data to reporting the results. Datawizard: Easy Data Wrangling and Statistical Transformationsĭatawizard is a lightweight package to easily manipulate, clean, transform, and prepare your data for analysis.

Easydatatransform