Tidying tabular data so you don't have to.
Leaning heavily on the principles of the tidyverse, and especially tidy data, this package makes it easy to reshape and tidy tabular data for easier data analysis and visualization.
Use cases include:
- Importing tabular data from a CSV file for analysis.
- Joining and normalizing relational tables for data query.
- Editing, filtering and mapping rows and columns of data in a table.
- Creating tidy data for visualization with elm-vegalite, elm-vega, elm-visualization and other visualization packages.
- Building data science applications in Elm.
Tidy data are tables of data where
- each variable is a column.
- each observation is a row.
- each value is a cell.
The reality faced by many trying to work with datasets is that they are not in this format. The tidy package allows you to transform data into tidy tables so you spend less time fighting with data tools and more time working on your analysis and visualization.