Tidy

Tidying tabular data so you don't have to.

Data Shaping and Tidying

Leaning heavily on the principles of the tidyverse, this package makes it easy to reshape and tidy tabular data for data analysis and visualization.

Use cases include:

• Importing tabular data from a CSV file for analysis.
• Joining 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 in a column.
• each observation is a row.
• each value is a cell.

Unfortunately, many datasets 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. For a more complete description of tidy data, see Wickham (2014).