This page is under construction :-)
Like families, tidy datasets are all alike but every messy dataset is messy in its own way. Tidy datasets provide a standardized way to link the structure of a dataset (its physical layout) with its semantics (its meaning).
Hadley Wickham (2014). "Tidy Data" Journal of Statistical Software. 59(10) http://dx.doi.org/10.18637/jss.v059.i10
Tidy Data is a way of structuring data so that it can be easily understood by people and analyzed by machines. Because all tidy data sets are structured the same way, tabular data using this standard are easy to explore, understand, use, and update.
The information on this page summarizes the tidy data standard but it is highly recommended that you also read the original paper (to section 3.4) as it contains excellent examples and more detailed descriptions.
Tabular data, or data table is a systematic representation of information arranged in a grid (often in a spreadsheet). Data tables are used to convey information to people and to format information for machines to process.
Variable (called a "dimension" in computer science)
"A variable contains all values that measure the same underlying attribute (like height, temperature, duration) across units." There are two types of variables:
Fixed variables: variables that describe experimental design and are known or set in advance.
Measured variables: what is measured or observed during the study, experiment, or process.
"An observation contains all values measured on the same unit (like a person, or a day, or a race) across attributes."
Tidy data is based on three core principles:
The principles can be easily visualized through a simple table. The table below contains 3 columns (variables), and 4 rows of data (observations) and 1 header row which names the variables. The color coding may you help visualize how the three principles work to create explicit relationships between data values.
Data that does not conform to the tidy data principles can still be valuable but may be harder to use. As identified by Dr. Wickham, the most common "messy data" problems are:
Examples for each are given in Wickham's paper, along with examples of how to tidy such data. Below we've included a small, messy data set that has then been tidied. The messy elements have been highlighted to help you identify them.