OpenRefine for Data Cleaning

Data Carpentry Open Refine Demo

Objectives

Motivate participants to clean, organize, enhance data before insert into a database or merging data with other data files.
Introduce participants to Open Refine as a powerful data-cleaning tool.
Encourage dataset exploration; look at the data with the visualization tools in Open Refine.
Introduce concept of facets
Show split columns by defined separator
Show power of include / exclude, sort by name / count
Show the power of clustering algorithms to reveal data patterns, data snafus
If time, show call to an API, a web service (JSON example here from a locality georeferencing service)
If time, show how to parse the JSON returned from the service.
Refine provides a gentle introduction to SELECT DISTINCT, COUNT, ORDER BY, GROUP BY, and other SQL concepts in a visual way. Covering Refine first provides the SQL instructor with examples to refer back to - giving the students a reference point for understanding SQL. And starting with the “better spreadsheet skills” lesson, before this Open Refine demo, gives the instructor even more analogies to use when leading up to SQL. We experimented with lesson order and this seems to work best for insuring those “aha” moments.
Show the power of undo / redo.
Can be used to introduce
scripting
regular expressions
APIs

Motivations for the Open Refine Lesson

It’s really important that you know what you did. More journals/grants/etc. are also making it important for them to know what you did. You can capture all steps done to your data in Open Refine to the raw file and share with your publication as supplemental material.
All steps are easily reversed in Refine.
You must save your work to a new file; Refine does not modify your original dataset.
Data is often very messy - and this tool saves a lot of cleaning headaches.
Data cleaning steps often need repeating with multiple files. Refine is perfect for speeding up repetitive tasks.
Some concepts like clustering algorithms are quite complex, but Refine makes it easy to introduce them, use them, and show their power.

Before we get started

Check that you have Firefox browser installed. Open Refine runs in this browser. It will not run in IE.
Download software from http://openrefine.org if you have not done this yet.
Unzip the downloaded file into a directory. Name that directory something like Open-Refine
Note that “double-clicking” a zipped file on a windows machine sometimes results in a correctly unzipped set of files, other times, this is not successful. Try installing 7-Zip and use 7-Zip to extract all files from the zipped file to the desired directory.
Go to your newly created Open-Refine directory.
Click the google-refine.exe file to launch Open Refine.
Note, this is a Java program that runs on your machine (not in the cloud). It runs inside the Firefox browser, but no web connection is needed.

Basics of Open Refine

You can find out a lot more about Open Refine at http://openrefine.org and check out some great introductory videos. There is also an Open Refine Google Plus community https://plus.google.com/communities/117280693504889048168 where you can find a lot of help and a lot of folks from the life sciences are members. As with other programs of this type, Open Refine libraries are available too, where you can find a script you need and copy it into your Refine instance to run it on your dataset.

Open source.
A large growing community, from novice to expert, ready to help.
Works with large-ish datasets (100,000 rows). Does not scale to many millions. (yet).

Demo of Open Refine

Start the program. (Double-click on the google-refine.exe file. Java services will start on your machine, and Refine will open in your Firefox browser).

Note the file types Open Refine handles: TSV, CSF, *SV, Excel (.xls .xlsx), JSON, XML, RDF as XML, Google Data documents. Support for other formats can be added with Google Refine extensions.

Data for Open Refine

For this lesson, we’ll use the data you just exported from Diane Rockstar’s spreadsheet, or you can download 00-spreadsheet-lesson.csv

Once Refine is open, you’ll be asked if you want to Create, Open, or Import a Project.

Click Browse, find 00-lesson-spreadsheet.csv
Click next to open 00-lesson-spreadsheet.csv
Refine gives you a preview - a chance to show you it understood the file. If, for example, your file was really tab-delimited, the preview might look strange, you would choose the correct separator in the box shown and click “update preview.”
If all looks well, click Create Project.

Faceting

Scroll over to the Diet name column
Click the down arrow and choose > Facet > Text facet
In the left margin, you’ll see a box containing every unique, distinct value in the
Diet Name* column and Refine shows you how many times that value occurs in the column (a count), and allows you to sort (order) your facets by name or count.
Edit. Note that at any time, in any cell of the Facet box, or data cell in the Refine window, you have access to “edit” and can fix an error immediately. Refine will even ask you if you’d like to make that same correction to every value it finds like that one (or not).

Cluster

One of the most magical bits of Refine, the moment you realize what you’ve been missing. Refine has several clustering algorithms built in. Experiment with them, and follow the link inside Refine, to learn more about these algorithms and how they work. https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth
In this example, in the Diet Name Text Facet we created in the step above, click the Cluster button.
In the resulting pop-up window, you can change the algorithm method, and keying function. Try different combinations to see the difference.
Intentional errors in these diet names have been introduced to show how errors (typos) in any position can be found with this method. All errors can then be fixed by simply entering the correct value in the box on the right. Often, the algorithm has guessed correctly.
After corrections are made in this window, you can either Merge and Close the Cluster pop-up, or Merge and Re-cluster.

Split Columns

If data in a column needs to be split into multiple columns, and the strings in the cells are separated by a common separator (say a comma, or a space), you can use that separator to divide up the bits into their own columns.
Go to the drop-down tab at the top of the column that you need to split into multiple columns
For example, go to the Sample Date column > from drop-down choose Edit Column > Split into several columns
In the pop-up, for separator, remove the comma, put in a -
Remove the check in the box that says “remove column after splitting”
You’ll get three extra columns called, in this case: Data of Sample 1, Date of Sample 2, Date of Sample 3

Undo steps

To Undo create columns, look just above the Diet Name cluster in the left side of the screen. Click where it says Undo / Redo. Click back one step (all steps, all changes are saved here). Just go back to the previous step and click. The extra columns will be gone.

Scripts

Refine saves every change, every edit you make to the dataset in a file you can save on your machine.
IF you had 20 files to clean, and they all had the same type of errors, and all files had the same columns, you could save the script, open a new file to clean, paste in the script and run it. Voila, clean data.
In the Undo / Redo section, click Extract, save the bits desired using the check boxes. Save the code in a .txt file. To run these steps on a new dataset, import the new dataset into Refine, open the Extract / Apply section, paste in the .txt file, click Apply.

Export

Save your work when you are done by exporting it in the desire format. Save your files with meaningful names, no spaces. Refine does not change your original dataset.