This lesson takes about 100-115 minutes with learners installing the tool and working through the examples and challenges.
Technical tips and tricks
The current version of the lesson has been tested with OpenRefine 3.4.1. Using older versions is not recommended, behavior may be different and cause confusion in the learners.
Creating a Project
Start the program. (Double-click on the openrefine.exe file. Java services will start on your machine, and OpenRefine will open in your browser).
Note the file types OpenRefine handles: TSV, CSF, *SV, Excel (.xls .xlsx), JSON, XML, RDF as XML, Google Data documents. Support for other formats can be added with OpenRefine extensions.
In this first step, we’ll browse our computer to the sample data file for this lesson (If you haven’t already, download the data from:
https://ndownloader.figshare.com/files/7823341). In this case, I’ve modified the Portal_rodents.csv file. I added several columns: scientificName, locality, county, state, country and I generated several more columns in the lesson itself (JSON, decimalLatitude, decimalLongitude). Data in locality, county, country, JSON, decimalLatitude and decimalLongitude are contrived and are in no way related to the original dataset.
Once OpenRefine is open, you’ll be asked if you want to Create, Open, or Import a Project.
Click next to open Portal_rodents_19772002_scinameUUIDs.csv
OpenRefine 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.
Exploring data by applying multiple filters
OpenRefine supports faceted browsing as a mechanism for
seeing a big picture of your data, and
filtering down to just the subset of rows that you want to change in bulk.
Typically, you create a facet on a particular column. The facet summarizes the cells in that column to give you a big picture of that column, and allows you to filter to some subset of rows for which their cells in that column satisfy some constraint. That’s a bit abstract, so let’s jump into some examples.
Click the down arrow and choose Facet > Text facet
In the left margin, you’ll see a box containing every unique value in the scientificName column and OpenRefine 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 OpenRefine window, you have access to “edit” and can fix an error immediately. OpenRefine will even ask you if you’d like to make that same correction to every value it finds like that one (or not).
In OpenRefine, clustering refers to the operation of “finding groups of different values that might be alternative representations of the same thing”. For example, the two strings “New York” and “new york” are very likely to refer to the same concept and just have capitalization differences. Likewise, “Gödel” and “Godel” probably refer to the same person.
One of the most magical bits of OpenRefine, the moment you realize what you’ve been missing. OpenRefine has several clustering algorithms built in. Experiment with them, and learn more about these algorithms and how they work.
In this example, in the scientificName 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.
For example, with this dataset, the nearest neighbor method with the PPM keying function shows the power of clustering the best.
Intentional errors in these scientific 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 / Leading - Trailing Whitespace / Undo - Redo
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 scientificName column > from drop-down choose Edit Column > Split into several columns
In the pop-up, for separator, remove the comma, put in a space
Remove the check in the box that says “remove column after splitting”
You’ll get two extra columns called, in this case: scientificName 1, scientificName 2
This will reveal an error in a few names that have spaces at the beginning (so-called leading white space).
These can be easily removed with another OpenRefine feature in the column drop-down choices. See drop-down: Edit cells > Common transforms > Remove leading and trailing whitespace
To Undo create columns, look just above the scientificName 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.
If learners are using a browser other than Firefox, or OpenRefine does not automatically open for them when they click the .exe file, have them point their browser at http://127.0.0.1:3333/ or http://localhost:3333 to launch the program.
Mac users with the newest operating system will have to allow this to run by “allowing everything” to run. They can change the setting back after the exercise.
Some students will run into issues with
finding the .exe file once the software has been unzipped
finding the data file on their computers after downloading