Working with OpenRefine
OverviewTeaching: 15 min
Exercises: 20 minQuestions
How can we bring our data into OpenRefine?
How can we sort and summarize our data?
How can we find and correct errors in our raw data?Objectives
Create a new OpenRefine project from a CSV file.
Recall what facets are and how they are used to sort and summarize data.
Recall what clustering is and how it is applied to group and edit typos.
Manipulate data using previous steps with undo/redo.
Employ drop-downs to split values from one column into multiple columns.
Employ drop-downs to remove white spaces from cells.
Creating a Project
Start the program. Double-click on the openrefine.exe file (or google-refine.exe if using an older version). Java services will start on your machine, and OpenRefine will open in your browser.
Launch OpenRefine (see Getting Started with OpenRefine).
OpenRefine can import a variety of file types, including tab separated (
tsv), comma separated (
csv), Excel (
xlsx), JSON, XML, RDF as XML, and Google Spreadsheets. See the OpenRefine Importers page for more information.
In this first step, we’ll browse our computer to the sample data file for this lesson. In this case, we modified the
Portal_rodents CSV file, adding several columns:
country and generating several more columns in the lesson itself (
decimalLongitude). Data in
decimalLongitude are contrived and are in no way related to the original dataset.
If you haven’t already, download the data from:
Once OpenRefine is launched in your browser, the left margin has options to
Open Project, or
Import Project. Here we will create a new project:
Create Projectand select
Get data from
Choose Filesand select the file
Openor double-click on the filename.
Next>>under the browse button to upload the data into OpenRefine.
- 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(bottom right). If this is the wrong file, click
<<Start Over(upper left).
- If all looks well, click
Create Project>>(upper right).
Note that at step 1, you could upload data in a standard form from a web address by selecting
Get data from
Web Addresses (URLs). However, this won’t work for all URLs.
Exploring data by applying multiple filters
Facets are one of the most useful features of OpenRefine and can help get an overview of the data in a project as well as helping you bring more consistency to the data. OpenRefine supports faceted browsing as a mechanism for
- seeing the big picture of your data, and
- filtering down to just the subset of rows that you want to change in bulk.
A ‘Facet’ groups the values that appear in a column according to some criterion, and then allows you to filter the groups and edit values across many records at the same time.
One type of Facet is called a ‘Text facet’. This groups all the identical text values in a column and lists each value with the number of records in which it appears. The facet information always appears in the left hand panel in the OpenRefine interface.
Here we will use faceting to look for potential errors in data entry in the
- Scroll over to the
- Click the down arrow and choose
- In the left panel, you’ll now see a box containing every unique value in the
scientificNamecolumn along with a number representing how many times that value occurs in the column.
- Try sorting this facet by name and by count. Do you notice any problems with the data? What are they?
- Hover the mouse over one of the names in the
Facetlist. You should see that you have an
- You could use this to fix an error immediately, and OpenRefine will ask whether you want to make the same correction to every value it finds like that one. But OpenRefine offers even better ways to find and fix these errors, which we’ll use instead. We’ll learn about these when we talk about clustering.
There will be several near-identical entries in
scientificName. For example, there is one entry for
Ammospermophilis harrisiand one entry for
Ammospermophilus harrisii. These are both misspellings of
Ammospermophilus harrisi. We will see how to correct these misspelled and mistyped entries in a later exercise.
More on Facets
As well as ‘Text facets’ OpenRefine also supports a range of other types of facet. These include:
- Numeric facets
- Timeline facets (for dates)
- Custom facets
- Scatterplot facets
Numeric and Scatterplot facets display graphs instead of lists of values. The numeric facet graph includes ‘drag and drop’ controls you can use to set a start and end range to filter the data displayed. These facets are explored further in Examining Numbers in OpenRefine
Custom facets are a range of different types of facets. Some of the default custom facets are:
- Word facet - this breaks down text into words and counts the number of records in which each word appears
- Duplicates facet - this results in a binary facet of ‘true’ or ‘false’. Rows appear in the ‘true’ facet if the value in the selected column is an exact match for a value in the same column in another row
- Text length facet - creates a numeric facet based on the length (number of characters) of the text in each row for the selected column. This can be useful for spotting incorrect or unusual data in a field where specific lengths are expected (e.g. if the values are expected to be years, any row with a text length more than 4 for that column is likely to be incorrect)
- Facet by blank - a binary facet of ‘true’ or ‘false’. Rows appear in the ‘true’ facet if they have no data present in that column. This is useful when looking for rows missing key data.
Facets are intended to group together common values and OpenRefine limits the number of values allowed in a single facet to ensure the software does not perform slowly or run out of memory. If you create a facet where there are many unique values (for example, a facet on a ‘book title’ column in a data set that has one row per book) the facet created will be very large and may either slow down the application, or OpenRefine will not create the facet.
Using faceting, find out how many years are represented in the census.
Is the column formatted as Number, Date, or Text? How does changing the format change the faceting display?
Which years have the most and least observations?
- For the column
Text facet. A box will appear in the left panel showing that there are 26 unique entries in this column.
- By default, the column
yris formatted as Text. You can change the format by doing
To number. Doing
Numeric facetcreates a box in the left panel that shows a histogram of the number of entries per year. Notice that the data is shown as a number, not a date. If you instead transform the column to a date, the program will assume all entries are on January 1st of the year.
- After creating a facet, click
Sort by countin the facet box. The year with the most observations is 1997. The least is 1977.
In OpenRefine, clustering means “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,
Godel probably refer to the same person. Clustering is a very powerful tool for cleaning datasets which contain misspelled or mistyped entries. OpenRefine has several clustering algorithms built in. Experiment with them, and learn more about these algorithms and how they work.
- In the
scientificNameText Facet we created in the step above, click the
- In the resulting pop-up window, you can change the
Keying Function. Try different combinations to see what different mergers of values are suggested.
- Select the
key collisionmethod and
metaphone3keying function. It should identify three clusters.
- Click the
Merge?box beside each, then click
Merge Selected and Reclusterto apply the corrections to the dataset.
- Try selecting different
Keying Functionsagain, to see what new merges are suggested. You may find there are still improvements that can be made, but don’t
Closewhen you’re done. We’ll now see other operations that will help us detect and correct the remaining problems, and that have other, more general uses.
Important: If you
Merge using a different method or keying function, or more times than described in the instructions above,
your solutions for later exercises will not be the same as shown in those exercise solutions.
If data in a column needs to be split into multiple columns, and the parts are separated by a common separator (say a comma, or a space), you can use that separator to divide up the pieces into their own columns.
- Let us suppose we want to split the
scientificNamecolumn into separate columns for genus and for species.
- Click the down arrow at the top of the
Split into several columns...
- In the pop-up, in the
Separatorbox, replace the comma with a space.
- Uncheck the box that says
Remove this column.
OK. You’ll get some new columns called
scientificName 2, and so on.
- Notice that in some cases
scientificName 2are empty. Why is this? What do you think we can do to fix this?
- Note that the character on which the split is performed could be anything. The default is a comma, and you changed that to a space in this case, but you could make it any letter or number or special character. The only requirements are that A) it appears in every row of the column, and B) it appears consistently in the place where you want the column to be split.
The entries that have data in
scientificName 4but not the first two
scientificNamecolumns had an extra space at the beginning of the entry. Leading white spaces are very difficult to notice when cleaning data manually. This is another advantage of using OpenRefine to clean your data. We’ll look at how to fix leading and trailing white spaces in a later exercise.
Try to change the name of the second new column to “species”. Are you able to do this? Or do you encounter a problem?
scientificName 2column, click the down arrow and then
Rename this column. Type “species” into the box that appears. A pop-up will appear that says
Another column already named species. This is because there is another column where we’ve recorded the species abbreviation. If you capitalize the S, it will work. Or you can choose another name
speciesNamefor this column or change the other
speciescolumn name to
Undo / Redo
It’s common while exploring and cleaning a dataset to discover after you’ve made a change that you really should have done something else first. OpenRefine provides
Redo operations to make this easy.
- Click where it says
Undo / Redoon the left side of the screen. All the changes you have made so far are listed here.
- Click on the step that you want to go back to, in this case the previous step. The added columns will disappear.
- Notice that you can still click on the last step and make the columns reappear, and toggle back and forth between these states.
- Leave the dataset in the state in which the
scientificNameswere clustered, but not yet split.
Important: If you skip this step, your solutions for later exercises will not be the same as shown in those exercise solutions.
Trim Leading and Trailing Whitespace
Words with spaces at the beginning or end are particularly hard for we humans to tell from strings without, but the blank characters will make a difference to the computer. We usually want to remove these. OpenRefine provides a tool to remove blank characters from the beginning and end of any entries that have them.
- In the header for the column
Trim leading and trailing whitespace.
- Notice that the
Splitstep has now disappeared from the
Undo / Redopane on the left and is replaced with a
Text transform on 3 cells
- Perform the same
scientificNamethat you undid earlier. This time you should only get two new columns. Why?
Removing the leading white spaces means that each entry in this column has exactly one space (between the genus and species names). Therefore, when you split with space as the separator, you will get only two columns.
Undo the splitting step before moving on to the next lesson. If you skip this step, your solutions
for later exercises will not be the same as shown in those exercise solutions.
Faceting and clustering approaches can identify errors or outliers in data.