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.
Understand potential problems with file headers.
Use facets to summarize data from a column.
Use clustering to detect possible typing errors.
Understand that there are different clustering algorithms which might give different results.
Employ drop-downs to remove white spaces from cells.
Manipulate data using previous steps with undo/redo.
Creating a new OpenRefine project
In Windows, you can start the OpenRefine program by double-clicking on the openrefine.exe file. Java services will start automatically on your machine, and OpenRefine will open in your browser. On a Mac, OpenRefine can be launched from your Applications folder. If you are using Linux, you will need to navigate to your OpenRefine directory in the command line and run
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 will be using data obtained from interviews of farmers in two countries in eastern sub-Saharan Africa (Mozambique and Tanzania). Instructions on downloading the data are available here.
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 Project and select
Get data from
Choose Files and select the file
Open or double-click on the filename.
Next>> under the browse button to upload the data into OpenRefine.
4. 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 then choose the correct separator in the box shown and click
Update Preview (bottom left). If this is the wrong file, click
<<Start Over (upper left). There are also options to indicate whether the dataset has column headers included and whether OpenRefine should skip a number of rows before reading the data.
5. 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 both 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 a big picture of your data, and
- filtering down to just the subset of rows that you want to change in bulk.
A ‘Facet’ groups all the like values that appear in a column, and then allow you to filter the data by these values 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 it appears in. 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
villagecolumn 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.
Chirdozois likely a mis-entry of
Rucais likely a mis-entry of
Ruaca - Nhamuendaand
Ruaca-Nhamuendarefer to the same place (differ only by spaces around the hyphen). You might also wonder if both of these are the same as
Ruaca. We will see how to correct these misspelled and mistyped entries in a later exercise.
- The entry
49is almost certainly an error but you will not be able to fix it by reference to other data.
Using faceting, find out how many different
interview_datevalues there are in the survey results.
Is the column formatted as Text or Date?
Use faceting to produce a timeline display for
interview_date. You will need to use
To dateto convert this column to dates.
During what period were most of the interviews collected?
For the column
Text facet. A box will appear in the left panel showing that there are 19 unique entries in this column. By default, the column
interview_dateis formatted as Text. You can change the format by doing
To date. Notice the the values in the column turn green. Doing
Timeline facetcreates a box in the left panel that shows a histogram of the number of entries for each date.
Most of the data was collected in November of 2016.
More on Facets
As well as ‘Text facets’ Refine 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 each word appears in
- 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.
Using clustering to detect possible typing errors
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
villageText 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 two clusters.
- Click the
Merge?box beside each cluster, 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 should find no more clusters are found. None of the available methods offered to cluster
Chirodzo. To merge these values we need to hover over them in the village text facet, select edit, and manually change the names.
Ruaca. You should now have four clusters:
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.
Different clustering algorithms
The technical details of how the different clustering algorithms work can be found at the link below.
The data in the
items_owned column is a set of items in a list. The list is in square brackets and each item is in single quotes. Before we split the list into individual items in the next section, we first want to remove the brackets and the quotes.
- Click the down arrow at the top of the
- This will open up a window into which you can type a GREL expression. GREL stands for General Refine Expression Language.
First we will remove all of the left square brackets (
[). In the Expression box type
value.replace("[", "")and click
What the expression means is this: Take the
valuein each cell in the selected column and replace all of the “[” with “” (i.e. nothing - delete).
OK. You should see in the
items_ownedcolumn that there are no longer any left square brackets.
Use this same strategy to remove the single quote marks (
'), the right square brackets (
]), and spaces from the
value.replace(" ", "")You should now have a list of items separated by semi-colons (
Now that we have cleaned out extraneous characters from our
items_owned column, we can use a text facet to see which items
were commonly owned or rarely owned by the interview respondents.
- Click the down arrow at the top of the
Custom text facet...
- In the
You should now see a new text facet box in the left-hand pane.
Which two items are the most commonly owned? Which are the two least commonly owned?
count. The most commonly owned items are mobile phone and radio, the least commonly owned are cars and computers.
Perform the same clean up steps and customized text faceting for the
months_lack_foodcolumn. Which month(s) were farmers more likely to lack food?
All four cleaning steps can be performed by combining
.replacestatements. The command is:
value.replace("[", "").replace("]", "").replace(" ", "").replace("'", "")This can also be done in four separate steps if preferred. November was the most common month for respondents to lack food.
Perform the same clean up steps for the
no_food_mitigationcolumns. Hint: To reuse a GREL command, click the
Historytab and then click
Reusenext to the command you would like to apply to that column.
Using undo and 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 go back several steps to before you had done any text transformation.
- Visually confirm that those columns now contain the special characters that we had removed previously.
- Notice that you can still click on the later steps to
Redothe actions. Before moving on to the next lesson, redo all the steps in your analysis so that all of the column you modified are lacking in square brackets, spaces, and single quotes.
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.
- Create a new text facet for the column
respondent_wall_type. You should see some choices that appear identical (
muddaubboth have two choices). In reality, one of these choices includes either leading or trailing whitespace.
- To remove the whitespace, choose
Trim leading and trailing whitespace.
- You should now see only four choices in your text facet.
OpenRefine can import a variety of file types.
OpenRefine can be used to explore data using filters.
Clustering in OpenRefine can help to identify different values that might mean the same thing.
OpenRefine can transform the values of a column.