Cleaning Untidy Data with OpenRefine


Section Time
Setup 00:00
Performing Basic Functions in OpenRefine 00:10
Clustering and restructuring data in OpenRefine 01:00
Finish 02:00

(The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.)

Setup instructions

You can download OpenRefine from There are versions for Windows, Mac OS X and Linux.

Installing OpenRefine

For Windows and Linux, the address above will provide a zip file. Unzip the downloaded file wherever you want to install the program.

For Mac, you will be downloading a ‘dmg’. Open it and then drag the OpenRefine application to an appropriate folder on you computer.

You need to have a ‘Java Runtime Environment’ (JRE) installed on your computer to run OpenRefine. If you don’t already have one installed then you can download and install it from Click on “Free Java Download”.

Running OpenRefine

On Windows: Navigate to the folder where you’ve installed OpenRefine and either double-click ‘google-refine.exe’ (for Google Refine 2.5), ’openrefine.exe’ (for OpenRefine 2.7) or ‘refine.bat’ (for either)

On Linux: Navigate to the folder where you’ve installed OpenRefine in a terminal window and type ‘./refine’

On Mac: Navigate to where you installed OpenRefine and click the OpenRefine icon

The interface to OpenRefine is accessed via a web browser. When you run Refine normally this should open a window in your default web browser pointing at the address If this doesn’t happen automatically you can open a web browser and type in this address.

Part 1: Performing Basic Functions in OpenRefine

Personal consumption expeditures dataset

In this activity set, we will be:

Download the dataset

Download the expenditures by state dataset here.

Review the dataset and load it into OpenRefine

  1. Open the file expenditures_by_state.xlsx in Excel and take a look at it. This is a freely available dataset from the Bureau of Economic Analysis, which provides average expenditures on a wide range of goods and services. Notice the following:

    • The data file has been formatted for reading rather than analysis. It has a blank column A. It has some rows at the top containing descriptive information that is not part of the data table. We can also see that each state is only listed once, which is fine for viewing, but will mess things up if we try to sort the data in order to analyze it.
    • The “Expenditure” column has leading spaces in it.
  2. Close the Excel file. Next, start up OpenRefine.
  3. Ensure that Create Project is selected. Click on Choose Files. Browse to the file expenditures_by_state.xlsx. Click Open. Then, click the Next button.
  4. You are now viewing the dataset in Preview view. Here you can see what data will look like when loaded, and make some changes to the dataset. Notice the following:

    • The descriptive text at the top of the Excel worksheet is showing in the preview, and is messing up OpenRefine’s ability to identify the column headings. We can instruct OpenRefine to ignore these rows that aren’t part of the data table. Select the check box beside Ignore first, and type 6 in the box to ignore the first 6 lines at the beginning of the file.
    • Numbers are displayed in green–this means OpenRefine has recognized these columns as containing numeric data
  5. In the Project name box, give the project a name of your choice. Click Create Project.
  6. Your data has now been loaded into OpenRefine. Note that it has stored a copy of this data with the OpenRefine installation files on your computer. When you make edits using OpenRefine, you are not editing the original data file you uploaded, all edits are made to the copy OpenRefine has created.

Perform some basic data cleanup

  1. In the top toolbar, select 50 in order to show more rows on the screen at once.
  2. Remove the blank columns: Look for the pull down menu for the column named “Column”. From the pull down menu, select Edit Column -> Remove this Column.
  3. Change a column name: Click on the pull down menu for the column name “GeoName”. Under Edit Column, choose Rename this Column. Rename the column to “State” and click OK.
  4. Fill down data: We want to fill the entries down in the State column so that all rows have a state associated with them. From the State column pull down menu, select Edit Cells -> Fill Down. In the top toolbar, click next a few times in order to look at a few pages of results. Verify that the fill operation seems to have worked.
  5. Delete leading whitespace: Hover your cursor over a cell in the Expenditure column and click edit. You’ll see that there are leading whitespaces that could cause problems down the road. Click ‘Cancel’ to close the Edit window. From the Expenditure column pull down menu, select Edit cells -> Common transforms -> Trim leading and trailing white space. Check a cell to verify that the leading spaces are gone.

Sort, filter and facet data

  1. Sort data on a row: Rows of data are initially loaded in the order they appear in the original data file. In this case, they are grouped by state, with Alabama first. To change the sort, from the State column pull down menu, select Sort… In the Sort window, sort as text, ordered from z-a. Click OK.
  2. Add a secondary sort: From the 2017 column pull down menu, select Sort…, and sort by numbers, from largest first. Notice the “sort by this column alone” option – that only appears when there is already one or more sorts in place. If you don’t check that option, it will keep the original sort and add this as a secondary sort.
  3. Remove a sort: You can remove a sort at any time by pulling down the column’s menu, and choosing Sort -> Remove sort.
  4. Filter the data: Filtering allows us to search for certain information within our dataset. Let’s say we want to display only the Pennsylvania data. From the State column pull down menu, choose Text filter. The text filter appears in the left-hand sidebar, under the “Facet/Filter” tab. Type ‘pennsylvania’ in the search box. OpenRefine automatically removes any rows from the display that don’t match, leaving a total of 24 rows remaining (out of 1227 total).
  5. Add a second filter: We can have text filters on more than one column at a time. From the Expenditure column pull down menu, choose Text filter. Type ‘food’ in the search box for that filter. The two filters are combined, showing us all the food-related expenditure categories for Pennsylvania.
    • Note that any functions you perform on filtered data will only be performed on the filtered records, not the entire dataset. This is also true of exporting the file–if you have filters applied, only the filtered data will appear in the export.
  6. Remove filters: You can remove a filter by clicking on the x in the top left-hand corner of the filter box. Remove both filters now. You should have all 1227 rows displayed again.
  7. Creating a facet: A facet summarizes all the values that appear in the column, and lets you select which data to view, as well as provides ways to edit the data. From the State column pull down menu, choose Facet -> Text facet. The facet appears in the left-hand sidebar, in the same area where the filters were previously. It shows you how many total values there are in this column (50), how many rows contain each value (for this dataset it is the same for each, 24), and allows you to sort the values by name or by count (count won’t be helpful in this case since they all have the same count).
  8. Filter data using facets: Click on Pennsylvania in the value list. This has the same effect as using the text filter to search for Pennsylvania, leaving 24 matching rows. However, from there we can do more than the filter allowed. We can select a second value at the same time. Hover your cursor over West Virginia in the value list and choose include. You can then exclude one or both of the selections at any time. Hover your cursor over Pennsylvania in the value list and choose exclude. Now only West Virginia rows are shown.
  9. Working with numeric facets: From the 2017 column pull down menu, expand Facet, and look at the options. There are some other types of facets available, including numeric facets. If we created a numeric facet now, it would only work for this column, so you would have to facet each year of data separately. Let’s manipulate the data a bit first, and then come back and work with numeric facets.

Activity 1: In addition to the text facet on State, add another text facet on the expenditure column. Using the facets, view the Pennsylvania, West Virginia and Ohio data for “Gasoline and other energy goods” expenditures. Which state spent the most per capita in 2017?

Bonus Activity: Remove the above facets. Apply a numeric facet to the 2017 columns and facet to only expenditures over $30,000. Using an additional facet, determine the expenditure category with the most values over $30000.

Transpose the data from wide format to long format

  1. Wide vs. Long format: The dataset is currently in “wide” format with years across as columns. We should convert it to “long” format to work with it using numeric facets. Converting to long format will put all the years into one column as a ‘Year’ variable, and all the numeric data values into a second column.
  2. Transpose the data to long format: Remove an facets you have in place. From the 2017 column pull down menu, select Transpose -> Transpose cells across columns into rows….The Transpose window appears. You are going to put the data from the 8 numeric data columns (named 2010 through 2017) into two columns, one containing the year, and one containing the numeric data value (representing an expenditure amount). For the From column, choose 2010. For the To column choose 2017 (or last column, either will work). In the Transpose into section, we will use the Two new columns option. The Key Column will be the years – call it Year. Give the Value Column the name Per_capita_expenditure. Check the Fill down in other columns option. Click Transpose.

Note: For each state, for each expenditure type, we now have 8 separate rows, one for each year. Notice the dataset now has 9792 rows, compared to 1227 before transposing. It has fewer columns, but many more rows – this is why it is referred to as a “long” format. Long format can be useful for certain types of data analyses, where all your data measuring the same thing (e.g., per capita expenditures) need to be in one column instead of spread over many.

Explore more advanced uses of facets

  1. Work with numeric facets: From the new Per_capita_expenditure column pull down menu, choose Facet -> Numeric facet. Numeric facets provide a sliding scale where you can choose which values to include. Notice the blue areas indicate where the values fall – you can see where the bulk of your values lie, and where there are some outliers. Let’s try to remove the outliers by dragging the handles so the facet includes only the largest block of blue values. This removes a number of rows from the display.
  2. Clean up non-numeric cells: At the bottom of the numeric facet, there are options to show Non-numeric values, Blanks, or Errors in this column. There are no blanks or errors in this data column, but there are non-numeric values. Uncheck Numeric to look only at the Non-numeric values. Most of these have values of “F” in them, but some of them are actually blank! Why are they included here rather than counted as blank cells by the facet? Hover your cursor over a blank cell and click Edit. There are spaces in this cell – remove them using Edit cells -> Common transforms -> Trim leading and trailing white space. Notice in the facet that there are now a number of cells recognized as blank. Note: In OpenRefine, any actions you perform are only applied to the rows currently selected, i.e., the above task was only applied to the non-numeric cells that are currently selected.
  3. Use facets to edit data in bulk: What does the “F” value mean? This was included in the information at the top of the original spreadsheet, which we removed when we loaded it into OpenRefine. If you were to go back and look at the Excel file you’ll see that “F” means the data was too unreliable to be published. If you wanted to change the value of “F” to be something more descriptive, you can use facets to edit data in bulk. However, we can’t do it from a numeric facet, we need a text facet instead. From the Per_capita_expenditure column pull down menu, select Facet -> Text facet. Notice that only the non-numeric values are listed – this is because you still have only non-numeric values selected (via the numeric facet). Hover your cursor over the value F and choose edit. Change F to something more descriptive, such as Not published. Click Apply. All values of F in the dataset are automatically changed to Not published.

In summary: filters are for free text searching; you can identify all matches of your search string in the column. Facets are for structured viewing and editing of unique values.

Export data from OpenRefine

  1. Remove any facets you have in place. In the top right-hand corner of the screen, pull down the Export menu and choose comma separated value (or Excel, or whatever format you would like to download).

    • Note that if you choose Export project, it will export as a zip file with the original and final versions of you data and various versions in between. You could open the project again in OpenRefine to view your process.

Part 2: Clustering and restructuring data in OpenRefine

Citizen science dataset

In this activity set, we will be:

Download the dataset

Download the citizen science dataset here.

Create a new project from the citizen science dataset

  1. Start a new project: Start up OpenRefine (if it isn’t running) or click on the OpenRefine logo on the top left to go to the main screen. Note: If you were working with another project, it has been automatically saved in OpenRefine and the files are stored locally on your computer. You can see your project listed here and can use Open Projects to go back to it later.
  2. Import your dataset: Click on Create Project and import the citizenscience.csv file. Maintain the default settings, rename your project and click Create Project. You should see that there are 1991 records in our dataset. Click on show 50 rows to show more rows displayed in the window.

Use the clustering feature to make your data consistent

  1. Create a text facet: Go to the species_guess column (where our citizen scientists have made a guess as to what species they have observed). From the species_guess column pull down menu, select Facet->Text facet.
  2. Examine your data: You’ll see that some of them are a bit unusual, and in those cases, you may want to edit them; however, in other cases, you’ll see that there are two facets that look very similar, but just have different capitalization, such as American Pokeweed. When we have facets that look similar, we can use OpenRefine’s clustering features to help improve the consistency of the values in that column. Click on the Cluster button at the top of the facet window.
  3. Set your clustering algorithm: At the top of the screen, you’ll see that there are different methods and keying functions you can choose from to find clusters. They roughly go from more strict/unforgiving to looser. Let’s keep the default for now. Note: In this case, you should see that the column values are just variations in capitalization, but clustering can also catch typos, plural vs singular and other small differences as well.
  4. Cluster similar values: You can see that it has found entries that it thinks are all referring to the same thing and suggests merging them under one recommended facet. You can put a check mark next to the ones you agree with, and edit the heading that you want to merge them into–or just click on the name you want to use. Go through and merge the entries found into new terms that have only the first word capitalized by adding a check mark under Merge? and adjusting the New Cell Value. When done, click on Merge Selected & Re-Cluster. You might’ve noticed that as you did a merge, it flashed at the top of the screen how many rows were affected/mass edited.

Activity 2: Try a different clustering algorithm and see if you can clean up the species_guess column more. Try different clustering algorithms and see if you can clean up the species_guess column more. You can ‘Select All’ and quickly scan the suggestions to merge in bulk. After cleaning up the column, can you identify what the most common bird species is in the dataset?

Bonus activity: Find all of the blank cells in the common_name column and rename them to N/A. Bonus activity: In the coordinates_obscured column, change all ‘false’ values to ‘0’ and all ‘true’ values to ‘1’.

Split columns in the dataset

  1. Split the scienctific name colunm into two: We have a column called scientific_name. With scientific names, the first part is the genus name and the second part is the specific name. So let’s split this column so we can see how many of a particular genus were identified. From the scientific_name column pull down menu, select Edit column–>Split into several columns. For the separator, put a space, split into 2 columns at most, and uncheck Remove this column because we want to keep it. Then click on OK.
  2. Edit column names: From each new column’s pull down menu, select Edit column–>Rename this column. For the first one, call it genus. For the second one, call it specific. You could use text facets on the genus column to see how many of each genus were identified. From the genus column pull down menu, select Facet–>Text facet In the facet window, click on Sort by count to sort the facets and see which genus comes out on top.

Concatenate columns in the dataset

  1. Add a new column based on an existing column: You can concatenate (join) strings and/or values from two or more columns together. Let’s say that we wanted to combine the information on the user id and login into one column with the format username (user id). For this example, we’re going to create a new column to store this information using the Add column based on this column feature. From the user_id column pull down menu, select Edit column–>Add column based on this column. Give the new column the name User.

  2. Use GREL to define a format: OpenRefine’s scripting language is called GREL (Google Refine Expression Language). We can use it to define combinations of characters, strings, and numbers. In our expression to define a new column, value refers to the value of the current column. If we want to refer to another column in our expression we use the term cells. and then the name of a column then .value So for the expression in this case, type cells.user_login.value + “ (“ + value + “)” The plus sign is used to join the different values or strings together into one long string. So we’re creating a string that is the user login, a space, and then the user id in parentheses.

Actvity 3: Concatenate the scientific_name column with the common_name in parentheses into a new column called Descriptive Name. The format should be <scientific_name> (<common_name>).

Restructure the dataset by removing columns and rows, and then work with Undo/Redo to roll those changes back

  1. Delete columns in bulk: Go to the special All column pull down menu on the far left. From the All column pull down menu, select Edit columns->Re-order / remove columns… From here you can drag columns from the left to the right to remove them – do this for private latitude and private longitude. We can also reorder columns. Move license, species_guess and quality_grade columns to just under id to move those columns more to the left. Click on OK to make the changes.
  2. Flag or star rows: Click on the flag symbol next to a row of interest – try flagging the first few rows of the dataset.
  3. Use facets to define and flag a subset: From the license column pull down menu, select Facet–>Customized facets–>Facet by blanks. Click on true to show only the rows where that column is blank (i.e., rows where no license has been specified). From the quality_grade column pull down menu, select Facet–>Text facet Select the casual facet. Now we have a subset of rows that have a blank for license and are casual observations. Let’s say that these 18 rows were no good to use. We could flag them (or star them or remove them). From the All column pull down menu, select Edit rows->Flag rows
  4. Remove flagged rows: Reset all facets by clicking on the Reset All button on the left above the facet windows. Now you should see all the rows in your dataset again, some are flagged and some are not. Later if you decide that you want to remove those flagged rows that you were unsure of, you can. From the All column pull down menu, select Facet–>Facet by flag and then select true from the facet window to show only your flagged rows. You can delete all of them. From the All column pull down menu, select Edit rows–>Remove all matching rows. All the flagged rows should now be removed from the dataset. Reset all facets again by clicking on the Reset All button on the left above the facet windows to see your remaining rows.
  5. Undo and redo actions: Click on the Undo/Redo tab above where the facets show up. You’ll see a number of steps that outlines everything we did to this dataset. It is a great way to keep track of what you’ve done. You can also roll back your changes to a previous version by clicking on the last step you were happy with. Then everything after that has been rolled back. You can go back and forth in time to take a look at the dataset at a particular point. For example, click on the item that says Reorder columns. You’ll see that the steps after that have greyed out, which means they haven’t happened yet. So for this example, those flagged rows have now not be deleted, and you should see them in your dataset.

Note: If you go back to a previous step (like we’ve just done), and then start making new changes/transformations - all the subsequent steps will be deleted permanently.

Bonus: Removing Duplicates A common clean up process is the removal of items that are duplicates based on one or more columns. For a step-by-step guide to using faceting for this purpose, see

Extract a JSON script to reproduce your steps with another data file

  1. If you have a similarly structured dataset – perhaps for a different snapshot in time – and want to perform the same steps, we can extract a JSON script for future use. Click on the Undo/Redo tab and click on Extract. Choose the steps you want to repeat. Copy the code and save it in a text file to keep a copy of your steps. Later if you load up your new dataset, you could go back to the Undo/Redo tab and select Apply and paste in this code into the window to run those steps on the new dataset.

Activity 4: Start up a new project and load the citizen science dataset again. Apply the JSON code that we just copied by going to the Undo/Redo tab, clicking Apply and copying and pasting the code into the window. Click Perform Operations.

Shutting down OpenRefine

  1. To ensure that all of your steps are saved, it is important to properly shut down OpenRefine.

    • On a PC, hit Control-C on your keyboard.
    • On a Mac, go to the OpenRefine app in the doc and choose Quit.