Transforming Data

Overview

Questions

• How can we transform our data to correct errors?

Objectives

• Learn about clustering and how it is applied to group and edit typos
• Split values from one column into multiple columns
• Manipulate data using previous cleaning steps with undo/redo
• Remove leading and trailing white spaces from cells

Data splitting

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We can split data from one column into multiple columns if the parts are separated by a common separator (say a comma, or a space).

1. Let us suppose we want to split the scientificName column into separate columns, one for genus and one for species.
2. Click the down arrow next to the scientificName column. Choose Edit Column > Split into several columns...
3. In the pop-up, in the Separator box, replace the comma with a space (the box will look empty when you’re done).
4. Important! Uncheck the box that says Remove this column.
5. Click OK. You should get some new columns called scientificName 1, scientificName 2, scientificName 3, and scientificName 4.
6. Notice that in some cases these newly created columns are empty (you can check by text faceting the column). Why? What do you think we can do to fix it?

The entries that have data in scientificName 3 and scientificName 4 but not the first two scientificName columns had an extra space at the beginning of the entry. Leading and trailing white spaces are very difficult to notice when cleaning data manually. This is another advantage of using OpenRefine to clean your data - this process can be automated.

In newer versions of OpenRefine (from version 3.4.1) there is now an option to clean leading and trailing white spaces from all data when importing the data initially and creating the project.

Exercise

Look at the data in the column coordinates and split these values to obtain latitude and longitude. Make sure that the option for Guess cell type is checked and that Remove this column is not. Rename the new columns.

What type of data does OpenRefine assign to the new colunms?

Show me the solution

Both new columns will appear with green text, indicating they are numeric. The option for Guess cell type allowed OpenRefine to guess that these values were numeric.

Undoing / Redoing actions

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It is common while exploring and cleaning a dataset to make a mistake or decide to change the order of the process you wish to conduct. OpenRefine provides Undo and Redo operations to roll back your changes.

1. Click Undo / Redo in the left side of the screen. All the changes you have made will appear in the left-hand panel. The current stage in the data processing is highlighted in blue (i.e. step 4. in the screenshot below). As you click on the different stages in the process, the step identified in blue will change and, far more importantly, the data will revert to that stage in the processing.

2. We want to undo the splitting of the column scientificName. Select the stage just before the split occurred and the new scientificName columns will disappear.

3. Notice that you can still click on the last stage and make the columns reappear, and toggle back and forth between these states. You can also select the state more than one steps back and revert to that state.

4. Let’s leave the dataset in the state before scientificNames was split.

Trimming leading and trailing whitespace

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Words with spaces at the beginning or end are particularly hard for humans to identify from strings without these spaces (as we have seen with the scientificName column). However, blank spaces can make a big difference to computers, so we usually want to remove them.

1. In the header for the column scientificName, choose Edit cells > Common transforms > Trim leading and trailing whitespace.
2. Notice that the Split step has now disappeared from the Undo / Redo pane on the left and is replaced with a Text transform on 2 cells

Exercise

Repeat the splitting of column scientificName exercise after trimming the whitespace.

Show me the solution

On the scientificName column, click the down arrow next to the scientificName column and choose Edit Column > Split into several columns... from the drop down menu. Use a blank character as a separator, as before. You should now get only two columns scientificName 1 and scientificName 2.

Renaming columns

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We now have the genus and species parts neatly separated into 2 columns - scientificName 1 and scientificName 2. We want to rename these as genus and species, respectively.

1. Let’s first rename the scientificName 1 column. On the column, click the down arrow and then Edit column > Rename this column.
2. Type “genus” into the box that appears.

Exercise

Try to change the name of the scientificName 2 column to species. What problem do you encounter? How can you fix the problem?

Show me the solution

1. On the scientificName 2 column, click the down arrow and then Edit column > Rename this column.
2. Type “species” into the box that appears.
3. A pop-up will appear that says Another column already named species. This is because there is another column with the same name where we’ve recorded the species abbreviation.
4. You can use another name for the scientificName 2 or change the name of the species column and then rename the scientificName 2 column.

Edit the name of the species column to species_abbreviation. Then, rename scientificName 2 to species.

Combining columns to create new ones

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The date for each row in the data file is split in three columns: dy (day), mo (month), and yr (year). We can create a new column with the date in the format we want by combining these columns.

• Click on the menu for the yr column and select Edit column > Join columns....

• In the window that opens up, check the boxes next to the columns yr, mo, and dy.

• Enter - as a separator.

• Select the option Write result in new column named and write date as the name for the new column.

• Click OK

  

You can change the order of the columns by dragging the columns in the left side of the window.

Once the new column is created, convert it to date using Edit cells > Common transforms > To date. Now you can explore the data using a timeline facet. Create the new facet by clicking on the menu for the column date and select Facet > Timeline facet.

Data clustering

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Clustering allows you to find groups of entries that are not identical but are sufficiently similar that they may be alternative representations of the same thing (term or data value). For example, the two strings New York and new york are very likely to refer to the same concept and just have a capitalization differences. Likewise, Björk and Bjork probably refer to the same person. These kinds of variations occur a lot in scientific data. Clustering gives us a tool to resolve them.

OpenRefine provides different clustering algorithms. The best way to understand how they work is to experiment with them.

The dataset has several near-identical entries in scientificName. For example, there are two misspellings of Ammospermophilus harrisii:

• Ammospermophilis harrisi and
• Ammospermophilus harrisi

1. If you removed it, reinstate the scientificName text facet (you can also remove all the other facets to gain some space). In the scientificName text facet box - click the Cluster button.

2. In the resulting pop-up window, you can change the Method and the Keying Function. Try different combinations to see what different mergers of values are suggested.

3. If you select the key collision method and the metaphone3 keying function. It should identify one cluster:

4. Note that the New Cell Value column displays the new name that will replace the value in all the cells in the group. You can change this if you wish to choose a different value than the suggested one.

5. Tick the Merge? checkbox beside each group, then click Merge selected & Close to apply the corrections to the dataset and close the window.

6. The text facet of scientificName will update to show the new summary of the column. It will now have ten options:

Clustering Documentation

Full documentation on clustering can be found at the OpenRefine Clustering Methods In-depth page of the OpenRefine manual.

Key Points

• Clustering can identify outliers in data and help us fix errors in bulk