Exploring Open Refine


Chapter 1 Overview in Relation to Excel

When using Open Refine, I noticed many similarities in regards to its Excel counterpart. After working my way through the chapter, I can definitely say that I prefer Open Refine’s editing tools over those available in Excel. The ability to undo/redo from any part of the project is more convenient than Excel. In Excel, you would have to constantly save another version outside of the original dataset so that no permanent damage is done to the original data. To record my thoughts throughout chapter 1, I made notes of each recipe as I worked through them.

Recipe 1
In recipe 1, I had to install the Open Refine application. For the most part, this was pretty straight forward, but I did have to go in and edit my security settings before it would allow me to complete the download. After doing that, I was ready to go.

Recipe 2
In recipe 2, I had to upload a dataset into Open Refine. Once uploading, I was able to play around with the settings to see how it could affect the data. A dataset that once appeared as a confusing mess of information was able to transform into a nice, orderly list that could be easily read. When deselecting the quotation option, I was able to get Open Refine to ignore the quotation marks and separate the information properly. I saw some similarities in the process that would have been performed in Excel, but right from the beginning, I realized that Open Refine had some additional features for displaying data that Excel lacked.

Recipe 3
In recipe 3, I was able to become more familiar with Open Refine’s many tools. One that I found rather useful was the all column that helps differentiate between clean and non-clean records. When dealing with a lot of records, this is a great tool that can help you keep track of where you left off. Unlike Excel, this feature gives you a sort and fill that lets you distinguish between edited and unedited records through the use of the flag tool.

Recipe 4
In recipe 4, I learned how to collapse, expand, rename, remove, and move around records. In my time working with these functions, I found performing these tasks simpler than if they were performed in Excel. My favorite feature was, once again, the all column. I found the ability to reorder and even collapse columns through it an easier way of working with column edits than performing the tasks individually.

Recipe 5
In recipe 5, I learned about the magic that is project history. Out of all of the features I have learned about in chapter 1, this is my favorite. Unlike Excel documents where once saved and closed it is altered forever, Open Refine lets you go back and return to any point in the project regardless of how much time has passed. This is extremely beneficial because it means you do not have to constantly save new versions of the dataset to prevent permanent changes like you would in an Excel document. It is worth noting that you still have to be extremely cautious when making changes so that you do not permanently undo a set of actions by reverting to an earlier version. This reminded me of having a notes tab in Excel that keeps track of what you have done, but unlike Excel, Open Refine lets you undo what you want without guessing.

Recipe 6
In recipe 6, I learned about the many different ways of exporting data. To obtain a copy of the altered data, you have to export the data in some way, shape, or form. While you do have to download the data, you have some degree of choice as to how you choose to do it. This was similar to Excel because you have some degree of autonomy when it comes to choosing a file format.

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