Exploring Chapter 2 of Open Refine Pt. 2

Recipe 4
In recipe 4, I learned how to perform text filters, and just like searching any type of document, it is riddled with problems. While you can certainly perform a text filter, it does not account for the many variations of a word that might have been typed in. In my opinion, this feature is handy if dealing with a dataset that you created or even a dataset with consistent entries (do those even exist?). This reminded me of the find feature in Excel. It is a quick way to retrieve data, but accurate results are not guaranteed.

Recipe 5
In recipe 5, I learned more about retrieving records out of a dataset. The most beneficial to me was the ability to edit cells to remove uppercase letters. What I enjoyed most about this feature was the ability to edit multiple cells at once. It is a quick way to make changes to a large set of data.

Recipe 6
In recipe 6, I learned how to apply everything that I learned throughout the chapter and make changes to my dataset. While I appreciated learning the purpose of each and every tool throughout the chapter, the ability to actually make changes in the dataset helped bring what I learned to a head as I could make permanent changes to my dataset. While I know that these changes can easily be undone through the undo/redo tab, it was nice seeing how all of these tools can work together to eliminate duplicate data, mistakes, and empty records that have no place within the entire dataset.

Through the rest of the recipes, I was able to explore more tools within Open Refine. After testing out multiple features, having a recipe like 6 was a great way to put multiple features to work at the same time to achieve some level of data cleaning.

Comments

  1. StevenNovember 18, 2019 at 6:28 AM

    Good work! Keep up those OpenRefine skills and be sure to put it in your CV when you look for positions!

    Dr. MacCall

    ReplyDelete

Post a Comment

Popular posts from this blog

Working through R: Part. 1

Over the course of the weekend, I have had some time to explore R and its many parts as I worked through parts 1-3 of our assignment. There were many times during the assignment where I felt like throwing my computer across the room… (I admit, I did toss it across the bed and scream) but I persisted, nonetheless. While I have found R a rather fascinating tool when working with data, it is quite frustrating if you are a novice. Muddling through the instructions, I encountered many issues when it came to installing packages, connecting with ORCID, and lastly performing some of the functions on Clarke’s tutorial. Perhaps, this was in part due to the uploading procedures not being as described in the instructions--that could be a Mac issue? Anyway, after I finally got connected and got the data loaded into R, I was ready to go. It started off great, and I was able to easily explore the dataframes. I was even able to receive the same results as Clarke showed in his tutorial; what a succes...
Read more

Working through R: Part. 2

Last night, the amazing Brittani sent me a wonderful tutorial on how to input data on R when using a Mac computer. This was the most beneficial thing I have received for this assignment, as without it I would have been unable to work through part 3 of Clarke’s data exploration tasks. This weekend, I thought I had uploaded the data in its entirety, but I was mistaken. Using the tutorial, I was able to add my data into R and get started on Clarke’s tutorials. With the data in the system, I was able to quickly work through the tutorial and retrieve the intended results. While I did have a few hiccups, Clarke’s tutorial had some slight errors in field names, I was able to resolve the problems and continue through the tutorial. One thing that I noticed during my attempts is that capitalization matters! If you forget to capitalize something it could very well result in an error message. That was a learning experience, but I caught on quickly. Overall, I was able to muddle through the tutoria...
Read more

Experiencing Wikidata Query Service

Today, I took the time to explore Wikidata SPARQL. The first query I ran involved locating triples that had an instance of house cat. The query results appeared in a spreadsheet style format below my search. After learning how to perform these queries, I was able to explore Wikidata and craft my very own queries. To start, I did a simple transition from house cats (Q146) to sloth (Q2274076). Since I kept my property the same but changed the value, I received the same type of exhaustive list that house cats produced but for sloths. Feeling semi-confident about venturing into new queries, I performed additional property value searches that would allow me to text my knowledge of the overall process. During one of my explorations, I wanted to explore books that fell within the same series as Harry Potter and the Philosopher’s Stone . At first, I attempted my query with the property as part of the series (P179) with the value as Harry Potter and the Philosopher’s Stone (Q102438). This sea...
Read more