Adding R to the Data Toolkit

by Kristin Briney Posted on 2019-11-05

I’ve officially jumped on the R bandwagon. I worked on a project last year for which R turned out to be the best solution to tackling a lot of messy data (OpenRefine was not reproducible enough and let’s not even talk about the disaster that was Access). Since then, I’ve thrown other data at R and now consider it as part of my regular suite of data tools.

I want to emphasize that last point that R is just one piece in the data toolkit. Software like R has a steep learning curve if you’ve never programmed before. There are other tools, like OpenRefine, that get the job done and are friendlier to the average user. But for processing large amounts of data in a reproducible way, R is definitely worth learning. (Here’s roughly how I break my data needs down: Excel is for everyday data work; OpenRefine is for one-off data cleaning; and R is for large scale/reproducible data cleaning and processing.)

So if you find yourself with a lot of data to process, I have some tips for learning R:

  • Run R in RStudio.
    • It takes a little effort to learn the RStudio interface but it will be a better experience if you’re not used to the command line (base R).
  • Have a problem to solve.
    • Learning a programming language is always easier if you have a specific task to accomplish.
  • Take advantage of existing resources.

Finally, I should say that I’m a patron of the Tidyverse, which is a flavor of R that comes with its own tools and methods for data handling. The Tidyverse makes data cleaning easy but you do have to organize your data in a particular way, with columns as variables and rows as individual observations. Tidy data is not condensed data and usually leads to a few columns with rows and rows of data, but this formatting enables streamlined processing. It’s not necessary to use the Tidyverse to use R, but it can be quite useful.

R is not the most efficient way to solve every data problem and it takes time to learn, but I think there is an advantage to learning a language like R (or Python or…) if you have serious data manipulation needs. Does it have a place in your data toolkit?

Posted in dataAnalysis | 1 Comment

Breaking the Blogging Silence

by Kristin Briney Posted on 2019-10-01

Wow what a year it’s been. I know that it has been really quiet on the blog since this time last year and that’s because life has been anything but quiet!

I stopped posting here a year ago when we added a new member to the family. And just when things looked to be calming down, we decided to move cross-country from Wisconsin to California. It’s been a big change and I’m still getting used to the weather, the culture, and the commute.

The good news is that I’ve started a new job as a Biology Librarian and will continue to do some data work in this role. So there will be future posts on data management tips and tricks! I’m looking forwarding to being back.

Posted in admin | Leave a comment

Data Management in Library Learning Analytics

by Kristin Briney Posted on 2019-02-25

My latest paper was published this week and I am so very excited to share it with you all. It is Data Management Practices in Academic Library Learning Analytics: A Critical Review.

Every article has a story behind it and this one, as happens with the best articles, started with me getting very annoyed. I had just been introduced to the concept of library learning analytics and was reading a pair studies for a different project. I couldn’t focus on the purpose of the studies because I kept running into concerns with how the researchers were handling the data. What annoyed me most was that one of the studies kept insisting that their data was anonymous when it clearly wasn’t, which has huge implications for data privacy. A little poking around made me realize that such data problems appear with terrible frequency in library learning analytics.

There’s quite a history of ethical debates around library learning analytics but almost no research on the data handling practices which impact patron privacy in very practical ways. After a little digging through the literature and a lot of shouting at my computer, I knew I had to write this paper.

So what did I find? Libraries: we need to do better. For all that we talk about patron privacy, there is sufficient evidence to show that we’re not backing up that intent with proper data protections. The best way to protect data is to collect limited amounts, de-identify it where possible, secure it properly, and keep it for a short time before deleting it. We’re not doing that. I’m also concerned about how we handle consent and opt-in/out, something I didn’t originally intend to study but couldn’t ignore once I started reading. There’s a lot more in the paper, including some explanations of why these are best practices, so I encourage you to go there for more details. And afterward go figure out how to protect your data better.

Finally, I need to again thank Abigail Goben and Dorothea Salo for acting as my sounding boards through this entire process. They listened to me rant, helped me worked out a path for this research, and edited drafts for me. I am deeply grateful for their assistance and I know this paper would not be half as good without their help.

Posted in dataManagement, libraries | Leave a comment

Taking a Break: Some Stories of Documentation

by Kristin Briney Posted on 2018-10-23

I’ve been thinking a lot about documentation this month as I prepare to take 12 weeks of leave away from my job. The upside is that I’ve had 9 months to plan for this, but I will also say that following good data management and reproducibility practices has greatly helped with the efforts to shift my duties temporarily to others.

In today’s post, I want to provide some snapshots on how I’m documenting tasks so that others can perform them in my absence. I’m hoping that these vignettes will provide inspiration for others aiming to provide enough documentation with their work, whether they are taking leave or not.

Story #1

My smoothest project to shift involved some R code I wrote over the summer to run automated reports for my library’s public services statistics. I knew going into the project that I would not be able to run the reports myself during the key period at the end of the semester, so I made sure to document everything at a level for a novice to pick up. In practice, this meant including a README.txt to walk someone through everything from installing the software to adjusting key variables to running the code. I also tried to make clear within the code, via commenting, which parts needed to be updated to customize the reports. Building code with the intention of it being used by others is really the best practice, and I can see the benefits of taking this approach that will help beyond my 12 weeks away.

Story #2

Another task I’m temporarily shifting is acting as Secretary for my professional association. Again, I’ve helped myself a lot here by having a good README.txt file laying out the structure and permissions for all of the files I manage as Secretary. So it was simply a matter of adding notes on my duties so that they could be adequately covered.

Story #3

A more involved project to shift is a research project I’m on for which I have an assistant. Key documentation here included a timeline for assistant onboarding tasks and a lot of communication with my collaborators. The timeline turned out to be a good idea, generally, as expectations are clear for everyone; this is likely a method I’ll use in the future. Otherwise, I’m trying to go for a more-communication-is-better approach, which requires extra work but will benefit everyone when I’m away.

These three vignettes show [what I hope are] successful efforts to document tasks for others. I think what makes them good is that I’ve built a lot of the documentation into the projects to begin with, making it easier for me to pass stuff on now. I admit that not every project I run is documented to the levels described here but my future self is usually more grateful than not for taking 10 minutes early on to write a README or send a status email.

I hope these stories provide you with some ideas for your own projects that may need to be passed along or picked up again by your future self. Even a little documentation created early in a project is helpful and usually doesn’t take a huge amount of time to create. The benefits to your collaborators and your future self usually make it all worthwhile.

Posted in documentation | Leave a comment

It Started with a Tweet…

by Kristin Briney Posted on 2018-09-10

My very first viral tweet, in fact. It turns out that many other people have strong feeling date formats.

I think that the benefits of the YYYY-MM-DD format are obvious, but I’m also the oddball who has the international date standard number memorized (it’s ISO 8601, by the way). These benefits are likely not obvious to everyone else, even to my data librarian peers who regularly deal with consistency in data formatting. Thus the lament in a reply to my tweet:

Yasmeen’s reply got me thinking about the small-but-significant number of times I’ve seen data librarian peers putting dates into non-YYYY-MM-DD formats. It also prompted me to go from occasionally grumping to myself about this issue to full-on ranting to others in the form of a commentary in one of my field’s journals.

All this is to say that you should check out my latest publication in the Journal of eScience Librarianship: “The Problem with Dates: Applying ISO 8601 to Research Data Management.” The redux is that I implore you to learn about the ISO 8601 standard and use it in your research data management. From experience I can say that it’s one of the best data tricks I know.

[PS. 10 points to anyone who catches the Harry Potter Easter egg in the article.]

Posted in admin, metadata | Leave a comment

SPEC Kit on Privacy and Library Learning Analytics

by Kristin Briney Posted on 2018-09-05

I mentioned in a post a few months ago that I started doing research in the area of learning analytics, libraries, privacy, and data management. I’m happy to report that my first publication to come out of this work went live today: an ARL SPEC Kit on Learning Analytics.

The SPEC Kit provides an overview of where large U.S. academic libraries currently stand with respect to learning analytics and privacy practices. If you’re at all interested in what libraries are doing with student data, I encourage you to check it out.

Also, many thanks to my co-authors Michael Perry, Abigail Goben, Andrew Asher, Kyle M. L. Jones, M. Brooke Robertshaw, and Dorothea Salo for their work on this publication. Expect to see more publications from this team in the future!

Posted in admin | Leave a comment