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07. Data

2022.01.18

Agenda

  1. Updates on goal progress
  2. Scheduling showcase
  3. Data Cleaning with OpenRefine

Meeting notes

Note-taker: Kari

Notes, Session 7

  • Install new application: OpenRefine
  • Updates
  1. Kari: databasing – how to make the process faster? Excel first, and only THEN integrate with Access; make a report and export to Excel, then reorder as needed and re-export to Access; Python might be a good option for making a new form, if not for this project then for the future
  2. Alexis: work for presentation – Henry Chapman Mercer’s Tiles of the New World; clickable objects that move to the relevant part of the text! Next step: overlapping images while retaining functionality
  3. Mallory: StoryMaps is turning out to be quite unwieldy – problem with computer? With image hosting? Would using a different browser help? Scalar (but Scalar doesn’t let you annotate deep zoom images, which might be a problem)?
  • Presentations
  1. Length: 10 minutes or so a person, plus Q&A
  2. Goal: to demonstrate what we’ve been learning this semester!
  3. Scheduling: 12:30/2, some Wednesday in February (preferably a VCC off-week)
  • Data
  1. Great to be able to deal with lots of data at once instead of piece-by-piece!
  2. Data can be thought of quantitatively, but can also be represented visually – anything that can, in one way or another, be stored and accessed as binary
  3. Important things to know how to do with data include: importing/exporting derivative datasets, sorting/filtering/faceting data, fixing errors and inconsistencies at scale, splitting columns with multiple values, introducing regular expressions
  4. Types of data
    • Tabular: basically, anything that can be represented with rows and columns (e.g., latitude and longitude) (only one value can be represented per row)
    • Graph: basically, data that can be represented by nodes and edges (multiple values can be represented per category) – websites are examples of directed graph data
    • Both human- and machine-readable types of data exist, and are not always the same
    • Tidy data: applying standard ways of optimally formatting data for use with the programming language R
  • OpenRefine
  1. Instead of documents, OpenRefine has projects
  2. To load something, go to “Create Project” and then “Choose Files”; the data will then be read and parsed, some settings may have to be changed; must be specified if items are to be identified as numbers, or else they will be identified as text – this must be done for each column - CSV = comma separated values - TSV = tab separated values - Sorting - Filtering - Text filters = good for identifying relevant entries quickly - Faceting - Transforming cells - Trimming whitespace - Clustering: can make other tasks, like trimming whitespace, more efficient; can be more or less effective depending on consistency of values
  • To-do before next session…
  1. Finish OpenRefine tutorial (continue along the same lines with visualization exercises during next session?)
  2. Logs

Business

  • Updates & announcements
  • Other business

Data

Action items

  • Add your updates by Monday morning
  • Other

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