Carter's Digital Garden

COGS 9 Lecture 8

Jan 26, 20242 min read

Source: https://drive.google.com/file/d/1efATrW7wpnBsTycfj29xohyOkkR0VFD1/view

Data Wrangling

Data Science

The scientific process of extracting value from data

Data Vocabulary/Fluency

Variables/Features

Variables are stored in columns on spreadsheets.

Observations

Observations are stored in rows on spreadsheets.

Tidy Data

  1. Each variable you measure should be in a single column.
  2. Every observation of a variable should be in a different row.
  3. There should be one table for each type of data.
  4. If you have multiple tables, they should include a column in each with the same column label that allows them to be joined.
    Tidy data `==` rectangular data

Benefits of Tidy Data

  1. Consistent data structure
  2. Foster tool development
  3. Require only a small set of tools to be learned
  4. Allow for datasets to be combined (much like in an RDB!)

Good Spreadsheets

Rules for Tidy Spreadsheets

  1. Be consistent
  2. Choose good names for things
  3. Write dates as YYYY-MM-DD (ISO-8601)
  4. No empty cells
  5. Put just one thing in a cell
  6. Don’t use font color or highlighting as data
  7. Save the data as plain text files (i.e. CSV)

Be consistent

  • Keep exactly the same variable names across spreadsheets.
  • Pick a way to code your variables and stick to it.

Choose good names for things

  • Avoid extra spaces
  • Use underscores not spaces
  • Choose meaningful names

Write dates as YYYY-MM-DD

Use ‘ISO 8601’ standard

No empty cells

Put just one thing in a cell

Don't use font color or highlighting as data

Common Problems with Messy Data Sets

  1. Column headers are values but should be variable names
  2. A single column has multiple variables
  3. Variables have been entered in both rows and columns
  4. Multiple “types” of data are in the same spreadsheet
  5. A single observation is stored across multiple spreadsheets

Tidy data is also relevant for unstructured data

Common data wrangling tasks (and verbs)

  • subset dataset
    • filter: filter rows
    • select: select columns
  • change order
    • arrange: reorder rows
    • reorder: reorder columns
  • add a column: mutate
  • summarize data:
    • group by: group by other variables
    • summarize: reduce multiple values down to a single value

Graph View

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