Class 9

Elisabeth Gawthrop

Today

  • Check-ins: name, how you’re doing, a win for this week

  • Guest lecture: Gerry Lanosga

Break

  • Announcements

  • Quiz

  • Go through quiz answers and discuss

  • NICAR presentations

Break

  • A few data tips + Tidy Tuesday explanation

  • Homework review / time to work on it

  • Check out

Announcements

  • Look ahead:

    • Coding notebook due Saturday

    • Over spring break: do one interview, will have an extra credit coding exercise

    • After spring break: Get into your analysis for stories, then data viz

  • Me:

    • Office hours this week: Today as normal + Thursday 5-6PM

    • I will be giving feedback on your masterfiles/story homework and catching up on entering extra credit and other grades with the goal of providing a mid-semester evaluation.

Class 9 Quiz

  1. Which two states were the focus of “Dangerous Heat, Unequal Consequences”? (2 pts)

  2. What are some demographic features of the neighborhoods/ZIP codes where heat illness rates are higher? (2 pts)

  3. What are some environmental features of the neighborhoods/ZIP codes where heat illness rates are higher? (2 pts)

  4. What do you use View() or glimpse() to do after you read in a dataset? Beyond just “look at the data” - what are you looking for? (3 pts)

  5. What does count() do? (1 pt)

  1. counts how many observations/rows there are
  2. counts how many columns there are
  3. counts how many total values there (rows x observations)
  4. counts the frequency of how many times each unique value appears in a specific column

NICAR presentations

I’ll go first… https://utdata.github.io/jedr-academy/trials/

Break

Data things

Story analysis

In groups, reverse engineer the story you read this week.

  • What purpose does the lede serve?

  • What’s the nutgraf?

  • There are various data analyses used in the story and outlined in the methods. Which one(s) drive the story? Which are nice to haves?

  • Anything else you gleaned from reading this about data-drive storytelling?

  • Anything you think didn’t work? (it’s okay - I can take it!)

Review and discussion

  • Homework review

Tasks before next class

  • Coding assignment due Saturday (on Canvas).
  • Do one interview for your story: Please record the conversation if your source will allow it. Then fill in the relevant information using a row in the “Interviews” section. For the “Notes” column, write 1-2 short paragraphs summarizing the conversation and the most important information gleaned from it. Due at beginning at next class (on Canvas/in Masterfile).
  • Extra credit: 30 pts for a code notebook to be posted, due at beginning of next class (on Canvas).

Checkout

  1. Do you feel behind, on-track, or ahead?
  2. About how long did the reading take you?
  3. About how long did the coding notebook take you?
  4. About how long did story-based assignment take you (including research time)?
  5. Any other feedback, notes on how you’re feeling, etc?

Decoding Climate Change: Unlocking the Power of Programming for Data Journalism