Making the biomass case

Research Methods — Week 2 Application

Today’s plan

Goals

Produce briefing-quality figures of the biomass data.

By the end of this session:

• 2–3 committed figures in your repo
• The code that produced them
• A sense of the story the data tells

• [5 min] Setup: open WebR, open GitHub Desktop, pull latest.
• Today is about producing the figures for the Phase 1 briefing. Less new content, more hands-on making.

Questions?

Submit questions anonymously:

PollEv.com/geol

text geol to 07480 781235

• Open the Poll Everywhere Q&A activity before the session and leave it active throughout.
• Questions appear in your Poll Everywhere presenter view; upvoted questions rise to the top.
• Address during transitions or at the end of concept blocks, not mid-explanation.
• Reset (archive) the activity between sessions so questions don’t carry over from the previous week.

The figures you’re building

Figure	What it shows
Trend plot	Biomass generation over time
Comparison plot	CO₂ per MWh: biomass vs coal vs gas
Import plot	Where do the wood pellets come from?
Context plot	Biomass as % of total UK electricity

• These correspond to the four main exercises. Not every student will finish all four — that’s fine. One good figure is better than four rushed ones.

Guided walkthrough

🎓💻 Building a biomass figure

Live demo

🖥️ Building a multi-fuel line chart in WebR

• [20 min] Build one complete ggplot on screen. Suggested plot: UK electricity by fuel type over time (line chart, colour by fuel).
• Walk through: load data → reshape if needed (pivot_longer) → ggplot → add labels → add caption. Narrate each step.
• Emphasise: “This is the kind of figure that goes in a policy briefing. Notice the caption tells the reader what to see.”

The anatomy of a good figure

ggplot(long_data, aes(x = year, y = twh, colour = fuel)) +
  geom_line() +
  labs(
    x = "Year",
    y = "Generation (TWh)",
    colour = "Fuel type",
    caption = "Source: DUKES 2025 Table 5.6B"
  )

Every figure should have a “so what” — what should the reader take from it?

• Show the completed plot. Ask: “What’s the story here?” Students should see biomass rising as coal falls.

Your figures

✏️💻 Independent exercises

Work through the exercises

The WebR page has four main exercises with hints:

1. Trend plot — biomass over time, with context
2. Comparison plot — emissions per MWh by fuel
3. Import plot — pellet origins over time
4. Context plot — biomass as % of total

Extension: Can you make biomass look more important — or less important — than it really is?

• [50 min] The bulk of the session. Circulate actively.
• Common issues: wrong column names, wide vs long format confusion, forgetting to load ggplot2, aesthetic mapping errors.
• The extension is a preview of Week 4. Don’t belabour it, but students who try it will be ahead of the game.

Discussion

💬 What story do your figures tell?

Show and tell

“What story do your figures tell?”

2–3 volunteers: share a figure. Class discusses:

• What does this figure show clearly?
• What’s missing?
• How do your choices (axis ranges, colours, what’s included) affect the impression?

• [15 min] This is important: first time students see each other’s choices and recognise that the same data can produce different impressions. Primes Week 4.
• Highlight specific choices: “You started the y-axis at 0 — good. What would happen if you started at 30?” “You showed all fuels — what if you only showed biomass and coal?”

Wrap-up

Commit your work

Save your code to week2.R. Commit and push.

Your repo now has two weeks of work:

• week1.R — exploratory code
• week2.R — figures

Next week: “How confident should we be?”

We’ll ask whether the biomass numbers really add up.

• Ensure everyone commits. Students who haven’t finished can continue at home — localStorage preserves their WebR code.