Meet the data (and Git)

Research Skills — Week 1 Application

Welcome back

• Load PollEverywhere and ensure that the Q&A activity is live.
• [16:05–16:10] Recap the biomass question from the content session. “Remember: we left with an open question — does biomass help us reach net zero? Today you get the data.”
• Reassure: if the pre-work felt shaky, this session is your safety net.

Today’s goals

By the end of this session, you will have:

1. Loaded real UK energy data in (Web)R
2. Made your first plots of the biomass story
3. Summarized data, in context
4. Committed your first .R file to GitHub

Questions?

Submit questions:

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 biomass question

“Does UK biomass power help reach net zero, and should we keep paying for it?”

This week: explore the data. Get a feel for the numbers.

Next week: make proper figures and start making the case.

• Don’t try to answer the question today. Today is about becoming comfortable with the data and with R.

First steps in WebR

🎓💻 Guided walkthrough

w01-ppdac-ring.R

Open the course page

🖥️ Navigate to the Week 1 exercise page

• [16:10–16:15] Get everyone to the page. Help anyone who can’t load it. Common issues: slow first load (WebR is downloading R packages), browser compatibility (Chrome/Edge best).

Loading the data

• What are the columns?
• What years does it cover?
• What units are used?

• [16:15–16:35] Live walkthrough — type this, don’t paste it. The students have the same file open on the exercise page; tell them to see whether they can beat you to it, and start typing after a minute so the slower ones can follow.

• Anticipated code:

  elec <- read.csv("data/uk_electricity.csv")
  head(elec)

• Then show str() and nrow() on the same cell. Narrate: “This is like pd.read_csv() in Python. head() is like df.head().”

• Point out: columns are fuel types, values are TWh.

• If the cell errors on the path, the page is being served from file:// — it needs HTTP.

A first plot

“What’s the first thing you notice?”

• total gen > sum(sources) because of unlisted “other”

• Again, type it. Put the bare plot() up first, with no labels, and ask the room what is wrong with it before adding them.

• Total generation, not bioenergy, so the students still have Exercise 2 to do themselves; the same plot() pattern carries over.

• Anticipated code:

  plot(elec$year, elec$total_twh,
       xlab = "Year", ylab = "Total generation (TWh)",
       main = "UK electricity generation")

• elec comes from the previous slide’s cell — run that one first, or re-read the CSV here.

• Total generation peaked at 398 TWh in 2005 and fell to 294 TWh in 2025, down 26%. Most students expect it to have grown.

• Ask: “How does this compare to what you expected?” Then: “why might it have fallen?” (efficiency, less heavy industry, more imports).

• Plant the seed for Exercise 5: any fuel’s share rises when the total falls, even if the fuel itself stands still. “Is that a big number?” comes next week.

• Drax converted its first unit to biomass in 2013 and stopped burning coal in 2021; the UK’s last coal plant (Ratcliffe) closed in September 2024, so 2024 still shows 2 TWh of coal; DUKES 2026 leaves 2025 blank, which the course file records as 0.

Independent exercises

✏️💻 Exercises 2–5

Work through the exercises

The WebR page has scaffolded exercises with hints.

Aim for at least exercises 1–4:

1. ✅ Inspect the dataset
2. Plot bioenergy generation over time
3. Add coal to the same plot — what do you see?
4. Calculate mean and SD of bioenergy for the last 5 years
5. What fraction of UK electricity was bioenergy in the most recent year?

Then the quick check: how many times more bioenergy in 2025 than 2000?

Finished early? Try the extension exercises.

• [16:35–16:55] Students work independently. Circulate with demonstrators. Common issues: $ syntax, forgetting quotes around column names, confusion about subsetting.
• Don’t rush through this. Students need hands-on time.

Git

📋🎓💻 Setting up your repo

Why Git?

Your code needs to be:

• Saved — not just in the browser
• Versioned — so you can go back if something breaks
• Visible — so you can share your progress

Git does all three. GitHub Desktop makes it painless.

• [16:55–17:00] Brief motivation. Don’t oversell Git — just frame it as “how professionals save their work.”

The workflow

Every session:

1. Open GitHub Desktop
2. Pull any changes
3. Do your work in .R files
4. Commit with a message
5. Push

That’s it.

Your commit history becomes your learning record.

Committing loads the van; pushing sends it to the depot.

• Emphasize simplicity. Resist the urge to explain branches, merges, or anything advanced. Week 5 will cover collaboration.+

• Committing is like loading your changes onto a removals van on your driveway.

• Pushing sends the van to the depot – your changes are safe

• Pulling delivers any changes from the depot to wherever you are working.

Live demo

🖥️ Switching to GitHub Desktop

1. Accept the assignment: biomass
2. Clone your personal repo in GitHub Desktop
3. Open the folder — see the starter files
4. Create week1.R — paste your WebR code
5. In GitHub Desktop: see the change → commit → push

• [17:00–17:10] Do this live on screen. Go slowly. Narrate every click.
• Show a commit message that says what you found, e.g. “Plot bioenergy vs coal: they cross in 2017” — not just “week 1” or “stuff.”
• Show GitHub.com afterwards: “See? Your code is now on the internet. Your demonstrator can see it. You can access it from any computer.”

Your turn

✏️📋 Clone, commit, push

Do it yourself

1. Clone your personal repo via GitHub Desktop
2. Create week1.R — paste your best code from WebR
3. Commit with a descriptive message
4. Push to GitHub

Raise your hand when you’ve pushed successfully.

• [17:10–17:25] This will take longer than expected. Git setup always does. Common issues: authentication (sign in via browser), repo not found (wrong Classroom 50 link), “nothing to push” (forgot to save file).
• Demonstrators prioritize students who are stuck. Students who finish early go back to WebR exercises.

Attendance

Show the QR from https://attendance.durham.ac.uk/mytimetable, then ← back to the task.

Discussion

💬 What did you find?

What did the data show?

“What did you find in the data?”

• Bioenergy grew about ninefold, then plateaued
• Coal has collapsed
• Bioenergy is now about 14% of UK electricity

• [17:25–17:40] Pull the room together. Collect observations.
• Ask: “If biomass is counted as zero-carbon, what happens to the UK’s official emissions figures?” Let students reason through this.
• Don’t give the answer. They’ll dig into it in Week 2.
• Collect the observations before advancing — the next slide is the chart they were asked to build, and showing it early ends the discussion.

Coal out, bioenergy in

w01-coal-and-bioenergy.R

• This is the chart from Exercise 3, drawn the same way: plot(), then lines(), then legend(), bioenergy solid and coal dashed. Say so — students should recognize their own code, not a lecturer’s better version of it.
• The headline has a silent “some”: coal lost about 120 TWh, bioenergy gained about 37, and gas and wind filled the rest.
• The crossover year is computed from the data by the script, not read off the chart by eye.

One more question

“If biomass is counted as zero-carbon, what happens to the UK’s official emissions figures?”

We’ll dig into this next week.

• Let this hang. Curiosity is the goal. Some students will already sense the tension — biomass has replaced coal, but if its emissions are counted as zero, the books look better than reality.

Wrap-up

Any questions we missed?

Submit questions:

PollEv.com/geol

text geol to 07480 781235

• Surface any questions that have come in since the early Questions slide, especially ones raised during today’s content.
• Once you’ve finished with this slide, archive (reset) the activity at the end of the session so questions don’t carry over.

Stuck? Ask the class

The class discussion board: the “Ask for help” link or https://github.com/orgs/geol-skills/discussions

• Private to the class and staff
• Your question is someone else’s question too
• Include what you tried, your code, and the full error
• Solved? Mark with a ✅ so others can see what helped

• Private - so you’ll need to be logged into GitHub first.
• Model starting a discussion, on screen: New discussion → General → title Introduce yourself → body “What are you looking forward to learning this term? What are you nervous about?” → Start discussion. Answer it yourself first.

Now: reply to Introduce yourself

• Everyone replies before leaving. (A first post that costs nothing makes the first “I’m stuck” post easier.)
• Can’t see the board? They haven’t accepted the Classroom 50 invitation yet.

Before you leave

• Your week1.R is committed and pushed
• You can see it on GitHub.com
• You’ve replied to Introduce yourself

Next time: “What do the numbers say?”

You’ll learn ggplot2 and start asking: “Is that a big number?”

• Check the Q&A — surface any top-voted questions before finishing.
• Ensure every student has committed. This is the non-negotiable outcome for today.
• Students who haven’t finished the WebR exercises can continue at home — their code persists in the browser via localStorage.