Analysing Data: Welcome and Escape Room

Overview

Introductions

Welcome to Analysing Data! We’re excited to get started.

Your module leads this term are Drs Jennifer Mankin and Milan Valasek. You can find their Contact Information on Canvas.

We also have a great team of Doctoral TutRs to teach and support you this term: Alaa, Alberto, Ali, Arianna, Carina, Dan, Giulia, Helena, and Jenny.

Rules for Zoom Practicals

In the main room, stay muted while the teaching leads are demonstrating, and use chat or “Raise Hand” button to ask questions. In breakout rooms: Unmuted, participate!

As for video, it’s your choice on or off, but we recommend on in breakout rooms - better for interaction!

If you need to step away or leave early, there’s no need to ask, but let us know if something’s wrong. Arrange with your teammates to catch up, and see [Timetabling and Absences on Canvas](https://canvas.sussex.ac.uk/courses/12727/pages/timetabling-and-absences.

Whenever you’re not sure: Ask! For! Help!

Teamwork Makes the Dream Work

Every week you will work with the same team. This has several benefits:

Pre-made study group
Share responsibilities and skills
Brainstorm and problem-solve together
Backup if technology fails

You will not have any marked group assessments!

If you miss a practical, get in touch with your team to catch up - see the team allocation spreadsheet on Canvas

Module Outline

Every week we will have a lecture beginning the new topic, a tutorial and quiz, optional seminars (StatsChats, focused on stats, and seminRs, focused on R), and a practical.

Details:

Communication

Module updates and other information always broadcast via Canvas Announcements
- Check your email regularly!
- We assume you have read these
Discussion and questions via Piazza on Canvas
Contact Information on Canvas for more

Practicals

Mix of guided demonstrations and unguided practice in teams
Focus on collaborative tasks, teamwork, and hands-on experience
Lots of time to try things and ask questions
Solutions to worksheets updated following week Friday

Expectations

Before your practical each week:
- Attend or review the lecture
- Complete the tutorial and attempt the quiz
- Get help or learn more in StatsChats and seminRs
In the practical:
- Attend ready to work
- Do your best and ask for help when you need it
After the practical:
- Finish any remaining tasks
- Check the solutions
- Get help or ask questions before the next practical

Hex Awards

We recognise outstanding achievement or contributions to the module with hex awards

If someone goes out of their way to help you, nominate them for the SavioR award.

pRactice makes pRfect

Learning R requires consistent practice, and just like with any language, small, regular increments are better than cramming. Even spending 10-15 minutes a day practicing R will make a huge difference.

Similarly, it’s important to get help early and often. If you get stuck, things can quickly pile up. You have a lot of options for help: from your team, in drop-ins, optional seminars, or practicals, and on Piazza.

It’s important to start this adventure together with clear goals and expectations in mind. We’ve laid out our expectations above and given you some resources to help - but what are you trying to achieve? Have a think about the following, and write down your answers:

What are the most important skills, qualities, or characteristics you need to be “good at R”?
What works well for you learning R? What do you enjoy?
What do you want to get out of this module? What do you want to achieve?
What questions or concerns do you have about learning R, or this module?

I also asked people who use R regularly, on the #rstats hashtag on Twitter, the same question. Here are their responses:

knitr::include_url("https://embed.polleverywhere.com/discourses/h6cJVZe5vRhMKe0Izw5hE?controls=none&short_poll=true", height="1000px")

Statistics and Growth Mindset Study

If you want to help us learn more about this, consider participating in our study!

Research exploring anxiety, growth mindset, engagement, and achievement
- Recruiting the 1st and 2nd year Psychology UG cohort
- Results used to make teaching and assessments better
NOT REQUIRED for this module - participation is completely voluntary
- But greatly appreciated!

Participate in the study here

R and RStudio Setup

If you have not done so yet (same instructions as Tutorial 01):

Create a folder for this module called “02_and” or “02_analysing_data”
In RStudio, create a “week_01” R project
- Underscores instead of spaces and 01 instead of 1!
- How to create a project, from PAAS
Open the project and create the standard folder structure inside:
- “data”
- “r_docs”
- “scripts”

RMarkdown Revision

RMarkdown is a document that can include both regular text and code
- It can make nicely formatted documents and reports
- It can also do lots of other nifty things (like make these slides, and our website!)
For your lab report assessment, you must submit an RMarkdown file that:
- Contains all of your data analysis, plots, tables, and report text
- Knits successfully, without errors
- Produces a nicely formatted report
- Has no raw code or data appearing in the knitted file

RMarkdown Revision: Escape!

Today we will practice debugging code. When you start, you will see lots of errors.

Don’t panic!

Use the error messages to figure out where the problem is, and use PAAS materials, Tutorial 01, Google, or whatever else to fix the problem!

Download the .Rmd file linked here

Save it in and > week_01 > r_docs and open in RStudio
Note: When you knit successfully, you will hear some short sounds
- Be careful of your headphone volume!
Once you’re done, you can “escape” this practical as well :)

Welcome and Escape Room