Overview

• Module Information

• Important Updates

  • RStudio Cloud

  • Quarto

  • The Battle of the Pipes: Magrittr %>% vs Native |>

• ExtRas

  • ChatRday, ChallengRs, Hex Awards, LeadeRboard

• KahootR

Welcome to Discovering Statistics!

• In this module we will build on the foundational skills in R and critical and statistical thinking from first year

• We'll be by diving into the many permutations of the linear model first introduced in Analysing Data last term

• At the end of this module, you will have a diverse toolkit of ideas, techniques, and analyses for your third year projects!

Module Information

Module Leads

• Dr Danielle Evans

  • Practicals, skills labs, coursework

• Dr Jennifer Mankin

  • Practicals, skills labs

• Professor Andy Field

  • Lectures, tutorials, exam

• Join the Discord server to ask us questions about the content!

Teaching Sessions

• Practicals

  • Led by Dr Danielle Evans and Dr Jennifer Mankin along with a team of doctoral tutors

  • These sessions alternate each week between online and in person

• Lectures

  • Delivered in person by Professor Andy Field

  • Usually one lecture per week but for the first two weeks of term only there are two lectures per week - check your timetable on Sussex Direct to make sure you don't miss them!

• Skills Labs

  • Delivered in person by Dr Danielle Evans and Dr Jennifer Mankin

Assessments

• Look on Canvas for more information about assessments!

• Practical Worksheets every week - 20%

• Take Away Paper (TAP) in Week 7 - 25%

• Research Participation (SONA) - 5%

• Exam - 50%

• You will also use the analysis and interpretation from the TAP assessment to complete your Developmental Psychology lab report due in Week 10

Important Updates

• There have been a number of updates and developments in the RStudio world and in the methods modules over the summer:

  • RStudio Cloud

  • Quarto

  • Native Pipe |>

• We'll be implementing some of these changes in your modules

• And you might start seeing them in online materials/forums so we're briefly introducing them now too!

RStudio Cloud

• Traditionally, we've taught R using the desktop version of RStudio

• New for this year, we're going to be using the RStudio Cloud which should make learning R a little easier

• Everything is setup on the Cloud for you - if you haven't had your practical yet do make sure to join the Discovering Statistics Workspace beforehand using the link in the sign up email

• We only just got access to it at the weekend so stay tuned for some tutorial videos coming soon!

Quarto

• The next new thing in the RStudio World is Quarto!

• is the next generation of R Markdown!

• Recognisable by the .qmd file extension as opposed to the .Rmd file extension

• Almost identical to .Rmd documents in the way they look and work

• But with the plus of an integrated visual markdown editor 🥳 which should make using R easier

• We have switched to Quarto for all materials in practicals

Quarto

• You'll encounter Quarto in your practicals this week if you haven't done so already

• It looks very similar to RMarkdown, except you'll have the choice of using the Source pane (like what we've used previously) or the new Visual editor mode

• The visual editor is more similar to MS Word-like software compared to the old source mode:

Magrittr %>% vs Native |>

• Previously, we've used the magrittr pipe %>% to chain commands together

  • But the magrittr pipe requires magrittr or tidyverse to be loaded

• We now have a native pipe |> which works in a similar way, but without needing those packages

data %>% 
  group_by(gender) %>% 
  summarise(mean = mean(age))

data |>
  group_by(gender) |>
  summarise(mean = mean(age))

• We are NOT switching to the new native pipe on this module but we will show you examples of both

• This is because you might start to see the new native pipe in other R documentation, materials & forums

ChatRday

• Join 'steminists' and methods module leads Dr Danielle Evans, Dr Jennifer Mankin & Jenny Terry for an informal, open Zoom chat about R

• We will invite and answer questions, debunk misunderstandings, enthuse about cool stuff in R, and announce that week's challengR

• We'll hold a session every Thursday at 11:00 UK time

• The sessions are mostly unstructured:

  • but we'll venture into the Upside Down and we'll show you how you can find your own Vecna song in R using the spotifyR package

  • we'll play around with some data from RuPaul's Drag Race

  • we'll have a go at creating the uggliest plots you can imagine

  • and much moRe!

ChatRday ChallengRs

• As self-proclaimed neRds, we love nothing more than creating cool stuff in R and telling anybody who will listen all about it

• This process of problem-solving and having a go at creating something really helps to develop R skills, plus it's something fun to do in R... 😁

• So every other week in ChatRday, we'll set a 'ChallengR' of something cool to create where you can earn Hex stickers for your efforts, and we'll vote on the best submissions!

• So come along to ChatRday to participate!

Hex Stickers

• Learning R is hard work, and working hard deserves the best kind of reward: Hex Stickers!!

• Hexagonal ("hex") stickers have been a feature of the RStats community for years

  • R enthusiasts give out or get stickers at meetings or conferences, and use them to display their pRide

• So, for every Research Methods module you pass (PAAS, AnD, DS, & QQM), you can collect a Hex sticker:

Hex Awards

• Hex Awards are special hex stickers that recognise particular achievements or contributions to this module

• There are two weekly awards:

Keen Bean

• This award will go to students who are proactive in their learning by asking questions, actively engaging in practicals and showing enthusiasm, dedication, and curiosity

SavioR

• This award will go to students who go out of their way to help other students learn and understand

• You can nominate anyone who has helped you debug your code or understand a new concept, supported or encouraged you, or any other acts of kindness on this module

Hex Awards

• There are two awards at the end of term:

High FlyR

• This award will go students who get top marks for their final overall mark on Discovering Statistics

ClimbR

• This award will go to students who have the biggest improvement between their final overall mark on Analysing Data and their final overall mark on Discovering Statistics

LeadRboard

• In every Skills Lab, you'll have the opportunity to earn points by participating in Kahoot quizzes

• At the end of term, the students with the highest score will receive a pRize for all their hard work!

  • You just need to make sure to use the same username each week so that your points are counted

  • You don't have to use your real name, but choose something memorable and unique to you & write it down!

  • You must submit your username on Canvas so that we can verify winnRs at the end of term, so please do this now if you haven't done so already!

LeadRboard

• After the first Kahoot, you'll be able to view your score on the leadRboard on Canvas

Getting Help

• This module focuses on developing R and Statistics skills

  • These are subjects that need a lot of practice!!

• But we are here to help you:

  • Ask questions in practicals, after lectures, at the R help desk

  • Post on Discord

  • Problem-solve with friends and classmates

  • Contact the student mentors

• There's no such thing as a stupid question when it comes to R/stats!!

  • A stupid question is one that isn't ever asked!

• It's best to seek help as early as possible so we can support you

• The worst thing you can do is avoid R entirely and disappear!!

It's never too late to staRt!

When I began at Sussex, I didn’t realise that R was a part of my course. As a lazy student, I didn’t even read my prospectus before joining, so it was a complete (unpleasant) surprise that I was studying statistics and coding. I was absolutely terrified to learn that I now had to learn coding and statistics.

I would often put it off, or leave early in the first year, which lead to me getting 42% in my module. I just couldn't begin to understand this seemingly foreign language of R. As my final year approached, I began prioritising my time around learning R and stumbled into the mountainous task of completing three different methods of statistical analyses for my dissertation.

I had never even tried to learn how to do any of those analyses before. I was unbelievably stressed. With Dr Danielle's guidance, I achieved a 68 in my dissertation. Fast forward two months later, and I now work as a Data Analyst in the R Programming Language. I love my job.

As above, I was never confident in my mathematical or technical abilities. I spent a long period of my academic career putting off learning how to code. If I can get this far, you all can too. It takes time, be patient and kind to yourself.

UoS Former Student

KahootR

• This is your last chance to submit your Kahoot username before we begin!