Before We Begin...
You have 90 seconds to tell someone around you, or write down, all the R functions you can remember!
01:30
This workshop aims to help you get stuck in to your analyses by serving as a reminder of the huge range of analytical skills you have developed throughout your degree, giving you a few tools of how you can put them into practice, and where you can find additional resources
so a lot of you might be feeling really stuck right now, and a bit lost maybe with ur analyses and what u need to do in R, might be feeling a bit anxious as well and thats okay, but i am here to tell u that you really dont need to be, because u know what ur doing, you might just need to be reminded that you do, and need a lil refresher of all the stuff that is in ur brain, so, as a lil game to test it out, before we begin, i would like u to have a go at either wrriting down all the R functions u can remember, or telling the person next to u or in front of u
Overview
Introduction
Posit Cloud RefreshR
Data Management
From Analysis Plan to Analysis Pipeline:
- Explore, Prep, Visualise, Analyse & Present
Getting Help
Finding ResouRces
R Help Desk
Overview
Introduction
Posit Cloud RefreshR
Data Management
From Analysis Plan to Analysis Pipeline:
- Explore, Prep, Visualise, Analyse & Present
Getting Help
Finding ResouRces
R Help Desk
Keep in Mind! Alllll of you will be at completely different stages with your data collection, prep, & analysis & that's OK - don't compare yourself to others!!
What You Need:
Agree to the terms and conditions of the service using this quiz
Join the dissertation workspace on Posit Cloud
Open this project
Open the analysis_demo.qmd if you want to follow along
Open the analysis_pipeline.qmd to make notes on the session today!
Introduction
Conducting your analyses can often feel very overwhelming
Trying to figure out exactly where to start can make you feel like you have no idea what you're doing
But I'm here to tell you that you do know!
Starting on your analyses can feel scary but it's the most exciting part!! You can finally uncover the answers to your research questions!
Analysing your data allows you to gain knowledge about phenomena that no one else in the world knows yet - and then you get to tell everybody about it in your dissertation...
Spilling The Tea
Valentine's Day is coming up 🙄... romanticise your results instead!! 😍
No one else in the world knows what you've found, your dissertation is your chance to share it
But first before we can share our findings with the world, we need to analyse our data and interpret our results to know what we've actually found!
Whatever your findings are, they are important and worth sharing...
Spilling The Tea
Valentine's Day is coming up 🙄... romanticise your results instead!! 😍
No one else in the world knows what you've found, your dissertation is your chance to share it
But first before we can share our findings with the world, we need to analyse our data and interpret our results to know what we've actually found!
Whatever your findings are, they are important and worth sharing...
Top Tip! Non-significant results are JUST as interesting as significant results (if not more so), don't fall into the trap of thinking non-significant == bad!
Posit Cloud RefreshR
The school has invested in a license for Posit Cloud, which is an instance of RStudio that you can access via any web browser
- This saves you needing to install R or RStudio on your machine, you can log into the cloud and RStudio and R will have been set up for you
Any project you create in the cloud will automatically have the packages from the
discovrtutorials pre-installedYou should use the Posit Cloud to conduct your dissertation analyses
To access this resource you need to:
If you're returning from a placement year and wasn't taught with the Cloud, you can sign up for a free account here (we recommend you use your uni email), before doing the above steps
Posit Cloud RefreshR
- Once you have followed the previous link, you will see a workspace called Dissertations 2024-25
Any projects you create within this workspace are part of our organizational account (that is, they have unlimited resources)
This workspace will be archived on 15th September 2025 so please make sure to download a copy of your project before then if you want to keep it
Using the Cloud ☁️
Before we can do anything with our data, we need to upload it to the cloud!
First we need to create a new project
Then upload our data
We then need to create a new document (Qmd, Rmd, R script, whatever you prefer)
& finally read our data into that file...
Using the Cloud ☁️
Before we can do anything with our data, we need to upload it to the cloud!
First we need to create a new project
Then upload our data
We then need to create a new document (Qmd, Rmd, R script, whatever you prefer)
& finally read our data into that file...
Woah! You might have multiple datasets that you need to merge! We haven't taught you this, but you can get help from me to do it!
Using the Cloud ☁️
Before we can do anything with our data, we need to upload it to the cloud!
First we need to create a new project
Then upload our data
We then need to create a new document (Qmd, Rmd, R script, whatever you prefer)
& finally read our data into that file...
Demo! Let's go through a quick refreshR of how to set up a new project on the Cloud! (You don't need to copy, just watch!)
as a reminder, here's how we do that process
demo new project on the cloud and opening new file
switch to half completed version
some considerations: data type, structure (wide vs long), multiple data files that need to be merged etc
Data Management 👀
You should store any research data collected during your project on a secure University system: i.e., OneDrive or Box
It is good practice to destroy participants personal data (e.g., consent forms, names) at the end of the project unless you have sought their explicit permission to retain their details for other purposes (such as contacting them for future research projects)
Data without personal identifying information may be stored up to 10 years after the completion of the project: this is the University default but check with your supervisor how long they would like you to store the research data for
When destroying research data, you'll need to delete it from the Cloud (all dissertation projects will additionally be archived on 15th September 2025), but you'll also need to delete any local copies of the data too
The Analysis Pipeline
Now we've read in our data, & stored it appropriately, we can analyse it! 🥳
The analytical process requires multiple steps
I couldn't think of a good acronym for it... 😓 so we have EPVAP:
Explore
Prep
Visualise
Analyse
Present
Watch Out! Your analysis pipeline might not follow this exact order - it might be more of a back and forth process!
we'll go through each of these analytical components and we'll have a brief refreshr on some code!
For Today:
We're going to talk about the purpose of each step in the analysis pipeline
& we're going to remind ourselves what functions might be useful to complete each step of the analysis pipeline
Open this assignment on the Cloud to access the analysis_demo.qmd file I'm going to demo with
This Cloud project also contains an analysis_pipeline.qmd that I encourage you to take notes in as we go through each topic!
A Word doc version is available on Canvas too if you'd prefer that
You can come back to these files later on when you do your analysis!
You might also find it helpful to have your analysis plan to refer to for today's session
Explore
- Once we've successfully imported our data, data exploration is the first step in data analysis!
Explore - Why?
To gain a deeper understanding of the dataset
To view what variables exist in the data
To understand the characteristics of the data
To examine any missing data or anomalies
To check over the structure of the data to see what needs wrangling
Explore - How?
The following functions are useful for data exploration:
View() & print()
names()
nrow()
ncol()
summary()
table()
str()
class()
levels()
Prep
- Data prep is usually the longest part in data analysis!
Prep - Why?
Transforming data from one form into another to make it appropriate for further analysis
Organising data to make it easy to work with
Cleaning data to fix any errors or issues
Restructuring data for a specific analysis
Dealing with missing data
Prep - How?
There are countless functions for data prep:
select(), mutate(), filter() & slice()
rename()
recode() & replace()
as.numeric() & as_factor()
mean() & sum()
rowwise() & c_across()
pivot_wider() & pivot_longer() (I can help you with these!)
Top Tip! Write #comments as you go to keep track of what you're doing at each step!
Visualise
- Data viz can take place at multiple times during data analysis!
Visualise - Why?
To look at trends in our data
To examine relationships between variables
To check model assumptions
To formally present our findings
Visualise - How?
The ggplot2 package is our go to for data viz:
- geom_boxplot()
- stat_summary()
- geom_violin()
- geom_point()
- geom_smooth()
But we can also use other functions for interactions & for data exploration/model checks:
- afex_plot()
- ggscatmat()
- plot()
- autoplot()
Top Tip! Draw out your graph by hand before you attempt it in R - you'll have a clearer idea of what you're trying to achieve with code!
Analyse
- Data analysis is considered the main part, but there's so much more to it!
Analyse - Why?
To answer our research questions
To test our hypotheses
To look at differences between groups
To look at relationships between variables
To look at the structure of a construct
Analyse - How?
SO many functions for data analysis:
lm(), lmRob(), glm(), model_parameters() & aov_4()
t.test(), correlation() & cor.test()
tidy(), glance() & summary()
joint_tests(), contrasts() & estimate_contrasts()
anova() & parameters()
sem(), fa.parallel(), fa(), omega() & alpha()
cohens_d(), hedges_g(), glass_delta(), omega_squared() & eta_squared()
Woah! The Model SelectR can help you decide which analysis is appropriate if you're stuck!
Present
- Presenting data clearly is super important!
Present - Why?
To formally present the results of our analyses
To summarise large chunks of information
To make it easy for our reader to understand our findings
Present - How?
As with everything in R, there's lots of ways we can present data:
summarise() & group_by()
n(), sd(), mean(), min(), max() & mean_cl_normal()
rempsyc::nice_table() & apa_table()
kable(), kable_styling() & round()
write_csv()
ggsave()
Top Tip! You don't have to create pretty APA style tables in R - you can recreate them in whatever software you're using to write your dissertation!
Finding ResouRces & Getting Help
Your supervisor is the go-to person for ALL statistical, analytical, and code questions
If they are unable to help with code specifically you can come to the R Help Desk!
Don't forget you have access to all the teaching materials we've used throughout your degree [Canvas > Modules > All Modules > Past Enrolments]
- The DissRtation HelpR & the R-Dex can help you navigate all those materials & where to find different topics!
You can also find help online (Google, Stack Overflow, R Cheatsheets, package help documentation etc) but be cautious copying code you don't understand!!
Be Aware! If your dissertation analysis is something we haven't taught you, your supervisor must teach it to you!
Keep in mind...
Keep in mind...
You can do this!!!!
just getting started is the hardest part
its totally normal to feel anxious and to procrastinate
we all do it, but if you leave it to the last minute you will regret it
allow yourself time to do it
just gotta take that first step and the rest will flow
so believe in yourselves like i do and take that first step!
Before We Finish...
You have another 90 seconds to tell someone around you, or write down, all the R functions you can remember now!
01:30
From Analysis Plan to Analysis Pipeline
For the remainder of the session, I'll hang around to answer questions and guide any of you with filling in the rest of your analysis plan or analysis pipeline
You can use these completed docs almost like a Recipe for when you start working in R
This allows you to approach your analyses in a more systematic way which will help with any R confusion!
- Remember, we want to be easy on ourselves and not overcomplicate things! Some prior planning will help us with that
Task! Try to add in what functions and teaching materials you'll use or where you can find resources to help you with each step to the pipeline doc!
Get started on filling in parts of ur analysis plan with actual code or functions required or what materials you can use or where to find resources on how to do it