F Class of 2022
This was the third year for the MSc in Psychological Research Methods with Data Science. The classes were back in person and we cracked on with the core motivation of PSY6422 - to set students up for their independent data science project, carried out over the summer, but also to ensure that they left the MSc with a complete data project they could show off as part of a portfolio.
F.1 Module project showcase
There were too many students on the module this year to show all projects, so here are a few highlights. Many creative, fun, interesting, challenging visualisation projects were produced but aren’t shown here, but these few give a flavour of what student’s got up to:
Florence’s project looked at Gender bias in the cover personalities of TIME Magazine (1920-2013)
Pages
Gemma’s project was “Female labour force participation rates over the Last 100 Years”
Pages
Repo
Yibo looked at Causes of death in the world, Pages
Peter’s project shows character preference for the Overwatch League 2019
Pages
Repo
Luke’s project is an interactive visualisation which allows you to explore “Global Levels of GDP per Capita, Population Density and Covid-19 Lockdown Severity, and their Relation to World Happiness”
Pages Repo
This project looked at “Covid-19-immunised to total region population ratio in the Czech Republic”
Pages
Repo
Ben looked at Efficacy of Self Testing - comparing two Covid 19 prevalence metrics across stages of the pandemic in England; Pages
Natasha: A stacked bar plot showing likert scale responses to imagined traumatic scenarios,
Pages
Other projects looked at the income of UK doctors compared to inflation over the last 12 years, Pages; a A History of Pitchfork Reviews, Pages; Kieran’s project looked at the effect of losing crowds on football scores: Home Advantage and Covid-19: Football’s Natural Experiment, Pages
F.2 Advice for future students
As part of the module assessment I asked those who took the module “what advice would you give someone starting this course?” Here’s what they said:
its not as hard as you think it will be, you learn pretty quick, google helps a lot, start work on it early.
F.3 “one thing you have read and enjoyed or found useful”
I also asked the students for recommended reading:
F.3.1 General Visualisation
Video: How to avoid death By PowerPoint | David JP Phillips | TEDxStockholmSalon
Schwabish, J. (2021, Feb 8). Five Charts You’ve Never Used but Should. PolicyViz.
“Data to Viz leads you to the most appropriate graph for your data. It links to the code to build it and lists common caveats you should avoid” data-to-viz.com/
Visual Vocabulary: Designing with data ft-interactive.github.io/visual-vocabulary/
Economist.com Mistakes, we’ve drawn a few: Learning from our errors in data visualisation
Cairo, A (2012). The functional art: an introduction to information graphics and visualisation (1 st ed.). New Riders.
F.3.2 R specific advice
Ruggeri, G. (2021, January 29). Better data communication with {GGPLOT2}](https://giulia-ruggeri.medium.com/better-data-communication-with-ggplot2-92fbcfea2c6e). Medium.
Datanovia: “TOP R COLOR PALETTES TO KNOW FOR GREAT DATA VISUALIZATION” extremely useful for the choice of the most appropriate colour palette for my final project ().
DeBruine, L. & Barr, D. (2019). Data Skills for Reproducible Science (1.0.0). Zenodo. https://doi.org/ 10.5281/zenodo.3564555
PDF: RStudio: Data Wrangling Cheatsheet
r-statistics.co by Selva Prabhakaran Top 50 ggplot2 Visualizations
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. (2 nd ed.). Springer.
Holtz, Y. (2022). The R Graph Gallery – Help and inspiration for R charts
discovr: a package of interactive tutorials https://www.discovr.rocks/discovr/
F.3.4 Advanced R packages
Paldhous, (2016). ‘From R to interactive charts and maps’
Creating a SQLite database for use with R
Hailperin, K. (2019). Animate ggplots with gganimate:: CHEAT SHEET
Shiny Tutorials. Psyteachr.github.io.
R Studio: Learn Shiny
Mastering Shiny: mastering-shiny.org/index.html * Particularly mastering-shiny.org/reactivity-objects.html
F.3.5 General coding / projects / reproducibility
PDF: Jenny Bryan’s “Naming Things” produced for a ‘Reproducible Science Workshop.’
stackoverflow.com (“any errors that I had somebody else most likely had the same and there was always a few different solutions provided.”)
- e.g Krabel, T. (2018). Make plotly annotation font bold
- e.g Formatting mouse over labels in plotly when using ggplotly
Broman, K. W., & Woo, K. H. (2018). Data organization in spreadsheets. The American Statistician, 72(1), 2-10.
F.4 Feedback on the module
Here is some selected feedback from the class of 2022.
Tom and Luke’s help was invaluable. Having two people within the class that could guide us made all the difference when facing certain problems.
I really enjoyed the final project. During classes I struggled a lot with the content but the final project I was able to go over everything we had learnt in my own time and understand it fully. This gave me a massive sense of accomplishment