Lab 16 Reflection - Chpts 10-15

16.1 Overview

As in Semester 1 we have covered a lot of material in these labs and now would be a good time to stop, recap, and reflect on what we have learned. As such this last chapter is again about looking back at what you have learned, reflecting on your knowledge and skills, resolving issues, and looking at other cool applications of R that have not been covered in this lab series.

16.2 PreClass Activity

As we are reflecting on what we have covered so far, your preclass activities this time are:

1. Review the previous chapters and note any issues you have with the elements covered - both in terms of concepts and code.

2. Post these issues on available discussion channels and we can look at them together at our next meeting.

16.3 InClass Activity

1. Like in the PreClass we want to spend sometime reflecting on what we have learnt and as such this InClass is about looking at ideas, concepts, and codes, that you have had issues with and seeing if we can resolve those issues. It would be particularly worthwhile spending sometime looking at aspects of working with the GLM and decomposition matrices as this will make up much of the course next year.

2. We will also look at some other interesting things you can do in R, should you wish to expand your own knowledge and skills. Below is the list we looked at in Semester 1 but, I am sure we have found loads more as the year has gone on, such as:

• New

  • redoc package, by Ross Noam, for collaborative editing of Word documents (https://noamross.github.io/redoc/)
  • faux for the creation of simulated data (https://debruine.github.io/faux/) (R-faux?)
  • checkpoint for timestamping the version of the packages you are using in your code
  • mydata <- read.delim("clipboard") for creating a dataframe from your copy and paste function on your keyboard.
  • R.Online to compare codes across different versions of R (https://srv.colinfay.me:1001/)
  • Hack Your Data Beautiful (https://psyteachr.github.io/hack-your-data/index.html) - a workshop run by a team of postgraduates in Psychology @ Glasgow that shows a range of excellent and interesting skills and applications.

• Previous

  • Popping out the Source Window to make working easier - Using Source Windows
  • Analysing Twitter data with the rtweet package (R-rtweet?)
  • Animating plots with the ggganimate package (R-gganimate?)
  • Creating quickfire quizzes with the webex (Barr and DeBruine 2021)
  • Make your own memes using the meme package (R-meme?)
  • The Hex Sticker Memory game and the background behind it
  • Creating interactive plots using ggplot and plotly
  • Even funkier visualtions using the ggforce package - check out facet_zoom() (R-ggforce?)
  • Many diverse fields are now using R and this is a good example: R for Journalists
  • Using the knitr::read_chunk() function to call R script code through R Markdown (Xie 2021)

16.4 Update Your Notes

Over the course of this book you have hugely developed your knowledge and skills in a variety of data skills, data management, data wrangling, and data analysis approaches. This is a great achievement and you should be very proud of youself. One thing to note though is that these skills are very much like learning a language or any other skill; the more you practice and the more you use them, they better you will become. As such, now would be an excellent time to review all your notes, check your understanding of the different approaches and sections, see what you know and what you are still uncertain of, and make yourself a plan to bring your notes together, fill in any gaps in your knowledge, and solidify your knowledge and learning. If you have any questions, please post them on the TEAMS channel or speak to a member of staff and don’t forget to add any useful information to your Portfolio before you leave it too long and forget.

16.5 In The End!

Excellent! That is it. That is all we wanted you to achieve before the end of Level 2! Congratulations on all that you have achieved over the course of this book! As we said before, you are now highly competent, and hopefully confident, at data-wrangling, visualisation, and analysis, as well as interpreting a whole host of different analyses. You are now more than ready for Level 3! Thanks for all your hard work. You have been amazing!

References

Barr, Dale, and Lisa DeBruine. 2021. Webex: Create Interactive Web Exercises in r Markdown. https://github.com/psyteachr/webex.

Xie, Yihui. 2021. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.