Lab 9 Reflection - Chpts 1-9

9.1 Overview

We have covered a lot of material in these first few chapters and now would be a good time to stop, recap, and reflect on what we have learnt. As such, this chapter is more about looking back at what you have learnt, testing your skills, resolving issues, and looking at other cool applications of R that have not been covered in this lab series.

9.2 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.

9.3 InClass Activity

1. Like the PreClass, we want to spend some time reflecting on what we have learnt and as such this InClass is about looking at ideas, concepts, and codes, that you have had issue with and seeing if we can resolve those issues. In class, we will spend some time looking at any issues you have had along the way.

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, such as:

   • Popping out the Source Window to make working easier - Using Source Windows
   • Analysing Twitter data with the rtweet package (Kearney 2020)
   • Animating plots with the ggganimate package (Pedersen and Robinson 2020)
   • Creating quickfire quizzes with the webex (Barr and DeBruine 2021)
   • Make your own memes using the meme package (Yu 2021)
   • 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() (Pedersen 2021)
   • 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)
   • The statcheck package for checking that you and others are presenting their inferential analyses accurately (in terms of df and p-value) and in APA format (Michele B. Nuijten <m.b.nuijten@uvt.nl> 2018)
   • A great repository of R resources for teaching, learning, writing webpages, CVs, and doing very cool things with webpages

9.4 Assignment

This is a summative assignment and as such, as well as testing your knowledge, skills, and learning, this assignment contributes to your overall grade for this semester. You will be instructed by the Course Lead on Moodle as to when you will receive this assignment, as well as given full instructions as to how to access and submit the assignment. Please check the information and schedule on the Level 2 Moodle page.

9.5 Solutions to Questions

Instructions on how to access the solution to this lab will be made available during the course.

References

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

Kearney, Michael W. 2020. Rtweet: Collecting Twitter Data. https://CRAN.R-project.org/package=rtweet.

Michele B. Nuijten <m.b.nuijten@uvt.nl>, Sacha Epskamp &. 2018. Statcheck: Extract Statistics from Articles and Recompute p Values. https://CRAN.R-project.org/package=statcheck.

Pedersen, Thomas Lin. 2021. Ggforce: Accelerating Ggplot2. https://CRAN.R-project.org/package=ggforce.

Pedersen, Thomas Lin, and David Robinson. 2020. Gganimate: A Grammar of Animated Graphics. https://CRAN.R-project.org/package=gganimate.

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

Yu, Guangchuang. 2021. Meme: Create Meme. https://github.com/GuangchuangYu/meme/.