Final Project MXB262, 2022 Oral presentations, individual, recorded on video Length of presentations: 10-12 minutes. 

The video must not be sped up or slowed down in order to meet the time limit (1.0x speed only). Must use slides (can be prepared in Rmarkdown, LaTeX, PowerPoint, or any related software), to be submitted along with the video. All code (including data wrangling code) must be submitted. You will be marked exclusively on the information given in the presentation and the final figures. No marks will be given for code that does not result in a useable figure. However, code must also be submitted as it will be used to verify your work, and a proportion of the grade will be given for the readability and usability of your code (see the CRA). ONE A4 page must also be submitted, outlining only the key aspects of your presentation listed below in the task description. This written document will not be explicitly marked on its own, rather it will be used as a supporting document that we can refer to while marking your presentations in case aspects are not clear due to presentation style. Therefore you should not include any information in this document that you do not also refer to in your slides and orally. Note the very short length limit has been intentionally set so that you must identify they key points in this single page. You must also give the source for your data on this page, as well as in your presentation. Task description Produce at least three visualisations using a single dataset that tell a cohesive story or message to the audience. Note that the intended audience for your visualisations does not need to be the actual audience for your presentations (i.e. fellow students and teaching staff). Although a single dataset must tie the three visualisations together, you are encouraged to combine that dataset with others if that serves your message well. In your presentations, please include: ● the main message you are aiming to communicate with your visualisations (it can be effective to consider the first 2-3 slides of the presentation as your actual pitch of this message to your intended audience and then spend the rest of the time explaining that pitch, although this structure is not required) ● description of the dataset (including a source) ● who/what/when/how for your three visualisations (some of these elements will be the same for all three – particularly the audience – and some will be different), and a full justification of this ● justification of plot type and pre-attentive attributes you have chosen to communicate your message to your audience ● the main roadblocks you overcame in the visualisation process, and why it was critical to overcome them for effective communication of your message ● two recommendations that you would have liked to implement but did not have the space or skill-set from this class to develop (and justify how they would have helped with the communication). Note that if these recommendations are deemed to have been possible, you will not receive any marks for this part of the assessment (e.g. ‘I would have rotated the plot, but I ran out of time’ is not sufficient). Note that the visualisations you create must be novel, not a replication of a visualisation found online. Students found to be replicating a set of visualisations for which a code tutorial can be found online (like the the Trump blog post from the first workshop) will receive a grade of 0 for this project. However, you can use the same dataset to create completely new visualisations that explore some other facet of the data. Choosing your visualisation types You must produce three different types of plot. Note that the complexity of the plots you choose will, in part, determine your grade. Even if technically good, three visualisations from the 2D data week of class is very unlikely to achieve a grade above a 4. In order to achieve a 6 or a 7 for this project, at least one of the visualisations must be complex – either a network or spatial visualisation, or, if you have a different pitch for a more complex visualisation, you must run it past Kate by the 20th of May. Choosing your datasets The most straightforward datasets to use are those that are either pre-loaded into R, or can be installed as data packages. You can explore the pre-loaded datasets by typing data() into the R console. Many of the packages we have been using in this class (e.g. dplyr, rnaturalearth, covdata) also contain datasets that will show up if they are loaded when you type data(). Therefore, it may be wise to load all your favourite packages using library() before exploring the pre-loaded datasets. You may NOT use mtcars or palmerpenguins datasets given how well-explored those have been in this class, nor can you use the datasets you explored in any of your Problem Solving Tasks (I will be cross-checking with your PST submissions). Note that it is very unlikely that you will receive above a grade of 5 for this assessment if pre-loaded datasets (or those contained in packages we have used in workshops) are used, unless the visualisations are extremely well done and novel, and other datasets are incorporated. Beyond pre-loaded datasets, there are many data packages available. The benefit of data packages (or packages containing datasets) is that they are likely to be cleaned and in a nice format. See this twitter thread: https://twitter.com/_jessie_roberts/status/1083613770977398784 from Jessie Roberts (who has worked on many of our workshop materials) for some ideas for Australian datasets. For a list of some more data packages try https://rviews.rstudio.com/2017/11/01/r-data-packages/ and the whole blog more generally at https://rviews.rstudio.com/. Twitter (#rstats or #dataviz) or even Reddit (r/rstats) are good places to hunt for data packages. You may also choose to use data from elsewhere on the internet that you load into R (as we have done most recently throughout the networks workshop). This is an excellent option, and offers you a lot of exciting options, likely resulting in more novel visualisations that can achieve higher grades. However, this is doing the project in hard-mode: wrangling the data may be more difficult, the structures might be slighly different, or there might be missing data. One place that we have come across downloadable data is the gapminder website, and you have likely seen some more throughout your searching for Project 1. Some google searching will open your eyes to data that might be out there (e.g. try ‘New York City data’). If you find useful repositories of datasets (beyond just the dataset you’re using), please post it to Slack – remember that we are a community with shared goals, not in competition with each other. One restriction on the visualisation: I recommend avoiding visualisations of Covid-19 cases or deaths, unless you have an extremely novel idea (and this must be pre-approved by Kate). This is an incredibly messy and full space at the moment, and it may be too tempting to replicate work already analysed at length online. One caveat is if you are combining this data with another kind of data, but again, this should be run past Kate first. Data wrangling Whichever dataset you choose, you are likely to need to subset, mutate, or generally wrangle your data. Remember we covered a lot of these methods through the R for Data Science textbook, and we have repeated many throughout the workshops. The textbook will remain a critical resource for this project. By this stage, you have all needed to google many issues in R, and you know that google will also be an important source for this process. Note that your data wrangling code will not be graded, so you do not need to achieve any of this in a stylish way. The only assessment of your ability to subset or wrangle the data is whether it resulted in effective visualisations in the end. Pitch session to Kate I will be running sessions in a week in the second half of semester for you to spend 5 minutes pitching your idea for the visualisations to me. This is a good opportunity to get quick feedback about whether the planned visualisations are complex and varied enough, and telling a clear enough story. This is not compulsory for your grade, but I hope you take advantage of this option. Times will be posted soon.