Brisbane city is home to a vast array of food trucks. There are some dedicated venues which have several trucks at a time, but there is also a body of people who follow their favourite trucks. This visual app is designed to allow people to follow their favourite trucks or their favourite types of food, termed here, truck watchers. The data is freely available via the creative commons 4.0 licence (https://creativecommons.org/licenses/by/4.0/). The initial data scrape included the 6 .csv files for the years 2017 to 2022. These were compiled into a larger dataset, food.trucks.csv. Once several steps of manipulation and map design were performed, R was unable to process the data. There was too much of it. So, the same manipulations and processes were performed with the independent .csv files, one at a time, creating 6 different maps. A further scrape of the website, https://www.bnefoodtrucks.com.au/, was performed. This was to gather the details of the “Category” for the food truck (Dessert, Italian, etc.). This was used to colour code the map dots so as to afford better tracking for truck watchers. Future maps will afford finer selections for trucks. These selections will include type, drinks vs food, spicy food (a special category), or even single truck watching. NOTE: It was only after several iterations of the code were performed that it was noticed that the website https://www.bnefoodtrucks.com.au/ had several of the features being attempted with the code herein. Please disregard that website. No code was taken from that website other than a string of information in the form: Dessert / Sweet discovered upon inspection of the page “https://www.bnefoodtrucks.com.au/food-trucks”.

Dr. Nathaniel Mitchell 2/29 Crown Street, Holland Park West, 4121 Mobile: 0410 638 227 Email: 77nathanielmitchell@gmail.com Orcid: 0000-0002-9511-2616

WNW is a streaming platform who is testing a new algorithm for recommendations. A live test began on the 18th of Jul and we have been supplied with the data for the whole of July for the investigation. Our working hypothesis is: – “The new recommendation algorithm engenders more viewing for all subscribers. We will see that the recommendation algorithm works and engenders different viewing habits in the subscribers. We will also see that this seems to affect the younger subscribers with more disposable income.

There are two groups, A and B, who receive and do not receive a flu jab, respectively. We will first randomly generate a table (grouped_df) which returns a boolean value for A and B given that they contract the flu given that the flu has a contagion rate of 30%, which is lessened to 10% with the flu jab

This is a brief explanation of what the data looks like. This is attached in order to assure myself that I’m looking at the dataset that is needed, and to assure the marker (the reader) that I have some understanding of what the dataframe is displaying. Each region is separated and named with an “SSC” prefix then a number “21184”. Each region has an indication of individual ages (from 0-55), a reference for M or F (male or female) and then a population count for “males at age 0”, “females at age 0”, and so on. This means that each row is one age number (0-55), one gender (M or F), and a population. In order to collect the data form the regions, we must group each region’s data by adding together the populations across M and F, and between all age groups. In order to get means, however, we need to multiplying the age by the population for every range (other than age 0, as this will throw up an error).

This data visualisation is designed to deliver a series of key informative metrics to stakeholders of the United States Energy Information Administration (herein EIA). Although over 10 years old, the details of energy production and consumption are relevant to any energy producer or industry body. Thus, the key audience are those members of the body of the industry who either organise energy production, use, or flow.