Assignment 3 (25%)
Your task is to produce a short report analysing some data using a quarto template with a targets workflow and a renv environment. You may use any data set you choose provided it includes at least 1000 observations. Possible sources of data include:
- Our World in Data
- Human Mortality Database
- Human Fertility Database
- UN Data
- IMF Data
- ABS Census Data
- Australian Electoral Commission data
- US General Social Survey
- HILDA Survey
- Johns Hopkins COVID data
The source data must be in as close to raw form as possible. e.g., csv or xlsx files obtained from the data custodians. You are not to use data that is already in an R package.
Your analysis should include the following elements:
- Reading the raw data into R.
- Cleaning and wrangling the data into a form suitable for analysis.
- At least two plots highlighting interesting features of the data.
- At least one statistical model fitted to the data (or a subset of the data). This could be a linear model, a GLM, a GAM, a GAMM, a time series model, or any other model you think is appropriate. There are no marks awarded for model complexity — you should use a model that is appropriate for the data.
- A discussion of the results of the model and how they relate to the plots.
You must use the targets package to manage the workflow, renv to manage the package environment, with the analysis described in a quarto report.
AI & Generative AI tools may be used in guided ways within this assessment. If you used AI in completing this assignment, please explain how it was used, including any prompts. Where used, AI must be used responsibly, clearly documented and appropriately acknowledged (see Learn HQ). Any work submitted for a mark must:
- represent a sincere demonstration of your human efforts, skills and subject knowledge that you will be accountable for.
- adhere to the guidelines for AI use set for the assessment task.
- reflect the University’s commitment to academic integrity and ethical behaviour.
Inappropriate AI use and/or AI use without acknowledgement will be considered a breach of academic integrity.
Due: 2 May 2025
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