代写FIT3152 Data analytics – 2024: Assignment 1代做R语言

日期：2024-04-11 05:48

FIT3152 Data analytics – 2024: Assignment 1

Your task	? Analyse the country level predictors of pro-social behaviours to reduce the spread of COVID- 19 during the early stages of the pandemic. ? This is an individual assignment.
Value	? This assignment is worth 25% of your total marks for the unit. ? It has 40 marks in total.
Suggested Length	? 8 – 10 A4 pages (for your report) + extra pages as appendix (for your R script, clustering table, and Generative AI statement if required). ? Font size 11 or 12pt,single spacing.
Due Date	11.55pm Monday 15th April 2024
Submission	? Submit a single PDF file and single video file on Moodle. ? Note that submission of a video report is a hurdle requirement. ? Use the naming convention: FirstnameSecondnameID.{pdf, mp4, movetc.} ? Turnitin will be used for similarity checking of all written submissions.
Generative AI Use	? In this assessment, you can use generative artificial intelligence (AI) in order to search for R functions and examples to perform tasks that you specify only. Any use of generative AI must be appropriately acknowledged (see Learn HQ).
Late Penalties	? 10% (4 mark) deduction per calendar day for up to one week. ? Submissions more than 7 calendar days after the due date will receive a mark of zero (0) and no assessment feedback will be provided.

Instructions

Address each of the research questions below and report the results of your analysis and your interpretation of those results.

You are expected to include at least one high quality multivariate graphic summarising key results. You may also include other simpler graphs and tables. Report any assumptions you’ve made in modelling and include your R code as an appendix. Your R code must be machine readable text as the university requires all student submissions to be processed by plagiarism detection software. You must include a declaration on the use of Generative AI at the beginning of your written report and if you used Generative AI,a statement on how it was used, as an appendix.

There are two options for compiling your written report:

(1) You can create your report using any word processor with your R code pasted in as machine- readable text as an appendix, and save as a pdf, or

(2) As an R Markup document that contains the R code with the discussion/text interleaved. Render this as an HTML file and save as a pdf.

Your video report should be less than 100MB in size. You may need to reduce the resolution of your original recording to achieve this. Use a standard file format such as .mp4, or mov for submission.

Software

It is expected that you will use R for your data analysis and graphics and tables. You are free to use any R packages you need but must document these in your report and include in your R code. You may use other software, such as Excel, to create the table of clustering data for Question 3(a) .

Use of Generative AI

In this assessment, you can use generative artificial intelligence (AI) in order to search for R functions and examples to perform. tasks that you specify only. Any use of generative AI must be appropriately acknowledged (see Learn HQ).

If you do use Generative AI for your assignment then you must include the statement “Generative AI was used in this assignment.” In the introductory/first paragraph of your report. You must also include the following information as an appendix in your report: (1) the technology you used (e.g. ChatGPT), (2 the information that was generated (e.g. R code fragments), (3) the prompts used (i.e. the questions you asked), and (4) how the output was used in your work.

If you did not use generative AI in your assignment, then include the statement “Generative AI was not used in this assignment.” In the introductory/first paragraph of your report.

Questions

During the early stages of the COVID- 19 pandemic, researchers surveyed participants around the globe. A baseline study was conducted with the aim of identifying the most important predictors of pro-social COVID-19 behaviours, that is, actions that would reduce the spread of the virus. You can  read a more detailed description of the research and results in Van Lissa (2022), see references.

The aim of this assignment is to understand country-level differences in predictors of pro-social behaviours, reported by participants as: "I am willing to:

? help others who suffer from coronavirus." (c19ProSo01)

? make donations to help others that suffer from coronavirus. " (c19ProSo02)

? protect vulnerable groups from coronavirus even at my own expense." (c19ProSo03)

? make personal sacrifices to prevent the spread of coronavirus." (c19ProSo04)

Your task is to analyse the baseline survey data overall, with a focus on the country you have been assigned. You may make use of any additional data you require to answer the following questions.

1.          Descriptive analysis and pre-processing. (6 Marks)

(a) Describe the data overall, including things such as dimension, data types, distribution of numerical attributes, variety of non-numerical (text) attributes, missing values, and anything else of interest or relevance.

(b) Comment on any pre-processing or data manipulation required for the following analysis.

2.          Focus country vs all other countries as a group. (12 Marks)

(a) Identify your focus country from the accompanying list (FocusCountryByID.pdf). How do participant responses (attributes) for your focus country differ from the other countries in   the survey as a group?

(b) How well do participant responses (attributes) predict pro-social attitudes (c19ProSo01,2,3 and 4) for your focus country? Which attributes seem to be the best predictors? Explain your reasoning.

(c) Repeat Question 2(b) for the other countries as a group. Which attributes are the strongest predictors? How do these attributes compare to those of your focus country?

3.          Focus country vs cluster of similar countries. (10 Marks)

(a) Using a collection of social, economic, health, political or other indicators from external sources, identify at least 5 countries in the baseline data that are similar to your focus country using clustering. Van Lissa (2022) refers to several indicators you might consider, among others. Some of these are listed in the references, but these are not exhaustive. State the indicators used and describe how you calculated/identified similar countries. Copy and paste the table of values you used for your clustering into your report as an Appendix.

(b) How well do participant responses (attributes) predict pro-social attitudes (c19ProSo01,2,3 and 4) for this cluster of similar countries? Which attributes are the strongest predictors? How do these attributes compare to those of your focus country? Comment on the similarity and/or difference between your results for this question and Question 2(c). That is, does the group of all other countries 2(c), or the cluster of similar countries 3(b) give a better match to the important attributes for predicting pro-social attitudes in your focus country? Discuss.

4.         Video Presentation: (Submission Hurdle and 4 Marks)

Record a short presentation using your smart phone, Zoom, or similar method. Your presentation should be approximately 5 minutes in length and summarise your main findings for Sections 1 – 3, as well as describing how you conducted your research and any assumptions made. Pay particular emphasis to your results in Questions 2(c) and 3(b)

5           Overall considerations (8 Marks)

This includes: the quality and clarity of your reasoning and assumptions; the strength of support for your findings; the quality of your writing in general and communication of results; the quality of your graphics throughout, including at least one high-quality multivariate graphic; the quality of your R coding.

Data

The data for this assignment is a reduced version of that collected for the PsyCorona baseline study, Van Lissa et al. (2022). The filename is "PsyCoronaBaselineExtract.csv". The data includes ordinal data coded on a numerical scale. For this assignment assume it is reasonable to treat these responses as numerical.

Create your individual data as follows:

rm(list = ls())

set.seed(12345678) # XXXXXXXX = your student ID

cvbase = read.csv("PsyCoronaBaselineExtract.csv")

cvbase <- cvbase[sample(nrow(cvbase), 40000), ] # 40000 rows

Locate your focus country using the accompanying document FocusCountryByID.pdf.