Lab report module 2

The purpose of this assignment is for you to demonstrate basic familiarity with importing, inspecting, visualising, and interpreting archaeological data. Remember that you are warmly welcome to ask for help at any time by posting a message to our Discord Links to an external site. workspace. You can do this assignment by logging into RStudio with your UW Net ID in your web browser here. Optimise your RStudio software for safe and efficient work by following my instructions here. 

We are investigating zooarchaeological and art data from Palaeolithic sites in south west France to answer the question: What was the purpose of Palaeolithic cave art?

Here's what you need to do:

1. Create a new RStudio project (New Project -> New Directory -> New Project), and:
   • call it 'archy-208-module-2-bmarwick' (replace my UW ID with yours)
   • you should get a new, empty folder, ready to work in
   • move the data from Rice and Patterson 1985 Links to an external site. that we have prepared for you. Download the CSV file with the data here Download Download the CSV file with the data here and move it into your RStudio project directory that you just created. 
   • remember that file names are vital here, it must keep its original name: do not rename the data file. Do not download it multiple times because this can cause confusion, and may cause you to lose points when your assignment is graded.
2. In your new project, create a new Quarto file (File -> New file -> Quarto Document -> click "Create"), and:
   • call it archy-208-module-2-bmarwick.qmd (replace my UW ID with yours), and ensure the YAML front matter has
   • an effective title (how?), e.g. in the form of a question or statement
   • your name, hint: author: "Ben Marwick"
   • the date automatically inserted, hint: date: now
   • html set as the output format, hint: format: html: self-contained: true
3. In your new qmd file:
   • delete all the template content
   • add a code block that reads in the data using the function read_csv() Note the underscore instead of the period in this function.
   • add three code blocks that use the ggplot2 package to produce three sets of plots. 
4. In your qmd file, write 4-5 fully-formed sentence that answer these questions:

• • What is the aim of your report (hint: what question are you going to answer? (see Thursday's lecture slides Download Thursday's lecture slides)
  • What types of data are you analysing? Show one instance of inline R code to get summary values, for example of how many regions are represented in the data. Note that we need to switch the internal backticks for single quotes when writing inline:  `r sum(art_fauna_data$'Art count')`. Ensure that you cite the publication where we got the data from using the APA citation style, and have a reference section with this item
  • What relationships do you observe when you visualise the data? Use terminology of correlation, especially strong/weak negative/positive correlation
  • What is your conclusion about the purpose of the cave art? Ensure that you cite the publication where we got the data from using the APA citation style and have a reference section. You are also welcome (but not required) to cite other scholarly works that you find relevant to your investigation. 

5. Render your qmd file to output a HTML document.

6. Before the next lab class, upload to Canvas your three files, one at a time (please do not zip them into one file), and only upload each file once:

• your data file, correctly named, check that your data file name is exactly the same as ours
• your qmd file, correctly named, with correct YAML, with code and text, and
• your HTML file, containing the output of your code and text

Don't forget: you are warmly welcome to ask for help at any time by posting a message to our Discord Links to an external site. workspace.  

Additional resources: 

• The best way to import CSV data into R Links to an external site. 
• R for Data Science: Data import Links to an external site. 
• Data Carpentry: Data visualization with ggplot2 Links to an external site. 
• R for Data Science: Data visualisation Links to an external site.