Final Project Expectations and Regulations

Logistics

Submission

  • Both qmd and html
  • Use the submission link I will provide


Deadline

  • Final project is due Dec 19th.
  • Final project approval is due Nov 5th.

Final Project Structure

In a single paragraph,

  • Describe what you are trying to find out (research question)
  • Describe why the research question is worthwhile answering
  • Describe what the final dataset would look like
    • observational units (plots in an agronomic experiment, county, gene expression, etc)
    • temporal and spatial scales if applicable
    • list of variables
  • Provide summary statistics for each of the raw datasets using tables
  • Generate appropriate figures that help the readers understand the nature of the datasets
  • Process raw datasets and combine them
  • Explore visually the relationship of variables you are interested in understanding
  • Run statistical analysis (unless impossible) and explain the results (Note: if you are Ag Econ/Econ students, you must run at least one regression)
  • Present the results in a table and draw conclusions
  • Summarize the report in several sentences


Regulations and Bonus Points

Regulation

  • Use Quarto (.qmd)
  • Use at least two raw datasets that need to be combined before analysis, and you need to merge the datasets at some point
  • Describe the purpose for each of the R code chunks
  • Use tidyverse functions wherever possible (for practice)
  • Use ggplot2 for all the figures you generate
  • Use one of gt, flextable, and modelsummary to create tables (Note: if you are in Ag Econ/Econ, you need to create at least one regression results table using modelsummary)
  • Your project must include spatial operations using at least one of sf, raster, and stars packages if you are in Agronomy and Horticulture.


Bonus Points

  • Define and use a function(s) in a meaningful way (2 points)
  • Use looping in a meaningful way and parallelize it (2 points)
  • Use more than three types of figures in a meaningful way (2 points)

Hints

Hint 1

The fastest and easiest way to find your final project is to simply bring back one of your old projects (whether it was done in R or outside of R) and redo everything (data wrangling, visualization, statistical analysis, reporting) using R, taking advantage of what you have learned in the class.


Hint 2

Assignment 1 is very similar to what your final project should look like.


Hint 3

This assignment is like writing a journal article except you provide lots of details about how you process datasets with all the R codes displayed.