Last updated on 2026-02-13 | Edit this page

Session 4

Required Self-Study

Session Plan

  • ? How are you typically using AI tools in your coding workflow?
    • small code fragments
    • individual steps
  • ? What issues arise from “localized” use of AI tools?
    • context switching
    • integration challenges
    • consistency issues

R 

url_part1 <- "https://raw.githubusercontent.com/"
url_part2 <- "dataprofessor/data/master/iris.csv"
the_url_for_the_data <- paste0(url_part1, url_part2)
data <- read.csv(the_url_for_the_data)

df2 <- data

df2 <- df2[df2$Species != "setosa", ]

df2$Sepal.Area <- df2$Sepal.Length * df2$Sepal.Width

library(dplyr)
df3 <- df2 %>%
  group_by(Species) %>%
  summarise(mean_area = mean(Sepal.Area))

df3 <- df3[order(df3$mean_area, decreasing = TRUE), ]

library(ggplot2)
ggplot(data = df3, 
       aes(x = Species, y = mean_area, fill = Species)) +
  geom_bar(stat = "identity") +
  theme(plot.title = element_text(size = 10, face = "bold")) +
  labs(title = "Mean Sepal Area by Species (Excluding Setosa)") +
  theme_minimal() +
  theme(legend.position = "none")
  • TASK:
    • test the code above
    • try to understand what it does
    • ! ask for a code documentation
    • check again if you can follow the code
    • ! ask for a code revision and define goals for the revision
      • repeat the process of revision and review until you are satisfied with the code and understand it!
      • ! keep your final goals for discussion
  • code revision … ? what are goals for code revision?
    • improve code quality
      • reduce redundancy (and number of temporary variables)
      • simplify logic and workflows
    • enhance readability
      • spacings, indention, …
      • comments and documentation
    • ensure best practices
      • naming conventions
      • package usage
  • AI is well suited to provide such a “holistic” code revision

OPTION 1: chat-based revision

  • upload existing code (file) for revision

  • prompt engineering for code revision

    • “revise this code to improve readability and maintainability”
    • “optimize this code for performance without changing its functionality”
    • “add comments and documentation to this code”

  • will produce revised code for download

  • ? What are the advantages and disadvantages of chat-based code revision?

    • advantages
      • interactive feedback
      • tailored revisions
    • disadvantages
      • context limitations
      • potential for misinterpretation
      • lack of integration with development environment to compare changes

  • NOTE: revision is typically a multi-step process

    • initial revision
    • review of revised code
    • further refinement requests

  • can also be applied to revise code snippets to see alternative implementations

  • Do not settle for code you don’t understand!

    • in that case ask to alternatives you are familiar with!
    • the final code is YOUR responsibility!

OPTION 2: IDE-integrated revision

  • use IDE AI plugin to revise code directly in the coding environment
    • either for full file
    • or for selected code blocks
  • PROBLEM: RStudio does not yet have AI-integration… 😢
    • possible workaround: chattr package to connect to chat-based LLMs from RStudio
      • but no direct code revision support yet
      • personally: so far, I found it currently not very useful for code revision tasks

OPTION 3: Issue-driven coding with Copilot

  • ? What’s a GitHub repository?

  • ? Relation of GitHub repository and local project?

  • ? What’s the difference of a GitHub repository and cloud-space-shared project?

  • ? Do you know git?

    • discussion of git workflow and basics

  • ? what’s a GitHub issue?

    • issue as “to-do” item
    • issue description as prompt for code generation
    • ! can be used to trigger autonomous code generation by Copilot !

  • DEMO: based on an existing GitHub repository with issues defined

  • ? What are the advantages and disadvantages of issue-driven coding with Copilot?

    • advantages
      • structured workflow
      • clear objectives
      • integration with version control
        • visualize and track changes
      • parallize workload by “recruiting AI assistants”
        • focus on high-level design, code revision and integration
    • disadvantages
      • learning curve for git and GitHub
      • potential for misalignment between issue description and generated code

  • ? In which scenarios would issue-driven coding be most beneficial?

  • TASK:

    • ! create a GitHub repository for a small project (or use an existing one)!
    • ! use the example solution of “Challenge 1” from the material as an initial issue to create the first R code file for your project
      • assign the issue to GitHub Copilot
      • review the generated code carefully!
      • if necessary, refine the issue description and try again!
      • merge/integrate the generated code into your project (closing the completed pull request)!
    • ! define at least 2 additional issues for the project, describing further tasks to be done!
      • coding (e.g. implementing a function, visualization, … or writing equivalent code in another language))
      • revision (e.g. improving existing code, suggesting tests, …)
      • documentation (e.g. updating/extending README.md or code commenting)
      • creating additional files (e.g. AGENTS.md)
      • …
    • ! use Copilot to address each issue!
      • review the generated code carefully!
      • if necessary, refine the issue description and try again!
    • ! review and integrate the generated code into your project (merging the completed pull requests)!

Closing Discussion

  • Always check balance between effort needed to guide AI and expected benefits…
    • … sometimes it’s just faster to do it yourself!
  • ? What are your key takeaways from this course?
    • ? Is/will AI-assisted coding be part of your workflow?