Merge pull request #289 from itsleeds/update-s4

Update Session 4 to align with new syllabus order

Author: Robinlovelace

s4/index.qmd

@@ -23,12 +23,15 @@ In this session, we will learn how to use origin-destination data. To do that we
 
 # Review Homework
 
-Your homework last week was to review some AI written code. Divide into groups of 2-3 people and share the question you asked AI, the code it produced, and the results it got.
+In the previous session and for your homework, you used AI to assist with loading datasets, creating visualizations, and working through vignettes on origin-destination (OD) using the `od` and `pct` packages. Let's evaluate your findings in groups of 2-3.
 
-* Does the code run first time?
-* Do the results answer the question?
-* Try modifying the code and the prompt to improve the code generated by the AI.
-* Discuss in your groups the strengths and weaknesses of AI written code. 
+* **The Workflow**: Show your group the specific prompts you gave the AI, the code it generated, and the resulting output.
+
+* **The "First Run"** Test: Did the code run the first time? If it failed, what caused the error (e.g., outdated library syntax, incorrect file paths, or spatial projection issues)?
+
+* **Accuracy**: Did the AI truly understand the spatial logic of data, or did it just provide a generic visualization?
+
+* **Refinement**: Share how you modified your prompts or the code itself to achieve a better, more accurate result.
 
 # Getting started with GIS in R
 
@@ -1073,79 +1076,7 @@ bike_trips |>
 # Homework
 
 1. Read Chapters 2-5 of [Geocomputation with R](https://r.geocompx.org/transport.html) 
-2. Using knowledge about datasets and new skills covered in the practicals 2 and 3, think of a simple research question that could be answered by analysing/visualising/modelling one of the datasets imported into R in your previous homework (or another dataset you have imported into R).
-3. Write a prompt, starting with your own code that imports the code, to generate some code answering the question and write it down in a new Quarto file called `gen-ai-test.qmd`.
-4. Copy the prompt and paste it into a generative AI chat interface such as [ChatGPT](https://chatgpt.com), [Claude](https://claude.ai/), [DeepSeek](https://chat.deepseek.com/) or [Gemini](https://gemini.google.com/)
-5. Paste the resulting code into a code chunk in a new .qmd file, e.g. called `gen-ai-test.qmd`.
-    - Does the code run first time?
-    - Do the results answer the question?
-    - Try modifying the code and the prompt to improve the code generated by the AI.
-    - Note: in the next practical session we will review the AI-generated code you will share the code with a colleague and they will try to run it.
-6. Bonus: try setting-up Copilot or other code auto-completion system in RStudio using the documentation at
-    <!-- https://docs.posit.co/ide/user/ide/guide/tools/copilot.html -->
-  [docs.posit.co](https://docs.posit.co/ide/user/ide/guide/tools/copilot.html) or VSCode using the [GitHub Copilot](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot) extension.
-7. Bonus: share your code on GitHub, for example by pasting it into the discussion group at [github.com/ITSLeeds/TDS/discussions](https://github.com/itsleeds/tds/discussions/172) or by creating a repo on your own GitHub account.
-8. Bonus: Read more about using the [tmap package](https://r-tmap.github.io/tmap/)
-9. Bonus: Read more about the [ggplot2 package](https://ggplot2.tidyverse.org/)
-
-<details>
-
-I wrote the following prompt:
-
-Starting with the following R code that starts by loading the tidyverse and stats19 R packages, write a script that finds out which local authorities saw the greatest percentage point decrease in the number of road traffic collisions between 2019 and 2020.
-Explore this relationship for the total number of collisions with summary statistics, ggplot2 visualisations, and perhaps a basic model. Furthermore, explore how the % change in collision numbers vary depending on factors such as urban or rural area, casualty severity, and the month used for comparison.
-
-```{r}
-#| eval: false
-library(tidyverse)
-library(stats19)
-collisions_2019 = get_stats19(2019)
-collisions_2020 = get_stats19(2020)
-collisions_combined = bind_rows(
-  mutate(collisions_2019, year = 2019),
-  mutate(collisions_2020, year = 2020)
-)
-# names(collisions_2020)
-#  [1] "accident_index"                             
-#  [2] "accident_year"                              
-#  [3] "accident_reference"                         
-#  [4] "location_easting_osgr"                      
-#  [5] "location_northing_osgr"                     
-#  [6] "longitude"                                  
-#  [7] "latitude"                                   
-#  [8] "police_force"                               
-#  [9] "accident_severity"                          
-# [10] "number_of_vehicles"                         
-# [11] "number_of_casualties"                       
-# [12] "date"                                       
-# [13] "day_of_week"                                
-# [14] "time"                                       
-# [15] "local_authority_district"                   
-# [16] "local_authority_ons_district"               
-# [17] "local_authority_highway"                    
-# [18] "first_road_class"                           
-# [19] "first_road_number"                          
-# [20] "road_type"                                  
-# [21] "speed_limit"                                
-# [22] "junction_detail"                            
-# [23] "junction_control"                           
-# [24] "second_road_class"                          
-# [25] "second_road_number"                         
-# [26] "pedestrian_crossing_human_control"          
-# [27] "pedestrian_crossing_physical_facilities"    
-# [28] "light_conditions"                           
-# [29] "weather_conditions"                         
-# [30] "road_surface_conditions"                    
-# [31] "special_conditions_at_site"                 
-# [32] "carriageway_hazards"                        
-# [33] "urban_or_rural_area"                        
-# [34] "did_police_officer_attend_scene_of_accident"
-# [35] "trunk_road_flag"                            
-# [36] "lsoa_of_accident_location"                  
-# [37] "enhanced_severity_collision"                
-# [38] "datetime" 
-```
-
-</details>
+2. Bonus: Read more about using the [tmap package](https://r-tmap.github.io/tmap/)
+3. Bonus: Read more about the [ggplot2 package](https://ggplot2.tidyverse.org/)
 
 # References