## About the class
Data wrangling is when you prepare the data for the analysis. A key aspect is joining data tables. AI can help design the process, and give you code to do that.
### Objectives summary:
Learn how to organize data in a tidy way, join multiple datasets, choose variables to answer a research question and create a reproducible workflow to analyze data.
### Learning Objectives
* Learn how to store information in a tidy way.
* Work with relational data.
* Join tables
* Use AI to explain complex concepts
### Before class
* Background reading: Békés-Kézdi (2021) Chapter 2
* Download `data-modified.zip` from [Here](/data/austria-hotels). Unzip. It is a set of csv files such as 'cities_modified'
* Also available on Moodle
* The data [description is available here](/data/austria-hotels/hotel-data-readme.md)
## Class Plan
### Recap
Discuss assignment 03 (20 mins by groups + 10 mins together)
* Create 4-member groups. Each groups will read reports by an another team (1-->2, 2-->3, N-->1)
* Read the other team's submissions with a 'reader's perspective' and take notes.
* which report did you like the most and why
* rank reports in terms of how much AI was involved from low to high and note suspicious examples
### Task 1: Use AI to understand these terms. Ask examples. (Individual)
* tidy data table
* relational datasets,
* schema,
* primary and foreign key
* composite key
* joining tables
* different types of join
* 1:1, 1:m
* joining tables in your language (python, R, Stata)
This is followed by a discussion.
## Task 2: Form 2-3 groups of people using same coding language
Use the data you downloaded to carry out joins and inspect results. Use AI but inspect.
## 1:1
1. Join hotels and cities. Compare left, right, inner, outer joins.
* what happens to N?
## 1:m
2. Start: Tabulate the frequency of hotels by city_hotel_counts
3. Cities to Hotels
* one city joins to multiple hotels
* filter on 2 cities for easier visibility
4. Join hotel and occupancy 1
* m:1
5. Join hotel and occupancy 2
* get hotel level
* trick: aggregate
6. Join on composite key
* create a data table at city-year-match level showing average occupancy and tourist arrivals
## Advice, ideas
* discuss and collect ideas from AI
* learn to focus on key suggestions (AI can go nuanced and not important points easily)
## Home Assignment
Suggested assignment [[/assignments/assignment_04.md](/assignments/assignment_04.md)]
## End of Week Discussion points
* How useful was AI in teaching skills?
* How useful was AI in actually joining tables?
* How can you debug what AI did in terms of executing code?
