Datasets

2022 LIHTC Tenant Data

Dataset: "2022 LIHTC Full Tenant Data.csv"

• 46 variables and 38,228 observations

Documentation

On the original site providing the data, refer to Documentation Guide for HUD’s Low Income Housing Tax Credit (LIHTC) Tenant Data. Provided is information on each of the tables that can come with the raw data. The dataset we have is a collection of different tables' data joined by property (name, city, and state).

• Columns 1-5 come from "2022 LIHTC Tenant Data by Property - HUD_ID.xlsx" (note that propertyname, city, and state are found in all tables)
• Columns 6-13 come from Table 4
• Columns 14-20 come from Table 8
• Columns 21-26 come from Table 9
• Columns 27-33 come from Table 10
• Columns 34-36 come from Table 11
• Columns 37-46 come from Table 12

Using the given documentation descriptions for each table, you should be able to interpret the column names and are encouraged to rename them for your comprehension.

Note - all other files in "2022 LIHTC Tenant Data/data" are raw files from HUD

2022 Qatar World Cup

Dataset: "worldcup_2022_matches.csv"

• 9 variables and 60 observations

Documentation

Data comes from football.db's worldcup repo in the 2022--qatar folder, which has been copied to our World Cup/data folder. Any confusion with the data presented in the dataset should be resolved by reading the original text files, cup.txt and cup_finals.txt, and the code in make_csv.R (you can review text processing and regular expressions here).

Predicting Dropouts from MOOC Courses

Setup

The full MOOC dataset would be too large to fit in a repo. Build it locally.

1. Install R
2. Access the download link here. Click the download link for the "Dropout Prediction Dataset".
3. The downloaded file is a .gz file. See if you are able to open it on your computer and unzip it. If not, search for instructions online for your Operating System. The only files you need from this .gz file are train_log.csv and test_log.csv.
4. In GitHub, you already have test_truth.csv and train_truth.csv in the MOOC folder. Download these files in GitHub locally in your computer, along with make.R and build_dataset.bat. Place the files in folders so that they are organized the same way that they are in the repo. Drag and drop train_log.csv and test_log.csv into the raw folder. Your folder should look like this:

MOOC/
  raw/
    train_log.csv
    test_log.csv
    train_truth.csv
    test_truth.csv
  make.R
  build_dataset.bat

5. Double-click build_dataset.bat.