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Bank Failures Multiple Views

✓ Published1🌍 Public
Jjjlaber
Last edited Apr 13, 2023
Created on Apr 13, 2023

This example presents two linked visualizations of US bank failures from 1934 to 2020, using data from a cleaned CSV file. The first view is a donut chart showing the number of failures by transaction type, with labels for segments exceeding 100 failures. The second view displays a treemap ranking the top 10 banks by a selected metric—estimated loss, asset value, or deposit value—via a dropdown menu. Both visualizations use D3 v7 with SVG rendering, employing `d3.pie`, `d3.arc`, and `d3.treemap` to construct the charts and `flatRollup` to aggregate values.

AI-generated description

Loading a dataset that has information on food orders/deliveries in NYC through the app Foodhub.

(Dataset from: https://www.kaggle.com/datasets/ahsan81/food-ordering-and-delivery-app-dataset)

Column Description/Data Types

order_id: unique id that is connected to each order (Ordinal)

customer_id: unique id that is connected to the customer ordering the food (Ordinal)

restaurant_name: name of the restaurant that the order is from (Categorical)

cuisine_type: type of food ordered (Categorical)

cost_of_the_order: how much the order cost (Quantitative)

day_of_the_week: either Weekend or Weekday, indicates when the order took place (Categorical)

rating: rating that customer gave on order (out of 5) (Quantitative)

food_preparation: time (in minutes) that it took the restaurant to prepare the order to be delivered (Quantitative)

delivery_time: time (in minutes) that it took for the order to be delivered to the customer (Quantitative)

MIT Licensed

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