HackMD
Lab 7: Datatypes and Types of Data
We've already seen a bunch of datatypes that have been built into Pyret, such as String, Number, Table, and List. We've even used our own custom datatypes, like Coord! By creating custom datatypes, we can describe a piece of data with many components.
What is is-___? (and why we prefer cases)
In class, we saw the cases notation used to distinguish between variants. When you define a custom datatype, Pyret automatically creates an is-___ function for each variant, that returns True if the input is of that dataclass' type and variant. For example, for the datatype
data Tech:
| cellphone(type :: String)
| laptop(brand :: String, memory-gigs :: Number)
end
to tell if my-tech-object is a cellphone, we could call is-cellphone(my-tech-object). In most situations (aka for all of the tasks we'll encounter in 111), however, we prefer that you use cases. This is because cases allows you to simultaneously ask which variant a piece of data is, and to assign names to the fields to use in any subsequent computations. cases also gets you in the habit of writing down a computation for every variant of a piece of data, which decreases hard-to-find bugs (for example, forgetting a variant when trying to use is-____).
Today, we'll do another design problem that draws on datatypes, tables, and lists.
Learning Objectives
In this lab, the goal is to:
- Think about ways of representing information
- Practice communicating clearly about data design decisions
- Practice defining datatypes
- Use
casesto deconstruct datatypes
If you don’t feel more comfortable with datatypes after working on this lab, come to TA hours!
Resources
Part 1: Clean Up the Catalog
You've been asked to write programs to help manage orders for items at a local store. The store maintains multiple tables: one a catalog of the items that they sell, and the other s table of the orders that have been placed on a given day (one table per day).
We'll start with the catalog table. Import the catalog Google Sheet into your program as a table by copying the following lines of code into the Pyret definitions window:
include shared-gdrive("dcic-2021", "1wyQZj_L0qqV9Ekgr9au6RX2iqt2Ga8Ep")
include gdrive-sheets
import lists as L
import math as M
ssid = "1REUzlRZ9aVZCO-KXb1d1vTpEnjJSvuFaRmghu7LX2GM"
data-sheet = load-spreadsheet(ssid)
item-table =
load-table: item-id, item-description, price, weight, dimensions
source: data-sheet.sheet-by-name("Sheet1", true)
end
Task 1: The first thing you've been asked to do is some analysis on the total volume (width x depth x height) of the items in the catalog. Look at the table you have loaded. Discuss with your partner how you might work with the data in the "dimensions" column to do this (you don't have to write the code, just discuss how you might do this).
Regardless of what your team decided, let's practice converting the dimensions strings into a collection of numbers that could be used to compute volume.
Task 2: Create a Dimension datatype consisting of three numbers: width, depth, and height.
Task 3: Use transform-column to replace each dimension string with a value of type Dimension. The original strings are formatted as: "WIDTHxDEPTHxHEIGHT", e.g., "12x10x5".
Hint: Look up string-split-all in the Strings documentation.
Hint: Splitting the string still leaves you with the numbers in string form. You need to convert those to numbers. Since someone might try to convert a String that contains characters other than digits (e.g., string-to-number("!@#")), string-to-number returns a special type (called an Option) that signals whether the conversion worked. Here's how to use string-to-number to extract the Number for a String of only digits (note the .value on the end):
>>> string-to-number("!@#")
none
>>> string-to-number("42")
some(42)
>>> string-to-number("42").value
42
CHECKPOINT: Call over a TA once you reach this point.
Part 2: Online Ordering
Next, you get a peek at how the shop is storing its orders. Each day, one table is created. That table contains a "name" column, specifying the person who placed the order, and a "description" column, listing the item that the corresponding person bought. Orders are bundled together and shipped on each evening, so one shipment goes to each person whose name appears in the table (for instance, in the table below, John won't receive two separate orders). Here is an example:
| name | description | price | count | weight | dimensions |
|---|---|---|---|---|---|
| Anne | Cork Board | 8.29 | 1 | 1.2 | 17x0.8x23 |
| John | Fuzzy Socks | 2.50 | 5 | 0.1 | 9x7x0.5 |
| Ken | Fedora | 16.99 | 2 | 0.1 | 9x9x4 |
| Anne | Printer | 129.99 | 1 | 9.5 | 15.4x11.8x5.7 |
| Anne | Fuzzy Socks | 2.50 | 3 | 0.1 | 9x7x0.5 |
| Ken | Printer | 129.99 | 1 | 9.5 | 15.4x11.8x5.7 |
| John | Lamp | 79.99 | 1 | 2.57 | 10.5x7x20.5 |
| Ken | Fuzzy Socks | 2.50 | 1 | 0.1 | 9x7x0.5 |
| Anne | Gift Card | 40.00 | 1 | 0 | 0 |
| Ken | Gift Card | 20.00 | 2 | 0 | 0 |
Gift cards don't appear in the catalog because they are by word-of-mouth, but people can still include them in their orders.
This part of lab asks you to think about the structure (column contents and types) of these tables and whether they can support the computations you'll need to do. These include:
- Compute the total cost of each person's order.
- Compute the total volume (with respect to spatial dimensions) of each person's order.
- Compute the maximum single dimension of each person's order (determines box size for shipping).
- Compute their total sales per day.
- Compute how many of each item gets sold for purposes of inventory.
Task 4: Discuss with your partner the strengths and weaknesses of the current table organizations. Write down your main concerns as a collection of brief bullet points.
Task 5: Spend 5-10 minutes coming up with an alternative proposal for the datatypes, tables, and lists that you might use. Indicate the types of all of your columns, components, and list contents. How does your proposal address each of your concerns from the previous question?
Task 6: Now, take a look at two of our concrete proposals, listed in this document. Which do you prefer and why?
CHECKPOINT: Call over a TA once you reach this point.
Part 3: The Tables Have Turned
"Well, well, well, how the turntables…"
To get more practice working with datatypes, we will now work with the second of our proposals.
Task 7: Define the datatypes ItemData, Order, and UserOrder as described in the document linked above.
(mini) CHECKPOINT: Call over a TA once you reach this point to see your datatypes.
Task 8: How specifically does this collection of datatypes address the issues that we identified with the original shop design?
Task 9: Recreate the information of the above orders table with your new datatypes.
NOTE: you should use all three of the datatypes you defined, in addition to the Dimension datatype from Task 2.
NOTE: you do not need to create a new table using these datatypes. We want you to rewrite the data represented in the table with your datatypes. For example, to represent John's orders, we can write something like
john = user-order("John", [list: order(...), order(...)])
Task 10: Write a function more-socks that takes a List<Order> (assume the list contains only items and not gift cards) and returns a List<Order> that has the items from the original orders, except each item matching the description "Fuzzy Socks" has its count increased by 1.
Task 11: Write a function any-oversized that takes a List<Order> and returns a Boolean indicating whether any single item in the order has a total linear dimension (length + width + height) of more than 40 inches (the same units as the original table).
Task 12: Write a function order-cost that takes a List<UserOrder> and a customer name and returns the total price of the items in that person's orders. Gift cards cost their amount plus a 50-cent processing fee per card. Physical items cost the price associated with the item (ignore tax, shipping costs, etc). Assume that the list contains the customer.
CHECKPOINT: Call over a TA once you reach this point.
Brown University CSCI 0111 (Spring 2025)
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