(Also available in Pyret)
Students learn how to chain Transformers together, and define more sophisticated subsets.
Lesson Goals |
Students will be able to…
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Student-facing Lesson Goals |
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Materials |
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Preparation |
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🔗Design Recipe Practice 25 minutes
Overview
In this lesson students build on what they have already learned to formalize their understanding of the Design Recipe, and then gain fluency with using the Design Recipe.
Launch
The Design Recipe is a sequence of steps that helps us document, test out, and write the expressions that our Transformers need to work properly - enabling us to thoughtfully analyze data! It’s important for this to be like second nature, so let’s get some practice using it.
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Let’s look at this Design Recipe worksheet together.
First, we need to decide which Transformer to use: Filter, Transform, or Build. Based on the Transformer’s already-provided name, students should deduce that they will use Filter. They can then record the Transformer’s name on the line.
Next, we provide example tables. In this case, we want to know the animals' names and their species, so we write down those column names. We want to list a few different animals - at least one that is a dog, and at least one that is not - to represent the variety of animals on the table. Then we think about what our transformed table will look like:
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Will Sasha be on the new table? No, Sasha is a cat. We only want dogs!
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Will Fritz be on the new table? Yes, Fritz is a dog.
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Will Toggle be on the new table? Yes, Toggle is a dog.
Now, we are ready to drill down on the contents of our Transformer.
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First - the contract, which requires a domain (what type of data will we provide) and a range (what type of data will be produced). Whenever we are filtering, we can expect the contract to be the same: Row -> Boolean.
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Next, we need a clear purpose statement which describes what the expression does to each row. In this case, the expression will consume an animal and compute whether the species is "dog" - as our example tables (above) demonstrate!
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And finally, we enter our expression, in this case:
species = "dog".
Each time students encounter a new word problem, we encourage working through it with paper and pencil, as above; the Design Recipe slows down students' thinking and encourages them to reason through each scenario fully rather than guessing haphazardly.
Investigate
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Open your saved Animals Starter File.
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Define the Transformers and expressions on Design Recipe 2.
Optional: Combining Booleans Suppose we want to build a table of Animals that are fixed and old, or a table of animals that are cats or dogs? By using the When we want to chain together different Transformers, however, this strategy will not work. We’ll need to find another way! |
Synthesize
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Did you find yourselves getting faster at using the Design Recipe?
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What patterns or shortcuts are you noticing, when you use the Design Recipe?
🔗Chaining 25 minutes
Overview
Students learn how to compose multiple table operations (sorting, filtering, building) on the same table - a technique called "chaining".
Launch
A journalist comes to the shelter who wants to write a story about a successful pet adoption — but she has a very specific set of criteria. The reporter wants to report on the adoption of an animal that weighs no more than 9 kilograms. She also wants to review an updated copy of the dataset each week (reflecting changes to the shelter’s population) before making a decision about which animal to showcase.
To help the journalist, you decide that you want to define and chain together two Transformers - Build Attribute and Filter.
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We’ll define a Build Attribute Transformer using the formula
pounds/2.205. (CODAP also requires that we provide a name for our new attribute, such askilograms.) -
We’ll define a Filter Transformer using the formula
kilograms<9.
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Which of the two above Transformers should we apply first,
weight-in-kgorfilter-if-light?-
We must apply
weight-in-kgfirst. (Kilogramsdoesn’t exist on our table otherwise!)
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What do you predict will happen if we apply them in the wrong order? Why?
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Students should predict that they will encounter some sort of error message.
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A perk of applying Transformers, rather than manipulating the dataset, is that any updates made to the original dataset will flow through the chain. As you chain together Transformers, it is important to remember the following:
Order matters: Build / Transform, Filter, Sort.
Suppose we want to build a column and then use it to filter our table. If we use the Transformers in the wrong order (trying to filter by a column that doesn’t exist yet), we might wind up crashing the program. Even worse, the program might work, but produce results that are incorrect!
Tip: Saving Transformers and Renaming Tables Saving a particular configuration of a Transformer is useful so that the Transformer can be easily accessed in the future. When we save a Transformer, we want to give it a useful name and purpose statement for ease of use later. We also encourage students to rename tables descriptively. By the end of this exercise, the table students create will have quite a lengthy name: |
Investigate
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How well do you know your Transformers?
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Complete Chaining and Chaining 2: Order Matters in to find out.
Synthesize
Ask students about their answers to Chaining 2: Order Matters.
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Which ones produced an error?
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Why will they produce an error?
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How could they be fixed?
Can students trigger a similar error in their Table Methods starter file?
As our analysis gets more complex, chaining is a great way to re-use code we’ve already written. And less code means a smaller chance of bugs.
🔗Additional Exercises
These materials were developed partly through support of the National Science Foundation,
(awards 1042210, 1535276, 1648684, and 1738598). 
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