Method Chaining

Students practice more of what they learned in the previous lesson, applying the Design Recipe to make Transformers with unique formula expressions that operate on rows of the Animals Dataset. These become the basis of the chaining activity that follows.

The Design Recipe is a powerful tool for solving problems by writing formula expressions. It’s important for this to be like second nature, so let’s get some more practice using it!

Did students find themselves getting faster at using the Design Recipe? Can students share any patterns they noticed, or shortcuts they used?

Students learn how to compose multiple table operations (sorting, filtering, building) on the same table - a technique called "chaining".

Now that we are doing more sophisticated analyses, we might find ourselves in a situation where we want to use a series of Transformers!

Let’s say that a journalist comes to the shelter. She 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 wants to consider all eligible animals in order from lightest to heaviest.

This reporter also informs you that it might take some time for her to find the perfect pet adoption scenario. Until she finds what she’s looking for, she wants a weekly updated list of the pets who meet her criteria.

Let’s break down the reporter’s specifications:

The journalist has a lot of needs!

Fortunately, in CODAP, we can use the result of one Transformer as an input to another. In other words: Transformers can be chained, resulting in a sequence of datasets, each a transformed version of the previous. Any updates made to an input dataset will flow through and affect its outputs. If you have a chain of transformed datasets and you change the original dataset, the updates will flow through the chain.

As you chain together Transformers, it is important to remember the following:

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!

Let’s go through how we might meet the journalist’s needs.

For this step, we’ll use Build Attribute, which makes a new copy of the dataset, adding a Weight (kgs) column. (Hint: To get from pounds to kilograms, divide by 2.205.)

We are going to Filter now, applying the Transformer to the dataset we created in the previous step. In the dropdown menu of datasets, our dataset is named (BuildAttribute(Animals-Dataset)). We will want to keep all rows where Weight (kgs) is less than or equal to 9.

Remember - we want to be able to easily create a table that meets the journalist’s specifications, so we’ll want to use the Sort Transformer here, rather than the Sort dropdown option that appears when we click Weight. Be sure to select the correct dataset to sort!

Because we’re using Transformers, each time the Animals Table is updated, the journalist’s table will be updated, too! Try adding or removing a row from the original table and observe the ripple effect in your chained tables.

How well do you know your table methods? Complete Chaining Methods and Chaining Methods 2: Order Matters in your Student Workbook to find out.

As our analysis gets more complex, chaining is a great way to re-use work we’ve already done. And less duplicate work means a smaller chance of bugs. Chaining is a powerful way to work, so it’s critical to think carefully when we use it!