Transformers

Students sort a column on a table by actually changing the table.

Open the Animals Dataset in CODAP. How many animals' names start with the letter M? What would make it easier to figure out how many animals' names start with the letter M?

Student work in groups or pairs. They will sort various columns on the table by clicking the attribute name, and selecting from the drop-down menu either Sort Ascending or Sort Descending.

Using the Sort Transformer, students sort rows of a table in ascending or descending order, according to one column. When they use transformers, students are building and modifying a copy of the original table.

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The Transformers plugin allows students to transform datasets to produce new, distinct datasets, rather than modifying the original input dataset itself.

First, show students how they can access the Transformers plugin (see screenshot on right), and invite them to explore the different Transformers available.

Explain to students that the Sort Transformer consumes: (1) a dataset; (2) a formula; (3) the type that the formula evaluates to; and (4) a sort direction (ascending or descending). But what does it produce?

Students may be more familiar with filters that actually change the table. In CODAP, all transformers produce a brand new table. Filtered tables are automatically saved; CODAP titles each new table with a number in curly braces at the end (for example, Filter(Animals-Dataset) {1} ) to indicate how many times the transformer has been applied. When students apply a transformer, they have the option of selecting the original table from the dropdown menu, or a new table that they’ve generated. Students can also rename saved tables, if they’d like.

Students learn how to filter tables by removing Rows.

Explain to students that you have "Function Cards", which describe the purpose statement of a function that consumes a Row from a table of students, and produces a Boolean (e.g. - "this student is wearing glasses"). Select a volunteer to be the "Filter Transformer", and have them randomly choose a function card from from the Function Cards set, and make sure they read it without showing it to anyone else.

Have 6-8 students line up in front of the classroom, and have the filter transformer go to each student and say "stay" or "sit" depending on whether their function would return true or false for that student. If they say "sit", the student sits down. If they say "stay", the student stays standing.

Ask the class: based on who sat and who stayed, what function was on the card?

Suppose we want to get a table of only animals that have been fixed? The Filter transformer consumes a dataset to filter and a formula expression that evaluates to either true or false.

The Filter Transformer walks through the table, applying whatever formula it was given to each row, and produces a new table containing all the rows for which the formula returned true. Notice that Filter takes a dataset and produces a copy of it that contains only the cases for which the given formula evaluates to true. If it consumes anything besides a single Row, or if it produces anything else besides a Boolean, we’ll get an error.

Debrief with students. Some guiding questions on filtering:

Students learn how to build columns, using the Build Attribute transformer.

Suppose we want to transform our table, converting pounds to kilograms or weeks to days. The Build Attribute transformer makes a new copy of a dataset, and adds a new attribute. We must provide a dataset, a name for the new attribute, an existing collection to add the attribute to, a formula for the attribute’s values, and an indication of the type of value the formula will evaluate to.

The Build Attribute Transformer walks through the table, applying whatever formula expression it was given to each row. Whatever the formula expression produces for that row becomes the value of our new column, which is named based on the string it was given. In the first example, we gave it Age < 5, so the new table had an extra Boolean column for every animal, indicating whether or not it was young.

Debrief with students. Ask them if they can think of a situation where they would want to use this. Some ideas:

Being able to define is a huge upgrade in our ability to analyze data! But as a wise person once said, "with great power comes great responsibility"! Dropping all the dogs from our dataset might be a cute exercise in this class, but suppose we want to drop certain populations from a national census? Even a small programming error could erase millions of people, impact funding for things like roads and schools, etc.