Design Challenge 1 (DC1): One dataset / Four Stories

In this assignment, you’ll pick one data set to make visualizations from. Then, you will make 4 visualizations – each telling a different “story” about the data. The idea here is that you should explore the different kinds of visualizations you might make from this data, and the different questions/tasks that you might want to show someone.

We will provide you with the data set. We will give you a few choices. See the DC1 Data Sets page.

Overview

We would like to emphasize the use of “end-user” tools (such as Tableau). In fact, we will require you to at least try Tableau. We discourage you from doing any programming from this assignment. You may use any tools that you like to create the visualizations – subject to the constraint that you are required to hand in PDFs, and to document your process.

You will hand in real visualizations with the real data. If you find that you aren’t able to exactly implement your design (e.g. you can’t figure out how to convince excel to use the colors that you want), feel free to “cheat” a little (e.g., save the picture and open it in Photoshop and paint over it), but part of the idea is to try to make pictures with real data (so don’t just sketch – unless you are doing precise measurements). If you’re really stumped on implementation, you can put a note in your caption “the red dots were supposed to be blue” – but try not to leave too much to the imagination of the viewer.

We will grade your visualizations based on criteria detailed below. Generally, we care that you identify interesting things to show in the data (stories) and choose visualizations that effectively show those things. We care that you choose appropriate encodings, give good rationales for your choices, and that you take active steps to emphasize the message you are trying to make.

Part of this assignment will be peer critique. You will turn things in without your name on it (so we can do critique anonymously). We will then give everyone a few designs from other class members to write critiques of. You will be graded on your critiques (these will contribute to your grade, but will not be used directly in grading the author).

Using Tableau is part of this assignment, although you don’t need to use it for your final turnin. There is pedagogical value in seeing Tableau as it is a nice embodiment of some of the “theory” we discuss in class. See the class Tableau page.

Note that for this assignment you are to create static visualizations, with appropriate captions, as PDFs. Interaction is for later in the class.

You will work individually for this design challenge.

Deadlines / Milestones

All deadlines are on Mondays

• 9/21 - Phase 1 - Tableau Tests
• 9/28 - Phase 2 - Data Experiments / Drafts - We would like to see your initial work, mainly
• 10/5 - Phase 3 - Turn in Designs - You will turn in your 4 visualizations as 4 PDF files. Please do not have your name in your files, but do have the PDF files be self-contained (with captions). Some of these will be given to your classmates for peer evaluation.
• 10/12 - Phase 4 - Rationale, Alternate and Peer Review - You will turn in reviews of some assigned examples from your classmates. You will also turn in the final documentation of your own designs.

Stories and Visualizations

The goal of this assignment is to create visualizations that “tell stories” from the data: that highlight / show something in the data. They shouldn’t just dump a bunch of numbers: they should make an intended message come out.

Try not to pick questions that can be answered with a single statistic – but something where the visualization adds value. The richer and more complex the task the story (or sets of stories) that the visualization tells makes it more interesting (and challenging), and gives you more opportunities to make a particularly cool “story”.

We’ve picked the data sets (but you get to choose amongst them). See DC1 Data Sets for your choices. You get to pick the stories to tell. Think about stories that someone would care about. Stories that would be interesting.

A visualization should have a purpose - something that it helps the viewer see. This is the “story” that it tells. Visualizations that simply present the data (without something in mind) are not the goal here. “This visualization lets us see the distribution of grades in the class” is not as interesting (for this assignment) as “this visualization shows the correlation between how multi-variate a visualization is and the grade, letting us see that that mutli-variate visualizations are pretty much necessary for a good grade”.

Part of the idea of this assignment is that by looking at the data in different ways, you can see different things in it. Therefore, we ask you to make 4 different visualizations, each telling a “different story” from the data (e.g., highlighting a different interesting thing in the data).

Data Sets

The data sets you may use are described on DC1 Data Sets. You must use one of the data sets that we provide. You may process the data or “augment it” (get some other data that helps interpret the data - for example you may find a table that helps you translate from three letter airport codes to city name or GPS coordinates). But, in general, you should stick to the data provided.

Criteria

We care that you have a diverse set of stories: your visualizations show different aspects of the data.

For each visualization / story, we will check for:

1. Is the question/story interesting and clear?
2. Is it multi-variate?
3. Is the design effective? (is it well adapted to the story/task?)
4. Do the details represent good choices?
5. Is the design appropriate for the data?
6. Is the rationale properly stated (in the documentation)
7. Is the design complete (it has enough of a caption that it stands alone)?

The best designs for this assignment are multivariate and specifically adapted to the task/story. They may use a standard design (stacked bar chart), but use good choices in the details (e.g., the ordering of the bars or the colors) to make the “answer to the question” easy to see. We look for signs of students making explicit good choices to make what they want the viewer to see easy to see. (you can explain your choices in the documentation)

We will look for diversity in the stories that you choose to tell with the visualizations.

Generally, we look for diversity in designs. If all of your visualizations are bar charts, that’s often a bad sign. Of course, if it really is the case that you have found four questions that are each best answered with various bar charts, that’s less of an issue. But in general, you may want to pick stories that are told with a variety of designs.

To emphasize: what we are looking for are what explicit choices you made to emphasize your “story.” A “data dump” (just making a chart of some of the variables) is not likely to get you a good score. If you make explicit decisions - selecting subsets or data, highlighting particular points, arranging designs that emphasize certain aspects, etc. - this will be rewarded.

How to Make Visualizations

In Phase 1 of this challenge we require you to use Tableau. We want you to at least experience what it is like to work with a state-of-the-art commercial tool. Tableau also embodies many important visualization concepts (data abstractions, explicit choices about encodings and transformations, automatic selection or chart types, …), so it is useful to see how it works. Underneath, Tableau is built on lots of the best research and implementation practice. And its automatic guidance tools embody a lot of design knowledge from expert practitioners.

We will provide you with access to Tableau (both online and desktop), and give you some guidance on getting started with it and suggest resources. We won’t “teach” you to use Tableau. See the Tableau page.

In Phase 2, we require that at least some of your designs use Tableau - to show that you have at least tried to use it with your intended data set.

For your Phase 3 and Phase 4, you may use whatever tools you like. We encourage you to use Tableau, but we will not force you to do so. But, any tool is fine, as long as you can get your visualizations into PDF files. It is fine to use Excel or Tableau or JMP or some other “tool.” We discourage you from programming to make your visualizations.

If you are going to make standard designs, you probably should use standard tools.

You may want to use some tools to “explore” the data initially, and then different tools to make the specific visualizations that you want. (although, Tableau is excellent for exploration)

Note: you are turning in static visualizations as PDFs. You may use interactive tools to make them, but you need the visualizations to “tell the story” as a static picture.

You may “edit” the visualizations that come from the tools (but please document this). For example, you might make a picture in Tableau, and then load that into PhotoShop or PowerPoint in order to add captions or adjust colors.

You may find you want to do some data wrangling, data cleaning, or analysis before visualization. Try not to make this the main part of the assignment. Many of the data sets are “clean” enough that you can use them directly in Tableau. If you need to do some programming for this part, it’s OK – be sure to describe what you did, and turn in the programs that you wrote.

What to turn in for the deadlines

There are 4 separate “assignments” to turn in (one for each deadline). You are required to do all of them.

You receive a letter grade for the whole design challenge.

Note that at the earlier deadlines you only need to turn in part of your work, but it builds up to an entire “final handin” at the end. For example, you must turn in your final visualizations in Phase 3 (so they can be peer reviewed), but you provide the rationales for them in Phase 4.

Deadline 1: Tableau Tests (September 21)

The goal of this phase is to make sure that you have at least tried Tableau, and have gotten enough familiarity with it to produce visualizations. Using Tableau will tie closely with the discussions in class (data abstractions, encodings, …). Some of the ideas behind Tableau will appear in optional readings. See the Tableau page for how to access Tableau, resources for learning it, and advice on how to get started with it.

You may pick any data set that you like (a Tableau tutorial data set is fine, or you might even try exploring one of the data sets for the later part of this design challenge).

You must turn in 3 different visualizations. You may turn them in as PDFs or image files (PNG or JPG). Images are easier for us in this phase. Please turn in each visualization as a separate post. Part of the assignment is to make sure you can get images out of Tableau.

We aren’t asking for much in terms of the visualizations - it’s mainly to check that you’ve tried to learn and use Tableau. But please make sure your visualizations:

1. Present 3 different basic designs
2. Present more than 2 variables (in at least some of the visualizations)
3. Explore different encodings (e.g., use color and size - in addition to the basic chart types)
4. Show that you can do some aggregation (e.g., counting or summation over a range of items)

In this phase of the assignment, we are not going to evaluate the visualizations you produce - we are merely checking that you have the required pictures.

We are going to ask you to turn in your designs by posting them to a Canvas Discussion so that your classmates can see them. You aren’t required to comment on other peoples’ designs - but you are welcome to. We hope that people will look at others’ designs for inspiration. Also, if you see something and wonder “how did you do that” you might ask the author.

In prior years, we’ve asked people to tell us that they have at least looked at the data sets and chosen one to work with. We aren’t asking you to tell us that this week - but you probably want to do it. The next deadlines will come up very quickly…

Post your images on Canvas as DC1-1: Tableau Tests (due Mon, Sep 21).

Deadline 2: Data Experiments / Drafts (September 28th)

Our goal here is to make sure that you are making progress towards the larger deadline next week. By this point, you should be making visualizations with a data set, and have at least some ideas what designs you will turn in for the following week.

Please turn in a draft of at least one of your designs. You can turn in more than one, and you can turn in a draft of a rationale as well. You should turn in the design as a PDF with a caption (as practice for what you will turn in the following week).

We will grade this as check/no-check. We want to see that you are making visualizations from one of the datasets, so you will be ready for next week’s bigger deadline.

We may try to provide some (limited) feedback on the designs you turn in, however, we cannot promise that we will be able to do so.

Turn your image files in on Canvas as DC1-2: Experiments / Drafts (due Mon, Sep 28).

Deadline 3: Designs (October 5)

This assignment is the “main” handin: you will turn in your designs.

It is important that you turn this in as 4 separate PDFs, named as we need them. We need to send your designs out for peer review, so we need things in a specific format.

For your 4 stories/designs, please number them 1.pdf, 2.pdf, 3.pdf, 4.pdf. Make sure that they are a single page and do not have your name on them. Each one should have a sufficient caption and labels that a viewer can figure them out. See the grading criteria for more discussion.

Each caption should clearly say which data set is being used. In general, you should assume the viewer is looking at the visualization separately (not together with the other ones you’ve made) and is unfamiliar with the dataset.

We will grade these visualizations as part of the final handin - you just need to turn them in for this deadline so we can send them out for peer review.

If you want, you can turn in one design “late” (as part of the final handin) without penalty. You can turn in 3 visualizations for this deadline, and add the fourth one that will not be peer reviewed. (we are only going to pick 1-2 of your designs to be peer reviewed). If you have not turned in 3 designs at the time we are assigning peer reviews, you will be penalized.

Turn your PDF files in on Canvas as DC1-3: Design Handins (due Mon, Oct 5).

Deadline 4: Rationale, Alternate and Peer Review (October 12)

This phase consists of two parts: your final handin, and peer reviews of others’ visualizations.

Final Handin

Please provide a written description of the assignment as a single PDF. It should describe:

1. What tools you used to create the visualizations. If you did any programming, explain what the program does (e.g., “I wrote a Python script to transform the data by aggregating by state. This made it easier to make the graphs I wanted in Excel.”). (this can be a 1-2 sentences, but you might have separate descriptions for each visualization). Also tell us what libraries you used.

2. What data you used, and what (if anything) you have done to process it. Generally, the answer to this question is one of the files we provided (e.g., “the airlines data in the airline_2019_large.csv file”). But, if you did something to the file (e.g., “I wrote a script that estimated the distance for each flight and added it as a new column”), please let us know. Also, let us know if you got your own copy of the data directly from the source.

3. For each visualization, explain what the story you were trying to convey is, and why you think the design that you chose is appropriate. See the grading criteria. (this doesn’t need to be too lengthy - 3-5 sentences per visualization is usually sufficient). The key is to provide the rationale for your design: what choices did you make and why.

4. Note that while we have your visualizations from Part 3, it is convenient for us if you also put them in your final document.

Turn your PDF file in on Canvas as DC1-4: Final Handin (due Mon, Oct 12).

Peer Review

We will send each student 3 designs from other students to critique. You will be sent an email with links to the designs you are required to critique, as well as a few optional ones (if you want to give more feedback to your classmates).

We will provide separate, detailed, instructions for this phase. They are available at DC1 Peer Critique.

You will turn in your critiques using a Google Form that we will provide. We recommend that you write the text of the critique in an editor beforehand, in case something goes wrong.

Your critiques will be given to the authors of the visualizations. Please do not identify yourself in the reviews.

You will be graded on the quality of your critiques. This will be part of your overall Design Challenge Grade. We will provide a rubric soon (for now, you can see last year’s for ideas).

Note: we are grading your critiques. Your critiques are not part of the grade for the designs that you are critiquing (although, we might consult your critiques to see if we agree).

The timing of the peer review will depend on when we can make the designs available for you to look at. This will be explained in separate instructions.

Grading

You will be assessed and given a letter grade for the design challenge.

Parts 1 and 2 will be graded “check / no-check” - you will be penalized half a letter grade (e.g., A to AB) if you do not turn them in before the cutoff. You will be penalized a half letter grade if you do not turn in enough designs to enable peer review in Phase 3.

20% of your grade will be from your peer reviews. A rubric will be provided as part of the detailed instructions for that part.

80% of your grade will be our assessment of the quality of your 4 visualizations and documentation. See Criteria above. We will provide some additional guidance closer to the deadline.

Providing you turn in assignments before the cutoff, we will grade them. Late penalties will be assessed separately.

Late Policy

This follows the class Late Policy

We will give you a grade independent of how late your assignments are. There are cutoffs - if you miss a cutoff, we may be unable to grade your assignment. Some of the cutoffs are harsh because of external constraints. For example, if you fail to turn in your designs for Phase 3, we will be unable to send them out for peer review.

We will record how late all of your hand-ins are, and consider it at the end of the semester. If you consistently miss deadlines, we will penalize you.

Some Advice…

We look for signs of students making explicit good choices to make what they want the viewer to see easy to see. (you can explain your choices in the documentation)

Your “story” should be “something that is easy to see in the visualization”, and the assignment is about making explicit choices to make the story be the thing that is easy to see.

Think about (and write in your rationale): What does this design make easy to see? This should be a fact about the data (the “story” – or the finding of the story). The fact should be specific – not just “we can see variable X and Y” – but something like “we can see the positive correlation” or “we can see the pattern that there are more yelp check ins on weekends.” And it should be something that really is easy to see (look at your visualization!). Why is it easy to see? What choices did you make that makes it easy to see?