Bar Charts and Dot Plots

Students use the options on CODAP’s Configuration and Measure menus to produce visualizations of the Animals Dataset.

Today’s lesson is all about producing visualizations in CODAP.

We’ll walk through some of the basic mechanics of creating a visualization - and then you will have the opportunity to independently create and explore visualizations in CODAP.

Until we assign variables for the axes, the data points are randomly distributed on the graph.

Hovering over an orange dot reveals the name of that particular animal. Selecting a particular dot causes the table row for that animal to be highlighted in blue. On Windows and Macs, holding the shift button allows us to select multiple dots in the data visualization. We can also select multiple rows by clicking on them (holding the shift key down on Windows, the Command key down on Macs).

When we select a table row (or multiple table rows), the corresponding dots change color. When we set aside selected and / or unselected cases (by using the "eye" icon), we can temporarily alter the amount of pets in the dataset (with the option to restore to the original dataset).

We can also resize the window by dragging its borders.

Once we have a graph of randomly distributed data points, we can organize the data by selecting attributes from our table that we want to appear on the axes of our graph.

Categorical data is used to classify, rather than to measure. Only when data is being treated categorically will you be invited to fuse data points - transforming a dot plot into a bar chart. Quantitative (or numeric) data must measure or compare; it is subject to the laws of arithmetic.

Bar charts look a lot like another kind of visualization - called a "histogram" - which visualizes numeric data, not categorical.

Creating each of these visualizations begins the same way: we create a dot plot, and then modify it using CODAP’s menus. Depending on what type of data the dot plot shows, we can transform it into either a bar chart (for categorical data), or a histogram (for numeric data). These visualizations serve unique purposes!

Pie charts visualize categorical data, too, but CODAP doesn’t offer them largely because many find them challenging to read.

Students learn how to create groups within groups, showing the relative frequency of one variable across values of another variable using stacked and multi bar charts.

Comparing groups is great, but sometimes we want to compare sub-groups across groups. In this example, we want to compare the distribution of sexes across each species.

CODAP allows us to build a variety of visualizations where we specify both a group and a subgroup.

All of the charts we’ve looked at in this lesson work with categorical data, showing us the frequency of values in one or two groups.

Students explore the CODAP data visualization options available to them. In doing so, they experiment with new charts and get comfortable with CODAP as a platform for doing data science.

There are lots of different kinds of charts and plots that we can build in CODAP!

CODAP is designed to be student-friendly! Its interface encourages guesswork…​ but we can save some time by being a bit more strategic.

Let’s try a more methodical approach to creating visualizations.

There are many possible misconceptions about data visualizations that students may encounter here. But that’s OK! Understanding all those other plots is not a learning goal for this lesson. Rather, the goal at this stage is to have them build familiarity and confidence with the CODAP tool and how it makes data visualizations.

Today you’ve added more data visualizations to your toolbox. You can create dot plots and bar charts to visually display categorical data, and you’ve developed a general approach to guide you as you create other visualizations.