Exploring Two Columns Activity Guide

[Karriem Drewery]

Iam modifying existing code to create a view, and was looking for commonly accepted set of style guidelines on how to rename the resulting columns in the view when the component table column names clash.

The style I see used most often is tablename_columnname, but that is so much a matter of taste and arbitration that I have never seen anybody publish a style guide for that. For example, if the view is called customer_details, the table customer_data and the column id, many people might go for the less redundant and shorter data_id.

Students will practice making conclusions from charts and learn to use Quorum Studio for two different kinds of charts, a cross tab, and a scatterplot. Students will practice reading each type of chart before learning to make them. Students will track their work using a provided activity guide. The lesson concludes with a review of key takeaways.

This lesson introduces students to two new ways of make visualizations. The crosstab and scatter chart are new in that they allow students to see patterns across multiple variables, noticing how one might seem to change (or correlate) with another. This is good preparation for their unit project in which they'll need to make and interpret a data visualization of their own.

Discuss: How many 'Herding' breeds live a maximum of 12 years? What is the most common maximum life span for 'Working' breeds? Which breed group lives the shortest? Which breed group lives the longest? How do you know? How confident are you in your answers?

Understanding Crosstab: Understanding Crosstab: Give students some time to think and discuss why a crosstab chart might be a good choice for finding patterns like the ones indicated on this slide. Further reinforce the fact that if either column has too many values you may end up with an enormous chart.

Fill Out the Activity Guide Digitally: We can access data in many different ways. Code.org's Data Visualizer is one, but using Quorum to output a Crosstab to a file, and opening it in Microsoft Excel, is another.

Do This: Have students go to the Lesson4_App1 in the Unit9 folder of the CSP Widgets and use both the 'Words' and the 'Favorite Classes'data sets to complete page 1 of the Activity Guide.

Have students go to the Lesson4_App2 in the Unit9 folder of the CSP Widgets. This app generates an accessible scatter plot related to when states were admitted to the United States and their land mass in square miles.

Goal: Students will hopefully notice later states are relatively larger than earlier added ones. The trend does not necessarily reflect any causation, but there is a slight uptick in state size as more are added.

The key takeaway is that the Data Analysis process has four key steps. First, we collect or choose the data. Second, we clean and filter it. Third, we try to find patterns in the data, through creating charts or using statistics. Finally, we try to find new information.

For charts, we decide how to create them via the information we know about the data and by thinking about what we want to learn. For example, if we have 1 column of numerical data, we might use a bar chart or a histogram to view it. If we have two columns, cross tabs can be useful if we have strings and few items, while scatterplots can be useful if we have a lot of numerical data.

Note: This place is a magical place and a geological masterpiece. Please help us protect these rock formations and this area for generations to come. Please make sure to take out ALL of your trash and DO NOT deface or harm these stone columns. If you see someone there that does, please help us educate them and protect this place!

These geological wonder stone columns of Crowley Lake were buried and hidden for ages under the tons of pumice and ash. The pounding waves of Crowley Lake helped carve out the softer materials at the base of the cliffs, eventually revealing these unique columns. There are over 5,000 columns within a 2-3 miles radius.

The stone columns at Crowley Lake are grouped together. They are also diverse in shape and size, but most of them have distinct encircled horizontal cracks about 1 ft apart. Many of them are still buried in the sand.

According to the researchers from UC Berkeley, the columns were created by the melting snow seeping down into the hot volcanic ash from a cataclysmic explosion 760,000 years ago. As the water boiled, it created evenly spaced convection cells similar to heat pipes. These tiny spaces were cemented into place by minerals that were able to resist the erosion of the surrounding forces such as wind and strong waves.

Luckily, a few weeks ago, one of our friends send us a recent video of her walking through the columns. This was then that we realized our mistake; we went there during the wrong season! Every time we went, it was either toward the end of spring, summer, or early fall. The water level dips down significantly toward the end of fall and through the winter. This is when these mysterious stone columns of Crowley Lake emerge. Once the snow melts, the lake level goes back up and they disappear.

After spending some time exploring the cave, we made our way to the columns. Make sure you wear your shoes walking around. The sand is made from small pebbles, they are not fun to walk on barefoot (I know, I did that).

An Explore is a starting point for a query that is designed to explore a particular subject area. Select the Explore option from the left navigation panel to open the Explore menu.

The Explore menu presents a number of descriptive model or group names that are organized in alphanumeric order. From the Explore menu, you can search for and select Explores, which are organized alphanumerically under the model or group name to which they belong.

Explores contain views, which are groupings of dimensions and measures. The data that is shown in an Explore is determined by the dimensions and measures that you select from the views that are listed in the field picker at the left. A dimension can be thought of as a group or a bucket of data. A measure is information about that bucket of data. In the Explore data table, dimensions appear as blue columns and measures appear as orange columns.

If an Explore contains modeled queries, you can use Quick Start analyses to populate fields. The next section provides an in-depth overview of Quick Start analyses and how to use them as a starting point for exploring data.

You can modify a Quick Start analysis once it has run by adding or removing fields from the All Fields tab, from Search results, or from the In Use tab in the field picker.

Filters are additive. This means that, when run, Quick Start analyses will include any existing Explore filters. If a selected Quick Start analysis has a filter value that conflicts with an existing Explore filter, you will be prompted to select which filter value to use in the analysis.

The CA order count by month analysis has a conflicting filter value for the Users State filter. The Choose filter set menu opens, and you're prompted to resolve the conflict by selecting either the Keep current filters option, which lists the current filter values, or the Replace with new filters option, which lists the filter values of the selected Quick Start analysis.

For example, selecting the Orders Status dimension in an Explore that contains Orders Created Date and Orders Count will display the order number of orders that have the status complete, pending, or canceled by day.

The Explore summary displays the total number of fields in an Explore (including custom fields and table calculations when permissions allow) in the bottom left corner, and the Go to LookML link in the bottom right. Go to LookML directs users to the explore definition in its LookML project. This link is visible only to users with the see_lookml permission.

When you open an existing Explore, the All Fields tab is displayed by default. This tab is the starting place for building an Explore and displays all the available fields that you can select for a query. Fields are organized alphanumerically by type (dimensions, followed by measures) under the name of the view or view label in which they are defined. Each field will show field-specific information and actions, such as a field's current and potential functions in an Explore query.

Fields that are selected in a query will appear highlighted by a gray background and corresponding field icons (pivot, filter) will appear in bold without you needing to hold the pointer over a field when it is active. For example, the field Profit in the preceding field picker example is highlighted in gray, indicating that it is selected. You can tell that this field is not pivoted or filtered because all corresponding field icons are not bold and don't appear when you aren't hovering over the field.

Select a field from the All Fields tab to add it to or remove it from an Explore query. Additionally, you can select the appropriate field icon to filter, pivot, or perform other field-specific actions from the All Fields tab.

Custom fields and table calculations are listed under the Custom Fields view label. Users with the create_table_calculations permission can create and edit table calculations, and users with the create_custom_fields permission can create and edit custom fields by selecting the Add button next to the view label, or by choosing a custom field option from a field's three-dot More menu.

The In Use tab also displays an updated Explore summary at the bottom of the tab. The bottom left corner displays the total number of active fields in an Explore. A Go to LookML link is available in the bottom right to users with the see_lookml permission. Go to LookML directs users to the explore definition in its LookML project. The preceding example shows that there are currently four total active fields in the Explore.

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