How to Generate Charts and Tables using Survey Data

This tutorial is the second in a series taking a user new to Tabx right through to delivery of Dashboards, Data Viz, Tables and Crosstabs. Read the first article, Survey Data Processing in Tabx.

This tutorial will pick up where we left off in the first tutorial, so you'll want to read that, register and create your project to follow along.

Note: You do not always need to perform data processing to generate charts and tables in Tabx. In fact, the only requirement to being able to generate a chart or table within a project is to group one or more variables - that's it!

The Interrogate Tool

At the heart of Tabx lies the Collation Engine. This proprietary technology allows Tabx users to interact, break, slice, split or otherwise interrogate their data without performing time consuming data shaping or waiting for recalculations to finish. The Interrogate Tool has been built to allow a user to deep dive into their data and use data visualization to extract insight from their survey data.

Tabx makes this process as quick and easy as possible, in fact whenever a project is uploaded a default chart and table are automatically generated for that user in the project (for both the Interrogate and Dashboard tools). Just navigate to the Interrogate Tool on the top navigation bar. Tabx will pick the first available variable to use in the chart and table. In the case of our project in this tutorial, that's the Gender variable:

Note, the Admin User in a Tabx license can always see all available tools, however additional users within the license will need to be given permission to view each of the tools.

In Tabx, charts are always accompanied by a table, whether either is visible or not. Changing settings for either, will push those changes to both. 

As we can see, the default chart will always be a column chart, showing percentages. Under the chart is the output data table - we can see the type of percentages shown on the chart are drawn from the "Column %" rows. The table also details the respondent Base, Absolute N counts and Totals for the data selected. Also note that the chart and table only cover half of the available area. This is because the Interrogate tool is designed for comparison - viewing two windows at once to explore the data from multiple angles. To make full width, use the Maximise button at the top right of the window. In the Interrogate Tool, users can add as many chart+table windows as they need for any project. For this tutorial, we'll keep the chart and table window at half size as this makes interaction within the Settings Dialogue (see Data Selection below) easier.

Exports

Tabx allows user to export their charts, tables and even whole dashboards in a number of ways. From the Interrogate Tool, we can see the following export options:

• Export Table - Generate an excel data file where cells are set with native cell types and merged appropriately.
• Embed - Generate a code snippet to embed a chart onto a webpage. Embedding charts retains the interactive elements and animations of the chart.
• Export Excel Chart - Generate an excel data file that contains a native office chart object to further format and use in any office program.
• Export Image - Generate a lossless PNG image to use anywhere an image can be used!

For example, if we export the chart as an image at this point, we get the following:

Data Selection

To modify anything on the chart or table, we'll use the Settings Dialogue. This can be accessed from the   Settings button at the top right of any Chart + Table window (or chart or table cards on the dashboard). The Settings Dialogue will appear as a sidebar on the right of the screen. 

Tabx is built around what we call the "Least Clicks" principal. That is, a user should click the absolute minimum of times to get to the desired output. Changing the data behind the table is extremely quick. Most analysis performed on survey data is two-dimensional - in Tabx we call these dimensions the Analysis variable and the Break variable. There is always an Analysis variable selected, but there doesn't always have to be a Break variable selected. In this case, we can see the Analysis variable is our currently selected Gender variable in the Settings Dialogue, on the Data tab:

Notice that in the Analysis variable and Break variable sections, the variables are organised into the groups we set up in the Data Processing phase. To change the Analysis variable, simply choose a group from the Group dropdown, then a variable from within that group from the Variable dropdown in the Analysis variable section. In our case, let's select the Brand Most Aware variable, from the Brand Awareness group:

As soon as the variable is chosen from the dropdown, the chart and table automatically update - it really is that easy! This change is automatically saved to your user - every actyion taken in the settings dialogue is saved. This means that even if your device explodes, you'll be able to pick up from where you left off the next time you login to Tabx. Here's how an exported chart image looks at this point:

Quick tip: Hover over (or touch if on mobile) the icon to see what your chart and table looks like underneath the settings dialogue.

We can see from the chart that awareness of Brand 2 is larger than all the other brands within this variable at 51%. Whilst this may be true for the dataset as a whole, it is probably more beneficial for us to understand if that awareness changes depending on a given demographic. To achieve this in Tabx is as easy as the selection we just performed, except this time we'll choose a variable inside the Break variable section instead. Let's see if awareness is affected between the genders:

To get a better idea of how each brand compares between the Genders, it makes better sense to switch around which variable is the Analysis variable, and which is the Break. Luckily, Tabx has a one-click solution to this: the Transpose button. After clicking the Transpose button we are better able to see the differences amongst each brand:

There are minimal differences for brands 1 to 4, however we can see that in the "None" category, there appears to be far more Females. Let's confirm our suspicion by turning to the data table underneath. In the same Data tab within the Settings Dialogue, scroll to the bottom section - Table Options - and enable Significance Test 99% and Significance Test 95%. These tests check to see whether a given Break category is significantly larger than the other categories within the Analysis variable - in this case with a 99% or 95% confidence level - and adds rows on the table to display this information. We can see using the provided Key row on the table, that indeed a significantly large amount of Females are not aware of any of the brands listed:

The text "abCd" reflects which other columns this column's figure is larger than, using the text's case to denote 95% or 99% significant. That is to say, the answer of "None" for "Females" is significantly larger than columns a, b and d (Brands 1,2 and 4) with a 95% confidence level, and significantly larger than column C (Brand 3) with a 99% confidence level. 

Interestingly, the reverse also seems to be true - significantly fewer Males are not aware of any of the listed brands - or to put it another way, the listed brands are more popular with Males than Females within this research.

Customisation

Once a researcher has drilled down into their data and found something insightful to include in a report or present to a client, there's a good chance they'll want to brand the chart or table ready to export to its final destination. Let's revert our data selection back to viewing the Brand Most Aware variable as the Analysis variable unbroken (nothing in the Break Variable). 

The Chart tab in the Setting Dialogue controls how the chart looks and behaves and we can modify things like:

• Chart Type
• Background, Font and Series colours
• Grid on/off
• Bar Gaps and Spacing
• Size and Style of all fonts

Let's customise our chart and brand it using the colours and fonts controls. To start with, let's change our chart type to a Donut:

Next, let's apply the following styling changes:

• Change the chart background to the Tabx Dark Green "rgb(4,34,43)" in the Chart Settings section.
• Change the Chart Title's colour to white "rgb(255,255,255)" in the Title Settings section.
• While we're here, also bump up the Title's font size and Change the position to Bottom.
• Select "Use Series Colour" from the Colour dropdown in the Value Mark Settings section.
• Hide the Legend in the Legend Settings section.
• Enable "Answers Inline" in the Value Mark Settings section.

The whole branding process took only a few clicks and dramatically changed the look of the chart. Likewise, similar options exist for branding the table:

Branding in Tabx is limited only to the colour palette of your company or client. Here's some examples of how Tabx can be used to brand and customise your chart, tables and dashboards: