How to Create a Simple Dashboard in Displayr
How do you create a simple dashboard that is interactive without using code? This article shows you how to create a simple dashboard in minutes using a worked example in Displayr.
You will learn how to import data, create and combine separate charts and visualizations into one dashboard, create and apply filters, and publish online. The resulting dashboard will look like this.
Step 1: Import data into your dashboard
This post works through an example data set that has already been set up in Displayr. You can access this dashboard example with preloaded data here. This dataset has been used before in other examples, including Using Q Projects in Displayr.
Alternatively, you can follow the step-by-step instructions in this post using your own data set (the processes described here can be used with any dataset).
If you are using your own dataset you will need to sign into Displayr here and then import your data. There are many different ways of getting data into Displayr. See Introduction to Displayr 2: Getting Your Data into Displayr for an overview.
Step 2: Set up a background or look and feel (optional)
Get creative and design a background for your dashboard e.g., add a block of color, a photo or a logo. The background used in this dashboard example is saved here. To insert a background image:
- Go to Appearance > View > Page Master.
- Insert > Image and select your image.
- Make sure the image fills the page by dragging it to size.
- Do this for every page type under Master you want to apply the background too.
- When you’re done, be sure to exit the Page Master: Appearance > View > Normal.
- Alternatively, you can just import images ad-hoc with Insert > Image.
Step 3: Create and modify a simple table
This next step adds a simple table to your dashboard.
- Insert > New Page
- Enter the title on your page: Market research analysis software ranking. I changed the font size and color as well (using the Appearance tab at the top of the screen).
- Locate the section in the bottom left of your screen called Data Sets.
- Drag across Software from the Data Sets tree onto the page. This will create a table.
- Click on the table and then the category Tableu. Note that it is spelled incorrectly. To fix this, press Data Manipulation > Rename (it should be in green) and change the name to Tableau. These changes will be applied whenever the data in Software is used again (i.e., they are remembered in the data, rather than being remembered as formatting of the table).
- Click on the table and then the category Other, and press Delete (it should be in green).
- Delete None and InHouse as well (tip: you can select both categories at once using Ctrl or Shift).
- Click on the % sign at the top of the table to select it and press Data Manipulation > Sort > By Values > Descending > %. This sort will also be remembered in the data (i.e., it is applied whenever this data is used again in any other tables).
- Click on the right-side of NET in the table, where you see the three horizontal lines, and slowly drag it back to the bottom of the table, releasing your mouse when you see the word Move appear.
If you have followed all the instructions, the table on the page should look something like this:
Step 4: Creating a crosstab
To turn the above table into a crosstab (e.g., cut it by geographic region):
- Click on the page in the Pages tree (top left corner of the screen).
- Press Home > Copy and then Home > Paste. This should have copied the entire page that you just created. If it didn’t, have another go at the previous point.
- Slowly drag Region from the Data Sets tree, across on top of the table created in the previous step, dropping it in the Columns slot. You should now have a crosstab.
- Change the title at the top of the page to Market research analysis software by region.
The crosstab should look like this:
Step 5: Create a chart
The most straightforward approach to creating a chart is to click on a table (i.e., like the one we have just created via drag-and-drop), and then click Home > Chart. Then you select the type of chart you want to turn the table into. In the example shown below, I have used a ranking plot.
Then, format the chart as you wish using the Object inspector (the panel on the right-hand side). In the example below, I have used Chart > FORMATTING > Show Value: Yes – Below.
Step 6: Creating a visualization
In this next step, we introduce a different way of creating a chart. The core difference is that the chart created in the previous stage was primarily described for printing and viewing in PDFs and PowerPoint, whereas the visualizations are primarily for viewing in dashboards as they have information contained when you hover.
- Click back on the first page in Pages.
- Insert > Visualization > Bar Chart.
- In the Object Inspector, click on the drop-down menu for Table and select Software [table.software].
- Check the Automatic option at the top of the screen (next to Calculate). This means that whenever the data changes, the visualization will automatically update.
- Click on the table to the left of the page and press Appearance > Hide. While it will stay on the screen, it gets grey stripes and will not appear when the document is exported. You may like to move this hidden table elsewhere (including off the page).
Step 7: Creating a custom visualization using R
When you use Insert > Visualization, even though we are using the menus, the actual chart is created using R, which is a statistical programming language. We can also create visualizations by writing the R code directly, which is described in this step.
- Insert > New Page > Title Only
- Give it the title Techniques used in market research
- Drag across Techniques from the Data Sets tree onto the page.
- Click on the table and then the category Decision trees ( e.g., CHAID and CaRT), press Data Manipulation > Rename (it should be in green) and change the name to Trees.
- Rename Structural Equation Modeling as SEM.
- Click on NET and select Data Manipulation > Delete (Rows/Columns).
- Appearance > Hide and drag the table to be below the page. This is so that the viewer will not see the table when they are in viewing the finished product. But we need to keep the table here so that it will update when any filters are applied by the viewer (and hence update the visualization accordingly).
- Insert > R Output, and paste the code below and press Automatic. Resize the R Output until the scrollbars go away. If you find the bubbles themselves do not resize, click on another page and go back to the first (we are working on this bug as I write…). This bubble chart is an open source package, so it is a bit inflexible (these more novel visualizations are always a bit hard to work with, as they have not been tested and refined like those available in the Displayr menus).
library(bubbles) x <- names(x) bubbles(value = x, label = labels, color = "#cee1f2", textColor = "#6e3814", tooltip = labels)
Step 8: Creating filters
- From the Data Sets tree, hold down the Ctrl key on your keyboard and click on Region, Role, How many years have you been working with data / information?, and In terms of qual and quant, would you describe yourself as.
- Insert > Utilities (More) Filtering > Create Filters from Selected Data and press OK. New variables will appear in the Data Sets tree in orange. You can now use these variables as filters (explained in the next section).
Step 9: Exporting the document as a dashboard
- Export > Web Page
- Press Snapshot and View in the popup window that appears, followed by A link that can be used by anyone and then Open in new tab.
Users can then export and filter this dashboard using the options at the top right of the screen.
Try it yourself
You can follow all the steps above using this Displayr document with the data preloaded. Or edit the finished unpublished example of the dashboard built in this post or check out the published online dashboards.
The data used in this post is from Ray Poynter and NewMR’s study on statistical tools used in market research. Thank you for allowing access this dataset.
About Tim Bock
Tim Bock is the founder of Displayr. Tim is a data scientist, who has consulted, published academic papers, and won awards, for problems/techniques as diverse as neural networks, mixture models, data fusion, market segmentation, IPO pricing, small sample research, and data visualization. He has conducted data science projects for numerous companies, including Pfizer, Coca Cola, ACNielsen, KFC, Weight Watchers, Unilever, and Nestle. He is also the founder of Q www.qresearchsoftware.com, a data science product designed for survey research, which is used by all the world’s seven largest market research consultancies. He studied econometrics, maths, and marketing, and has a University Medal and PhD from the University of New South Wales (Australia’s leading research university), where he was an adjunct member of staff for 15 years.