In the world of analytics, two of the most popular business intelligence and data visualization tools are Power BI and Tableau. This article won’t compare the pros and cons of using each tool. We will instead focus on how to create different charts from these tools. You think creating and designing is similar for both? Not at all.
In this tutorial, we will use a credit dataset to visualize insights using the same charts for both Power BI and Tableau. But the process of creation won’t be the same.
Let’s start first with the most basic and simplest kind of chart, a table.
Using the Show Me feature, you can quickly set up a table chart as long as you have selected at least one dimension and measure then choose text table from the available options.
Click table chart from the visualization panel then proceed in selecting the fields on the fields pane.
This type is the most common chart and is ever effective in comparing different sub-categories in terms of their respective values.
Measure is required while dimension is optional. But if you want to visualize all subcategories, then you must include at least one dimension. Just select the horizontal bar from Show Me, and click the swap button (highlighted inside the red rectangle).
To create a vertical bar chart, click the stacked column chart on the visualization panel. This is the only way because this option is flexible enough to visualize sub-categories on top of each other.
Pie chart is the most controversial chart as for many this is difficult to interpret due to its shape, especially if the partitions are more than four.
The default pie chart provided by Tableau is very plain. It needs a few tweaks to get the labels for both categories and values to appear. Though, the tooltip will give you a sneak peek on the information of each partition.
The default state of pie chart with Power BI is more advanced. You can instantly see the percentage share of each sub-category as well as their total sum, which is compressed in either hundred, thousand, million, or billion.
A scatter plot is the best chart to visualize specific correlation and patterns across categories.
This one is a little bit complicated. We use two measures (age and credit amount) and select the scatter plot from Show Me feature. The age measure should be converted into an average calculation. To disaggregate for each age, add the age measure into the detail of mark shelf, right click, then change from measure type to dimension type.
This is much simpler to do by selecting the scatter chart from the visualization panel along with the fields with which you want to see the correlation.
This chart is commonly used together with a date dimension. But, this can also be used to visualize continuous and discrete measures.
It is tricky to create a line chart for two measures and at the same time disaggregate by another field. Select the two measures (age and accepted) that you prefer to visualize then choose scatter plot from Show Me. Afterwards, from the marks shelf, change the mark type to line. Right click at the measure placed in the column shelf (age) and convert it to dimension. Finally, drag down the dimension (foreign worker) you want to disaggregate for the line chart into the colour of marks shelf.
You need to move fields a little bit to create a line chart with non-date axis. You have to manually drag the numeric field (age) into the axis shelf and also the categorical field (foreign worker) into legend shelf to separate the values for each category. You don’t have to worry about the data type because Power BI is smart enough to automatically change it based on the shelf it is placed on.
These advanced charts are used to visualize complex dataset and to compress multiple insights in a single chart.
These charts are all handy via the Show Me feature. Just add at least one dimension and one measure for each.
For box-and-whisker plot, you need to add another dimension on the detail of mark shelf.
For a bullet chart, once you click the option in Show Me, it will automatically disable. You can change positions and calculations as you wish for each measure and dimension.
When you select packed bubbles from the Show Me feature, none of the dimensions and measure will be placed under columns and rows shelf. They will be placed accordingly on marks shelf under its size, label, and colour options.
These three advanced charts are not supported by default by Power BI. But, there is an alternative way to still achieve these visualizations.
Search and download the custom visuals from the Microsoft AppSource. Please make sure that you are logged-in using your corporate or school account.
Once downloaded, import the custom visuals via the visualization panel and locate the files. Alternatively, you can go straight ahead importing from marketplace within the Power BI desktop itself.
There’s a difference for each related custom visual that caters to a specific visualization. For example, all custom visuals for bullet charts may have something unique to each other like functionality and how they design to visualize the chart. It’s for you to explore which of the related custom visuals fits your needs visually and technically.
Since there are a lot of custom visuals to choose from in the bubble chart, bullet chart, and box-and-whisker plots, we will cover only one example from each of them. This time, we did not use our dataset because it is not enough to cater the needs for these charts.
Creation of a bullet chart in Power BI needs categories that will fill up the color indicator threshold. Normally the threshold represents poor, satisfactory, and excellent.
https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104380755?tab=Overview
Box-and-whisker plot is a bit complicated with Power BI. Some custom visuals are very sensitive to calculations and this means they are intelligently flexible for more complex analysis.
https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104380831?tab=Overview
Bubble chart is an enhanced scatter plot that allows you to incorporate a secondary variable to visualize a third dimension. In the example below, a field was used to serve as a cluster for all the entities.
https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104381340?tab=Overview