1. Combination charts and custom visuals
In this demo, we will talk a little bit about cognitive load, line charts, line and column charts, and custom visuals.
Our first goal is to look at historical trends in orders processed by the company over the previous months and years. We’ll track that using a line chart, like so. Add Order_ID to the Y-axis field, ensuring the summarization is set to count. Then add Order_Date in the X-axis field. As it is a date hierarchy, we can choose what we want to show on the visual - so let's only leave Year and Month there.
We can see a range of total monthly orders over time. In the four-year range of the data, there have only been two instances where total monthly orders surpassed 220. It is also possible to see that the monthly orders have dropped to some of their lowest levels in the last few months of the period we have data for.
To investigate further and see if this is having an impact elsewhere, let’s add a new line and clustered column chart. The X-axis will be the year and month. This is the shared axis for the lines and columns. The Line y-axis will be Order_ID. You can see at this point that we have the same line chart visual.
Now we add Sales_Amount. This will show us the total sales amount received by the company every month.
We can see that the total sales amount generally tracks the same trend as the total orders. However, there are some periods - such as the start of 2018 - where total monthly orders decrease, but the total sales amount stays relatively constant. Further investigation into this period could help Threads Ltd improve their more recent total sales amount figures.
Let’s replace the line chart with our line and column chart, as it gives us additional valuable context.
Next up, we'll look at some more details regarding the products, and to reduce cognitive load, we’ll do this on another page. On this page, we will add a filter where the year is 2018.
We will now create a radar chart of the product sizes sold by Threads Ltd in 2018 and look at a couple of pricing details measures. Let’s add Product_Size to the Category field and then Product_Price and Product_Cost to the Y-Axis. It makes more sense to see the average of these two measures, so let's change the summarization. Nice!
Now let’s do two more things. First, we want to format this so that the colors are not the same. Now I can easily distinguish average product price versus average product cost.
Let's also resize it so that you can see the information more clearly. Now we can see the five different sizes sold by the company and the average product price and cost for each one in 2018.
Next, we will compare the same measures as before but for the different product colors sold in 2018. This can be easily done with a tornado chart. The tornado chart requires a Group and some Values.
We already have Year as a filter for the entire page, so there is no need to change that. We will then add Product_Color to the Group field. For each group, we will compare the same two measures as before: average product price and cost. Let's format the two columns so that they are both currencies.
Perfect. We can see that orange products had the highest average price in 2018 and the highest average cost. Also, even though pink products had the fifth-highest average price, they had the second-highest average cost. This could be an important insight for the company and could lead to an increase in the price of pink products.
Now it’s your turn to try this out.
2. Let's practice!