Building more visualizations in Tableau
1. Building more visualizations in Tableau
In the previous video and exercise, we learned about a few interesting but more complex visualizations for specific use cases. Let’s learn how to build them in Tableau. We will begin with the box plot. This chart is also available via Show Me, and we need zero or more dimensions, at least one measure, and a disaggregated dimension. Let’s control select (command selection on Macs) the Genre, Title, and the IMDb Score. Using Show Me, we will try to create a box plot. Tableau almost immediately has it right! We only need to convert the measure from the Sum to the Average, and we will change the size of the data marks to smaller ones. This chart tells us how the average IMDb rating is spread per title belonging to that genre, so every little dot represents a title (which is present in Detail). How does Tableau know that we want to use the Genre dimension as Category in columns and Title dimension as a disaggregated dimension in Detail? The decision is made based on the number of distinct values. Since there are more titles than Genres, Tableau guessed correctly what our intention is! Well, that was easy! Let’s check out the Waterfall chart next! As we have learned in the previous video, this chart is often used in financial analysis so let’s try to use it with the Gross profit measure. We will visualize the summed Gross Profit by Genre - we already see some positive and negative contributors. To build a waterfall chart, we need a running total, so we’ll apply this table calculation, and we will change the visualization to the Gantt chart to only have the lines at the end of the bar. Next, we need to fill the spots between these lines with the data. The trick is to drop the summed Gross Profit to the Size and add a negative sign to it. Lastly, we will apply the same measure to color to add the 2-scale coloring, to mark positives in blue and negatives in red. We’ll also add the labels. And finally, we will add the Grand total! So, while the overall financials look good, we see that some genres bring nothing but losses while others make almost no profit at all. This is a fascinating insight! As the last chart in this demo, we will make a scatter plot. Scatter plots are excellent in showing the correlation. Let’s test it and check if there is a correlation between the film scores given by the users of “The Internet Movie Database” , IMDb, as opposed to these of users of “The Movie Database”, TMDb. Let’s control select the two measures and a Title as a dimension. Opening Show Me, Tableau already highlights this chart as “Recommended”! With three clicks, we have a chart. Let’s adapt the aggregations to averages. And spend some time beautifying the chart. Some trend is already visible. Let’s change the Marks to Density, make it a bit smaller, and color it red. We’ll also drop a linear trend line. This way, we see a clear correlation but also where the most data points are, somewhere around six and a half. That means both IMDb and TMDb users typically give films a score of six or seven. This is insightful but let’s also make a more simple version of this chart, with fewer data points, based on genres, this time as filled circles. Let’s remove the trend lines now. If we drop Type to the Color, we split each genre into two categories and generate more data marks. Looking at this, it seems most genres have higher scores in the Show category, but there are quite some IMDb vs TMDb outliers. In just a few minutes, we’ve generated together four insightful charts. Now it’s your turn to work on the exercises!2. Let's practice!
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