In my previous post, I went into a quick example of creating a visualization using sample data regarding Volcanic Eruptions. However, normally there are multiple visualizations that can be created using one dataset that can give different insights for those viewing them. Many individuals will want all the information in one place so that they can gain a clearer picture of the data and make better decisions based on that data. This is where dashboards come in. Dashboards are hubs where different visualizations can be placed together.
To gain a better understanding of dashboards, we should create multiple visualizations to use. So, the first step is to find a good sample dataset. As I explained in my previous post, the Tableau Public website has multiple sample data sets that you can use for free to build up your skills.
In this case, I decided to use “Hollywood’s Most Profitable Stories”, located in the Entertainment section. Once you click the dataset link, it will automatically start downloading.
The next step is to connect Tableau Public to the file you just downloaded. Due to the data file being a csv file, which is a type of text file, we click the text file connection.
You navigate to where you saved the file and connect to that file. Tableau will open the data source page, which will have some of the basic information about the dataset. There is also an option to clean the dataset using Tableau programs.
Finally, we can start creating our visualizations by navigating to Sheet 1.
Creating the First Visualization
In this first visualization we want to look at how profitable each genre is. To start off, we will drag and drop the genre column to the center of the sheet to check what types of genres there are in this dataset.
This automatically enters the genre category in the row’s section. Next, we can drag the Profitability measure to the second column of values.
This will show how profitable each genre is for the top Hollywood Stories.
By dragging this numerical data type to the second column, Tableau automatically measures the sum of all the movies’ profitability, grouped by their genres. We can immediately notice that the top movies for profit are Comedies and Dramas.
Now that we have our new table, we can create a visualization for it. It is quite easy to do so: all you need is to click the Show Me tab in the top right corner.
As soon as you click the tab, there is a collection of the different types of visualizations you can use. There will be one recommended by the Tableau program; in this case, Tableau suggests using the horizontal bar graph.
As seen above in the graph, it clearly shows that there is a wide range in profitability for certain genres of stories. However, one part to note is that the measure being used for profitability is automatically set to SUM. The problem that may arise from this is that if there are more stories in a certain genre, the higher the profitability that the genre will show in the graph. One way to deal with this is to change the type of measure being used for profitability. If we were to use the Median measure instead, our visualization changes dramatically. To change this, we need to click the green tab in the column section and in the drop down go to measure. This will open a subset of measures that can be used. We use the Median measure and look at the new visualization.
We can notice that the profitability between genres is much closer than we originally thought. Now, the top genres for average profitability are Romance and Drama.
Creating the Second Visualization
In our second visualization we will check to see how profitable different Studios are at producing within the different genres. To start, we need to create a new sheet. In the bottom tab where we first navigated to Sheet 1, there are three different symbols next to it. From left to right, they are New Worksheet, New Dashboard, and New Story. By clicking that first tool, we create a new worksheet to build our second visualization.
The first variable we want to consider is the Lead Studios for each Hollywood Story.
As seen above, there are a total of 13 Lead Studios, with one being a group called Independent. The next variable to add is the genres column. As in the first visualization, we can drag the column name from the left side to the placeholder column. The results are fascinating.
In this table, we notice that there are multiple blank spots. This means that in this dataset, there were only a few stories in the Action, Animation, and Fantasy genres. On the other hand, almost all Studios had a comedy that made the cut for this dataset.
The next variable to add to the table is the Profitability measure.
Now that we have our table, we follow the same steps as in the previous visualization. Once again, the recommended visualization is the horizontal bar graph.
Tableau helps organize the graph in the most efficient manner. It first groups the movies by Genre, and then it splits the movies by the Lead Studio that produced the movie. As we can see in the above graph, the profitability was automatically measured by summing all the different profitability scores for each group. We should change this measure as demonstrated by the evidence shown in Independent Dramas. We can’t be sure that the high profitability is due to how well the movies did or if there were a large number of unique independent studios making Drama films. Let’s change the measure again to the Median measure.
It looks like we are correct. The profitability of Independent Romance Films is a lot lower than before, which would indicate that the SUM measure gave a false view of how well this genre profits compared to other genres.
Creating the Third Visualization
For our final visualization, we will look at how the average audience and Rotten Tomatoes Scores compare to each other. So, to start we should drag and drop the genres measure onto the sheet. Let’s have the genres be in columns section.
The next step should be to enter the Audience and Rotten Tomatoes Scores as rows. Each individual score is out of 100%.
Once again, we can notice that the scores are not out of 100 as we were expecting because Tableau automatically enters numerical measures with the aggregate function SUM. We take the same actions as before and change the measures value from SUM to Median.
Much better. Next, we choose our visualization type. We click the Show Me tab and due to the type of row data we have, I prefer the Side-By-Side Bar Graph.
This visualization is fascinating and tells us a lot about the movies in this data set. For one, we can see that for the most part, the Audience Score is going be higher than the Rotten Tomatoes Score. A second observation is that while Romance and Drama may be the most profitable genres, the most enjoyed movies were in the Animation and Fantasy genres.
Creating the Dashboard
We now have the three visualizations we want to show. To bring them all together to be viewed at the same time, we create a Dashboard. If you remember, when creating new sheets, there is a tool in the bottom toolbar that allows allows you to do so. The adjacent tool allows the creation of a dashboard.
As with the sheet, we are provided an empty dashboard. Notice how the dashboard is very small. In the column on the left, there is a section for size. We can change this tab to fill out the dashboard. It will depend on the size of your computer screen, but for my computer I chose to use 1500 x 600 pixels.
Just as with creating visualizations using a measure, we can add a visualization to our dashboard by dragging and dropping the sheet corresponding to the visualization into the dashboard. You can choose the positioning of the different visualizations and also increase or decrease the size of each visualization by bringing the cursor to the edge of the visualization itself, which will pop up an arrow. Dragging the arrow will increase or decrease the size of the graph.
Now, we have all the visualizations in one place. You can also note that titles of the graphs are still Sheet 1, 2, and 3. These titles can be renamed by right clicking the sheet in the bottom tab.
We now have a dashboard that brings all the visualizations together in one place. Nice! For your own experience, perhaps you can look at a larger dataset of movies and see how the visualizations differ from this particular dataset.
I hope this post helps expand the possibilities of creating visualizations and how to gather them in a dashboard. Thanks for reading!
For more sample data sets: