1. More advanced visualizations
Now that we know how to build basic charts in Tableau, we will progress to more advanced visualizations.
2. Visualizing for various use cases
For each use case, there exists a good chart type, and often more than just one. Throughout this course, we will introduce many of them, and, in this video, we will focus on some interesting charts, which require slightly more work or explanation.
Let's start reviewing them one by one.
3. Box plot - standardized chart for distribution of data
Box plots are a standardized way of displaying the distribution of data based on a five-number summary, plotting a box with lines, so-called whiskers, that present the minimum, maximum, and median as well as the first and the third quartile of the data.
This type of graph is excellent in answering the questions on data distribution, its variance, the symmetry or skewness, as well as discloses the amount and importance of potential outliers.
Box plots aren’t the only way of visualizing a distribution. This can also be done using a simple histogram, as we will see in the exercises.
4. Waterfall (bridge) chart - contribution and change
Next up is a waterfall chart, also known as the bridge chart.
This type of graph explains the net change in value between two points, split over categories.
It typically starts at a baseline of zero; then, there are a series of bars that present category contribution to the total.
The positive values can easily be distinguished from the negative ones by the use of a categorical or graded color palette.
This type of graph exposes the complexity hidden behind an aggregated number.
Looking at the grand total on the slide, we could conclude that the film industry is booming, but we wouldn't know that two out of four reported genres actually bring losses.
As you can imagine, this chart is, therefore, often used in financial reporting.
The large downside is that it works well with only a limited number of categories.
5. Heat map - density and matrix comparisons
Moving over to heat maps. This type of visualization is used for presenting density and comparisons, often in a matrix form, relying on the use of colors to communicate the values.
Heat maps have various forms and types.
The most frequently used are colored geo-maps, for example, to illustrate the density of the population, but they also are often used in web analytics to analyze where on the screen visitors click the most.
Another use case is a matrix comparison, such as visible on the picture on the slide.
The advantage of heat maps is that it gives almost an instant high-level picture through their use of easy-to-understand color gradations.
However, without appropriate labels, it might be challenging to interpret when a high level of detail is required.
6. Scatter plot - relationship and correlation
Scatter plots are charts showing the relationship between two numerical variables plotted simultaneously along both the X and Y axis.
They are often used in exploratory data analysis or when we need to plot the data on a quadrant.
They can be multidimensional, with the use of color, size, and shape, and easily store quite some data, with a high number of data marks.
In some cases, scatter plots are powerful to present a correlation between two measures, such as the example on the slide.
However, in many cases, scatter plots can be tricky to communicate the data insights with, especially when data marks are plentiful and when there is no immediate correlation legible from the chart.
7. Get inspired - Tableau's Viz of The Day
That's a lot of new charts for now. Ready for another Tableau tip? Get inspired with Tableau’s Viz Of The Day public website, where you can download truly stunning dashboards, get behind their workbooks and study how it’s done.
8. Let's practice!
Now, let’s do a small knowledge test.