Advanced visualizations
1. Advanced visualizations
So far, you have seen mostly basic plots to visualize categorical and numerical data. However, sometimes, data is too complex to be represented in those charts. Let's look at more advanced visualizations to address different data types.2. Visualize density
Scatter plots are good for representing relations, but they become hard to interpret when the density of the data is too high. To overcome this, you can use a heat map that shows the trend by highlighting the high-density areas. This example shows that the data is highly concentrated in some areas around the center.3. Visualize geographical trends
Heatmaps are also particularly useful when the values represent geographical points. You can represent the density and pinpoint the areas where the values are higher or lower. In this example, the heatmap shows the areas of the city where the houses are more expensive.4. Visualize trends over time
Data trends can spread over space and time. For example, a dataset containing a department's expenses over the course of a year. If your data contains values collected in successive time intervals, you are dealing with a time series. Usually, those intervals are evenly spaced over time, for example, every minute, hour, day, or month.5. Visualize trends over time
The line plot is one of the most common visualizations representing time series. This plot visualizes the evolution of a variable over time, revealing trends, patterns, or seasonal variations.6. Visualize trends over time
Sometimes, it makes sense to represent how the value increases over time. In that case, you can make your plot cumulative so the value of each time stamp becomes the baseline of the next one.7. Visualize trends over time
Line plots are also useful to plot multiple categories to compare them against each other. In this example plot, you can compare the expenses across different departments over the year.8. Visualize trends over time
A variation of the line plot is the stacked area chart. If the variables represent subgroups of the same value, this chart has the advantage of showing the overall trend. The example shows the overall expense trend and how the different stores have contributed to it.9. Visualize text
Text is a special data type. Its unstructured nature makes it hard to reduce to a meaningful visualization. One of the most common techniques for plotting text is the word cloud. Color and size are used to show the most common words inside a text.10. Numbers and tabular data
After seeing all those cool and complex visualizations out there, it is important to remember that you should rely on data visualization only when it adds value. For simple datasets or data summaries, a table or single numbers is more informative than any plot! Keep simplicity in mind, and don't overdo it.11. Let's practice!
Let's move to some exercises to consolidate the knowledge!Create Your Free Account
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