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Hafez Yari
Hafez Yari

Effective Data Visualization: The Right Chart F...


Data visualizations are a vital component of a data analysis, as they have the capability of summarizing large amounts of data efficiently in a graphical format. There are many chart types available, each with its own strengths and use cases. One of the trickiest parts of the analysis process is choosing the right way to represent your data using one of these visualizations.

Another task that shows up in data exploration is understanding the relationship between data features. The chart types below can be used to plot two or more variables against one another to observe trends and patterns between them.

Sometimes, data includes geographical data like latitude and longitude or regions like country or state. While plotting this data might just be extending an existing visualization onto a map background (e.g. plotting points like in a scatter plot on top of a map), there are other chart types that take the mapping domain into account. Two of these are highlighted below:

By using only one color, you draw attention to the most important information first and quickly present the main takeaway of your visualization. The example on the right gives the color more meaning and purpose, but in the ineffective example the color is purely used as embellishment.

Notice how cluttered and difficult to read the visualization on the left is. The reader is tempted to jump back and forth between both axes and the data to read it, and the bar graph does not quickly convey that the point is to compare sales over time for two different categories. The line graph on the right quickly communicates the purpose of the visualization and makes it simpler to compare sales over time and for each month.

Data Visualization is a way of representing data graphically to help people easily understand the information. It can be used to convey complex relationships between different variables or to analyze trends over time. Data visualization can take the form of charts, graphs, maps, histograms, scatter plots, and other visuals. By using colors, shapes, and other visual elements, data visualization can make it easier for people to comprehend large amounts of data quickly and accurately.

Tables are essentially the source for all the charts. They are best used for comparison, composition, or relationship analysis when there are only a few variables and data points. It would not make much sense to create a chart if the data can be easily interpreted from the table.

Line charts are among the most frequently used chart types. Use lines when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time, when the number of data points is very high (more than 20).

With line charts, the emphasis is on the continuation or the flow of the values (a trend), but there is still some support for single value comparisons, using data markers (only with less than 20 data points.)

Map charts are good for giving your numbers a geographical context to quickly spot best and worst performing areas, trends, and outliers. If you have any kind of location data like coordinates, country names, state names or abbreviations, or addresses, you can plot related data on a map.

The bad side of gauge charts is that they take up a lot of space and typically only show a single point of data. If there are many gauge charts compared against a single performance scale, a column chart with threshold indicators would be a more effective and compact option.

Multi-axes charts might be good for presenting common trends, correlations (or the lack thereof) and the relationships between several data sets. But multi-axes charts are not good for exact comparisons (because of different scales) and you should not use this type if you need to show exact values.

Data visualization is concerned with visually presenting sets of primarily quantitative raw data in a schematic form. The visual formats used in data visualization include tables, charts and graphs (e


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