Distributed Data Visualization.

Distributed Data Visualization.

Visualizing distributed data in the analytics is the major parameter. There are many ways to visualize the distributed data. They work for same functionality, but they differ with their procedure. A sample data set can figure out the efficiency of each visualization.

  • Plot the Data: Data plotting will showcase the observation density and distribution. This procedure will become complex when the data sets get increased. Data plotting will illustrate clusters and will have a wide range of the data.
  • Barcode Chart : A small line will indicate the distributed data. Including transparency and solid colours will indicate multiple points with same value. This chart will describe the data clearly and can also include multiple data. Barcodes will display the data after the code gets scanned.
  • Strip Plot : Strip plotting looks like the barcode charts. As the data increases, strip plot looks messy with this circulated plot procedure. This procedure is complex and represents the congested data into the specified area when datasets increases.
  • Bee Swarm Plot : This plot will outspread in the complete chart. Unlike strip plotting, this chart will make it easy to read the plotted data. Limited points in the chart will maintain the efficiency, increased points will make the structure complicated.

Bin the Data

Large data sets will confuse the data visualization. Data sets will get binned and the visualization parameters are enhanced. Data will be clearly represented even the large data sets are included into the existing data.

  • Unit Chart : This chart representation looks similar with the Bee swarm chart. Clumsy and complex data will be represented in neat and complete structured format. Data density and transparent representation are improved. Multiple circles are placed in the same region and they can be represented on top of the existing.
  • Histogram : Histogram technique is traditional technique used to represent the statistical values. In chart, histogram contains rectangles and these rectangles represent data. Histogram chart is capable to represent data clearly. Huge data sets won’t matter, bar goes on increasing.
  • Area Chart : Area chart will represent the density and volume of the data in the chart. Data will be clearly visualized with complete transparency in density. In the area chart shapes and patterns are given more significance. Based on the shape and patterns, data volume can be illustrated.
  • Line chart : Line chart is efficient to compare the distributions lies on the same scale. Lines are the common element in both histogram and line chart but have a slight curve intersection with line.

Summarize the Data

These plots make easy representation and concerned about the key elements of the data. Data summarizing will make an easy comparison with multiple distributions. Data representation using different summarized techniques.

  • Box Plot : Box plots are concentrated on the minimum and maximum values from the distributed data. This plotting procedure will make things easier to figure out the distributions and its specifications.
  • Min and Max Plots : Minimum and maximum plots are used to represent the average value from the minimum and maximum values. Audience will be capable to anticipate the other similar plots. This plot will sort out things, considering audience insights.

Conclusion

Data visualizing is an essential step to showcase the distributed data. There are various parameters to consider the efficiency of the chart that represent the data. An effective data representation will a summarized, binned and plotted. Integrating these three will improve the visualization efficiency.

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