R Visualizations within SAP Analytics Cloud | JET BI

Using R Visualizations within SAP Analytics Cloud

Published: August, 21, 2019

The standard functionality for data visualization within SAP Analytics Cloud (SAC) is extremely wide, but if not enough, it is possible to connect the R language, which allows you to lay out powerful algorithms for data visualization and analyze.

R is a programming language for processing and analyzing data, as well as for building visualizations. Today, R language is one of the most powerful and most multifunctional tools to work with data. It also has the following advantages:

  • Multiplatform - solutions and analysis algorithms built in this language can be easily transferred to any other platform if necessary, they are not tied to the SAC only.

  • The relative ease of use - fast implementation of complex analysis algorithms in comparison to other methods.

  • Flexibility - the capacity to implement analysis algorithms and to build visualizations of any level of complexity.

Benefits of Using R in SAP Analytics Cloud

R visualizations work with data from SAC models, filters that are configured for the whole history work with R visualizations as well, there is also the possibility of adding individual filters to these elements with standard SAC tools. This provides particular flexibility and the ease of use.

The functionality of R visualization in SAC enables you to create both standard graphs and visualizations of more complex data analysis. It is also possible to preview R visualizations and share a story containing R visualizations with other users.

Features of R language when creating visualizations in SAC

Let's take an example. All standard SAC graphs can be built exclusively using R language using R visualizations. This allows you to create graphs that are similar to standard, but they may contain any refinements and additional requirements both for data processing and design. For example, the second graph is a histogram of revenue by category.

It is also possible to use more sophisticated data processing algorithms using R visualizations. The first graph demonstrates the visualization of clustering by the k-means method used by R language. This algorithm is designed to automatically split the data into groups so that the data with similar features appear in the same group.

The third graph shows the visualization of the distribution of data - information that may be required for the future in-depth statistical analysis, identification of statistical patterns and data prediction.

In general, the ability to connect R language to the SAC enables the analysis of data of any complexity.

Using input controls

The SAP Analytics Cloud can filter data by user. For this, input controls are used.

Let's say, in some cases, you need to see complete data, and in others only the data relevant to some selected conditions. The implementation of input controls makes it possible to easily choose own conditions to display data.

When selecting “All” , all the model data is displaying. If you select only one category, only the data for this category is displayed:

The methods for working with Input controls are user-friendly and easy to understand.

Input controls allow choosing one or more attribute values. There are also more complex elements that provide an opportunity to independently select the type of filters (one or more selection values) as well as metrics on which the filters will be based. With filters, the user can limit values ​​for one or more chart elements, as well as for the entire story.

Let's say, in some cases you need to see complete data, and in others only the data relevant to some selected conditions. The implementation of input controls, makes it possible to easily choose own conditions to display data.

When selecting “All” , all the model data is displaying. If you select only one category, only the data for this category is displayed:

The methods for working with Input controls are user-friendly and easy to understand.

Input controls allow to choose one or more attribute values. There are also more complex elements that provide an opportunity  to independently select the type of filters (one or more selection values) as well as metricks on which the filters will be based. With filters the user can limit values ​​for one or more chart elements, as well as for the entire story.

 

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